The Best CAD Software of 2022 (All Levels)  All3DP
source

October 11, 2022, 12:15 PM   0
Share
“IP teams often embark on the IPMS journey with great optimism. Once in the thick of implementation, however, they may experience a turbulent journey.”
Success is not delivering a feature; success is learning how to solve the customer’s problem.
~ Eric Ries
Imagine that your family has decided to build a new home. You’ve got the vision, but you need to call in the pros—a well-established, highly expert homebuilder with a cadre of architects, designers, contractors, and tradespeople.
You’re relying upon the builder’s expertise to thoughtfully scope the project and prepare you for what lies ahead. This includes (a) helping you understand what financial and other commitments will be required of you; (b) educating you on challenges you’ll face along the way; and (c) highlighting available offerings that align with your vision.
You’re impressed by what the builder’s sales team promises to deliver, so you sign a contract. The price tag is substantial, but you feel you owe it to your family’s future well-being to move forward.
The sales team hands off the project to the builder’s design and construction teams. It’s then that a sobering reality begins to settle in, marked by a litany of unfulfilled promises, delays, surprises, cost overruns, uneven performance, and unresponsiveness.
The only way to salvage the project is for your family to make up the difference, all at incredible sacrifice to daily life. You essentially take a leadership role in the project, do many tasks you thought you’d contracted for, and chip in more money to get the work done. You also give up on the builder fulfilling all contract items.
Now imagine that your company or law firm has decided to implement intellectual property management software (IPMS) with a vendor.
In a worst-case implementation scenario, you may feel like you’re reliving the above homebuilding saga.
Indeed, IP teams often embark on the IPMS journey with great optimism. Once in the thick of implementation, however, they may experience a turbulent journey.
Armed with knowledge of what can go wrong, your enterprise can take proactive steps to drive stronger vendor performance and successfully leverage the power of your chosen IPMS.
Well known to IP professionals, IP management software provides functionality related to IP assets (e.g., patents and trademarks), disputes, operations, and/or tasks. Other descriptors for such software include IP management system, IP asset management system or software, IP portfolio management software or solution, IP lifecycle management software or solution, and IP docketing system.
IPMS vendors, providers, or developers abound in the IP software and services industry.
In its most basic sense, an IPMS is a database of IP records of an enterprise (e.g., a corporation) or multiple enterprises (e.g., multiple clients of a law firm or multiple business units of a group of corporate affiliates). Besides storage of IP asset data, an IPMS may provide docketing functions to enable the tracking of legal and organizational deadlines and related tasks.
IPMS software has come a long way from its docketing-centered roots. Similar to other enterprise teams such as finance, product management, engineering, marketing, and sales, IP teams are increasingly seeking workflow tools that (a) reduce time-consuming administrative tasks; (b) enable more collaboration within the IP team, with other stakeholders in the enterprise, and with external parties; and (c) help their operations to become data-driven.
The IP software and services industry has taken note. Many IPMSs now provide functionality related to portfolio management, invention disclosure submission, workflows to automate or semi-automate actions, document management, patent annuities, trademark renewals, analytics, invoice submission and processing, and the like. Vendors also are starting to introduce new connectivity (e.g., to cloud services) and modern interfaces to support more optimized workflows, digital transformation, and intelligent automation.
Your company’s or law firm’s decision to buy, implement, and use an IPMS may be quite consequential. Implementation and subscription costs may be high. You may need to commit substantial time and other non-monetary resources to support implementation and ongoing productive use.
No two IPMSs and no two enterprises are the same, resulting in significant variability among implementations and implementation projects.
For example, an enterprise using one IPMS may opt to switch to a new, different IPMS. The new IPMS vendor must migrate data from the current system to the new system.
When an enterprise doesn’t have an existing IPMS, datasets must be created from scratch or aggregated from multiple disparate sources. In one such scenario, a corporation’s IP asset data historically has exclusively resided in respective docket systems of its outside patent or trademark counsel. Now, the corporation wishes to implement its own IPMS to provide a full view (e.g., “shadow docket”) of its IP portfolio, or perhaps to begin insourcing IP work.
Another paradigm involves a corporation comprising multiple distinct business units, divisions, or other subgroups that apply different processes, procedures, and ways of viewing their associated IP. The corporation has decided to implement a singular IPMS that permits customization by business units or standardization of operational practices across such units.
An enterprise’s IPMS journey generally fits within four stages:
Some IPMS implementations may be relatively compact and straightforward. An enterprise may have a small portfolio of IP assets; it may have an existing IPMS containing clean data that merely needs to be migrated to another IPMS; or it only requires an entry-level IPMS for basic docketing functions.
Other IPMS implementations may be complex or extremely complex. In particular:
The list of complexities goes on.
What can go wrong during implementation? Potentially many things. Enterprises may experience turbulence such as:
1. Lackluster project leadership and execution
An enterprise may discover that its vendor approaches implementation principally as an exercise to migrate data and provision IPMS features, rather than as a project to deliver solutions closely aligned with the enterprise’s vision and needs.
For instance, the vendor makes no meaningful attempt to ascertain the enterprise’s current organizational, competitive, and IP ecosystem; its imagined future state; or other pertinent fundamentals. The vendor says things to the effect of, “Our software does this,” versus “What are your pain points?”, “What do you want to accomplish?”, and “This is how we can help you get there.” It seems to lack the desire or capacity to truly lead the project; passion for innovation and high service delivery are in short supply.
As a result, the enterprise expends significant energies just trying to be heard by the vendor. It feels that it must take the lead to compensate for the vendor’s lack of direction.
In addition to demonstrating poor leadership, a vendor may struggle mightily with execution of its implementation plan. A project team that acts passively, reactively, or incompetently is undesirable in any IPMS context. However, in particularly complex implementations, the enterprise’s troubles will be substantially compounded by the vendor’s shaky performance.
2. A Pandora’s box of unwelcome surprises
Surprises can arise in every implementation. In an implementation gone south, an enterprise may confront numerous surprises that seemingly could have been avoided but for the vendor’s action or inaction. Examples include:
3. Revisionist storytelling
As implementation problems arise, go-live seems ever distant, and it’s unable to collect subscription fees, a vendor may take strained positions in hopes of bringing money in the door.
For example, contrary to a negotiated contract and the clear understanding of the parties, a vendor suddenly asserts that it’s owed subscription fees despite not having completed the implementation stage and delivered the IPMS for the enterprise’s use. The enterprise is asked to accept the notion that go-live means to provide a test environment or perform implementation tasks.
4. Poor relationship management
The implementation stage may reveal flagrant weaknesses in how the vendor approaches its customer relationships. The vendor may consistently stumble in such foundational areas as managing expectations, fostering healthy communications, and resolving major and minor problems. These deficiencies erode the enterprise’s trust and hamper the parties’ ability to navigate the turbulence of implementation.
5. Massive allocation of enterprise resources
An implementation may require significantly more commitment from the enterprise than the vendor stated would be reasonably required. Internal stakeholders, including IP team or practice group members, must dedicate precious additional time, effort, and money to support the implementation and bring it to fruition.
Many of the above scenarios stem from the vendor. Simply put, it overpromised and undelivered. Others may be unavoidable, a byproduct of complexity or other realities that the parties did or could not anticipate despite their best intentions.
Whatever the cause, a troubled IPMS implementation brings dangers to the enterprise beyond delays and extra costs.
The enormous time devoted to the project takes team members away from other activities and disrupts ongoing work. Added costs and prolonged project completion may undermine the credibility of enterprise leaders who championed adoption of the IPMS. Team morale may suffer.
Moreover, a plagued implementation may sabotage a team’s efforts to deliver visionary, disruptive changes to the enterprise.
To avoid these dangers, companies and law firms should take proactive steps to ensure as successful an IPMS implementation as possible, which we will explore in Part II of this series.
 
Share
Warning & Disclaimer: The pages, articles and comments on IPWatchdog.com do not constitute legal advice, nor do they create any attorney-client relationship. The articles published express the personal opinion and views of the author as of the time of publication and should not be attributed to the author’s employer, clients or the sponsors of IPWatchdog.com. Read more.
No comments yet. Add my comment.
Your email address will not be published. Required fields are marked *
Comment *
Notify me of followup comments via e-mail. You can also subscribe without commenting.

Δdocument.getElementById( “ak_js_1” ).setAttribute( “value”, ( new Date() ).getTime() );
November 19, 2022
November 18, 2022
November 18, 2022
November 17, 2022
November 17, 2022
November 17, 2022
At IPWatchdog.com our focus is on the business, policy and substance of patents and other forms of intellectual property. Today IPWatchdog is recognized as the leading sources for news and information in the patent and innovation industries.
© 1999 – 2022 IPWatchdog, Inc.
Images on IPWatchdog Primarily Provided by
Our website uses cookies to provide you with a better experience. Read our privacy policy for more information.Accept and Close

source

A common complaint among in-house legal teams is getting sales teams to provide the right information during the intake process. When creating new contracts for new or existing customers, there is a direct relationship between sales and legal teams. And it continues from intake through deal management – involving everything from contract creation to review to execution to renewal.
Although sales teams are generally tasked with starting the contract generation process, they aren’t necessarily good at intake. It’s tedious, boring and can get in the way of closing the deal and earning a commission. To further complicate matters for a sales representative, there are often subtleties that have been pre-negotiated that may seem totally innocuous to sales, but are legally significant. These facts may be left out all together on an intake request.
To reduce the work required to create new, accurate contracts, some lawyers create customized templates to capture all the information they need. But those ad-hoc solutions often fail as incorrect data gets inputted, the whole process is ignored, or the template is hastily completed.
One example of a common roadblock can be highlighted by examining the non-disclosure agreement (NDA) drafting and signing process, often required for business to move forward. The first question to address is whether an NDA exists with any given customer. If so, where is it? If none exists, what will it take to get one created and signed? People spend too much time hunting down the NDA with a flurry of back-and-forth e-mails. Wouldn’t it be nice if this standard document was included in the process and easy to access from the beginning?
Intake challenges also include the input of incorrect information – everything from the use of outdated templates to unreviewed legal language or repetitive or obviously unnecessary errors or omissions. These issues simply frustrate the lawyers who are tasked with ensuring each contract brings with it minimal exposure to risk for the business.
In most sales circumstances, both sides will have a champion –a sales rep on one side, a buyer champion (sometimes called Sales Point of Contact, or SPOC), on the other. Both sides will work with their own legal representatives, who will offer comments and red lines. It’s not unusual for this process to happen again and again, wasting everyone’s time and resources. It’s the sales rep’s responsibility to manage the process – even though most don’t enjoy doing this and aren’t particularly good at it. Often, to move the process along, both sides will engage their own legal representatives in a phone or video conversation, an expensive and time-consuming exercise that often dissolves into a barrage of e-mails that can introduce errors into the whole process.
Each one of these cycles can be its own “mini-intake,” and can be subject to all of the above problems and frustrations that come with it.
One solution is to enlist the help of deal desk software – preferably one that is driven by artificial intelligence (AI) so it can continue to learn from all the changes and significantly improve the process. For example, Advocat offers a platform that enables collaborative redlining by centralizing all activity so that it’s easy to access and use.
Using software offers simple solutions to these problems that come up during the intake and deal management process. Here are some things the software can do, leading to significant returns on investment for companies that employ this evolving technology.
How deal desk software can help
Coordination: A virtual deal desk solution allows everyone to see the same information at the same time, avoiding cumbersome and time-consuming communication chains. This allows for a single source on negotiated documents, eliminating the many different versions of Word documents that users attach to emails and circulate around, getting messy and out of sync.
Overcoming issues: Deal desk software provides tools that make it easy for the legal team to show what they have done so it’s easier to approve changes.
Empowerment: A virtual deal desk empowers everyone involved to leverage all features embedded in the software, allowing all employees to use the tools to quickly move deals forward.
Essentially, intake problems are a symptom of bad incentives, confusion, and dis-empowerment. Adopting deal desk software can solve these problems by increasing communication, reducing confusion and overcoming disempowerment. When adopted correctly, deal desks can not only reduce frustration among and between the sales and legal teams, it can save the company time, money, and increase revenues by allowing deals to close quicker.
This is a collaborative effort, requiring support from all stakeholders involved.
Here are some examples:
Highlighting areas of confusion when redlining documents reduces the number of areas that don’t matter and allows resolution without escalation. This also points directly to things that actually DO matter and require escalation. AI can tell the difference, allowing the legal team to focus on what is truly important.
AI can unify the sales playbook with guardrails set up by the legal department. Frequently, everyone has a different view on what the ideal contract result should be. By aligning those views early in the process – and allowing the software to help – internal processes will be improved.
Deal desks allow shared and transparent timelines. The sales team may want to close a deal before the end of the quarter or around a buyer’s budget cycle. But the legal department may have a three-month backlog of work, frustrating the sales team, which may covertly try to avoid the legal process and create contracts on their own. It’s imperative for both sides to understand each other’s timeline to avoid conflict or risky actions such as the execution of non-vetted contracts.
Deal desk software empowers employees on all sides of the deal to quickly and easily solve problems by putting everyone in the same virtual room – both synchronously and asynchronously – to manage their time in an efficient way.
If you’re on the sales side, don’t let intake and deal management challenges get in the way of closing your next deal. If you’re on the legal side, be confident your sales team is working in the most efficient and legally prudent manner. Consider employing a virtual deal desk to improve your processes and bottom line.
See more »
Refine your interests »
Back to Top
Explore 2022 Readers’ Choice Awards
Copyright © var today = new Date(); var yyyy = today.getFullYear();document.write(yyyy + ” “); JD Supra, LLC

source

A Statement of Work outlines the scope of work to be performed and identifies the objectives, tasks, and schedule. Here’s what you need to know.
Statement of Work (SOW) documents are extremely detailed and binding contracts that specify all the details of a project, including hierarchies of reporting, timelines, budgets, deliverables, dependencies, resources, and other terms and conditions agreed upon by all stakeholders. It is a complete project plan that lays down the groundwork for the working process of the project from start to finish.
An SOW document is imperative in order to begin work on any project for effective project management. It is a clearly written project management plan detailing the minutest aspects of the project to bring all the stakeholders on the same page.
Managing a project begins with a well-constructed SOW document. A comprehensive SOW agreement defines each and every aspect of the project in clear terms to all parties involved. It describes the scope of work, including daily tasks, due dates, the governance process, quality assurance, and deliverables, along with the suitable facilities, resources, equipment, training, and budget required to make the project feasible.
A formal SOW document is especially helpful when working with external resources or outsourcing projects to vendors or third parties. It serves as a legally binding contract that comes into play in case of disputes arising due to failure to deliver, financial dues, discrepancies in the end product delivered, missed due dates, etc.
Scope of Work ensures that all stakeholders are on the same page regarding the deliverables of the project. It deals with a brief overview of the project, the list of tasks, a detailed description of services, members, or teams responsible for tasks and services, due dates, expected outcomes, and deliverables.
On the other hand, the Statement of Work encompasses the Scope of Work plus other aspects of project management like budget allocation, financing, resources, equipment provisioning, training, payment processing, performance management, and so on.
An SOW (Statement of Work) document is especially helpful when managing projects in software development as it regulates the service agreements between two or more teams, between developers and vendors, or between IT firms collaborating on building a software product. Although it's not composed as a legal document, it can have legal repercussions when not adhered to.
A clearly written SOW template in software development project management includes explicit descriptions for the following crucial points:
Includes an introduction, a brief overview, and pointers on the reasons and objectives of the project, the processes involved, the end goal, and what it would take to get there.
Where will the vendors, contractors, managers, developers, and other stakeholders work from? Office, remote, or overseas locations?
This section describes the list of tasks, task due dates, responsible teams and members, reporting structure, and task outcomes.
Date of project commencement, task due dates, major milestones, and dates for project conclusion.
This section defines what is to be delivered, when, and how.
This section deals with quality testing, feedback loops, and other standard procedures to maintain the integrity of the deliverables.
A list of all the facilities, equipment, dependencies, technical know-how, tools for project management, and other resources like training, upskilling, etc., required to ensure the successful completion of the project.
This section deals with the budget allocated to the project, payment schedule, services and goods purchase, invoicing, and other financial aspects of the project.
Things not covered in the above eight sections, like travel expenditure, payment for short-term external services, security issues, confidentiality clauses, etc., are usually covered under separate headings dedicated to the topics.
This section deals with what constitutes the successful completion of the project. It mentions the standard of acceptable deliverables within the agreed-upon timeline, with the allocated budget, and every other aspect, so there's no confusion or communication gap between what's expected and what's delivered.
The closing section deals with the project completion procedures and lists all the paperwork, product releases, and other paraphernalia to conclude the partnership.
Here's a quick list of downloadable templates for different types of SOWs.
If you are in the business of developing and managing projects or building software products and services, you'll be in frequent need of clearly composed SOW documents. Although you can easily plan a project using tools like Dropbox Paper, it doesn't help you write a thorough SOW document. Instead of writing one from scratch for each project, you can rely on web-based portals that specialize in generating proposal documents, Scope of Work documents, and SOW agreements.
Better known as digital contracting apps, these portals provide everything from readymade software contracts and legal documents to tools to manage a project, negotiate the terms, customize documents per project requirements, and measure the progress of your software development project. Here's a shortlist of such digital apps to help you with your SOW agreements.
Whether you're a freelance software developer, a member of a team of developers, or a software firm that hires vendors, it's a given that you'll need to write or sign an SOW document sooner or later.
You can use the free downloadable templates listed above, or outsource this process to software services that make the process easier and also provide free Scope of Work templates.
Former corporate communications specialist who's worked with Uber, Google, and TCS, Al Kaatib has ten years of experience as a freelance writer specializing in B2B and B2C content.

source

source

An effective combination of workflow, team collaboration, and document management, Microsoft SharePoint Online is an easy pick for our Editors' Choice designation. But make sure you need all this power because its price can be significant.
Microsoft has had to fight harder in the past few years to maintain its leadership in the productivity space; something that Microsof Office platform used to manage almost effortlessly. To help set it apart, the company has developed several back-office enhancements for Office and made them available in the cloud. Chief among these is Microsoft SharePoint Online, a very powerful combination of customizable workflow, team collaboration, and document management. In combination with Microsoft Office and Office 365, SharePoint Online can quickly implement broad productivity improvements in many organizations. While it carries a potentially high price tag once all users and options are accounted for (though it starts at just $5 per user per month), it’s still an easy pick for our Editors’ Choice in document management, along with Ascensio System OnlyOffice.
Microsoft doesn’t offer a free trial of Microsoft SharePoint Online, though the 30-day trial for Microsoft Office 365 Enterprise E3 does include access to Microsoft SharePoint Online (make sure you sign up for the Enterprise E3 trial as Microsoft also offers trial versions of Office 365 Home Premium and Business Premium but neither of these tiers includes Microsoft SharePoint Online). Microsoft SharePoint Online has two paid plans, named simply “Plan 1” and “Plan 2.” Plan 1 (which begins at $5 per user per month) includes really everything you would expect from a document management platform: support for multiple document libraries, collaboration tools, sharing with internal or external users, content management, records management, and workflows. Plan 2 (which begins at $10 per user per month) adds a number of advanced features, including customizable search capabilities, e-discovery, and compliance tools such as auditing and in-place hold.
You can also gain access to Microsoft SharePoint Online through any of the Enterprise tiers in Microsoft Office 365. The Office 365 Enterprise E1 tier provides Microsoft SharePoint Online access equivalent to Plan 1 as well as access to Exchange Online with 50GB mailboxes, Microsoft Teams ($5.00 Per User Per Month, Billed Annually at Microsoft 365 for Business)(Opens in a new window) , and online versions of Microsoft Excel, Microsoft Outlook, Microsoft PowerPoint, and Microsoft Word.
Office 365 Enterprise E3 adds desktop versions of Office 2016 (up to five installations per user) as well as mobile apps, unlimited Microsoft OneDrive ($5.00 Per User Per Month at Microsoft 365 for Business)(Opens in a new window) storage, and the addition of document management capabilities. Such capabilities include manual retention and deletion policies, manual document classifications, and eDiscovery (a suite of tools that lets you find documents related to litigation or information requests, and take appropriate action such as placing a hold on the documents or exporting the files). Office 365 Enterprise E5 adds to the eDiscovery toolset with analytics, automated document classification, and import through Advanced Data Governance. Pricing for Office 365 Enterprise tiers is calculated as a monthly rate per user with an annual commitment. Pricing begins at $8 per user per month for E1, $20 per user per month for E3, and $35 per user per month for E5.


It’s simple to sign up with Microsoft SharePoint Online. Just pick a plan and provide your name, email, phone number, company, and address. Then, create a user ID, password, and a unique URL (e.g., yourname.onmicrosoft.com). After that, verify that you’re not a robot by inputting a code that will be sent to you via a text or phone call. Finally, input your payment information and choose whether to pay monthly or annually. Payment information is not required if you opt for the free trial.
If it’s your first experience with Office 365, then you may encounter a bit of information overload when you first log in. If you have signed up for the free trial of Microsoft Office 365 Enterprise E3, then your dashboard will include over a dozen tiles linked to various Microsoft programs and features included in the Enterprise E3 tier. One of these tiles is Microsoft SharePoint Online. In addition, you can access the administrative aspects of Microsoft SharePoint Online under the Admin module by using the Admin Centers menu on the left-hand side.

Microsoft SharePoint Online lets users create individual sites as an organization and management entity. Each SharePoint site can have its own document library, notebooks, security, and design. In addition, sites can be organized as standalone sites or in a hierarchy through the use of subsites. When creating a site, you’re prompted to choose between a Team site or a Communication site. Team sites are the traditional SharePoint sites, intended for collaboration within an organization, particularly where a significant percentage of the users will be involved in document management. Communication sites are useful for scenarios in which an individual or small group is creating content to be consumed by a larger group of users; think of it as a cross between a document library and a blog. Both site options can be used to share documents and are highly customizable.
Team sites in Microsoft SharePoint Online are flexible, provided someone is willing to navigate the learning curve. A site’s document library, for example, can be configured with numerous metadata fields, which can be leveraged for a number of different functions. A document view can be customized to show specific fields for you to use to search and sort. A view can also be filtered to only display documents with matching metadata, which would let you create multiple views, each customized to show a specific set of documents.

Microsoft SharePoint Online document libraries can be synchronized with your computer or mobile device by using the Microsoft OneDrive client. Desktop users can simply click the Sync button within the document library to configure the connection (or download the software). New files can be added to the document library through drag-and-drop, copying files into a synced folder on your desktop, or by using various Office apps on desktop or mobile devices. One knock against Microsoft SharePoint Online for many years was due to the unpredictability of the OneDrive sync client. Microsoft has since released a new OneDrive client, which seems to do a much better job of keeping your files in sync.

At its core, Microsoft SharePoint Online is a document management platform. In addition to basic document management capabilities, such as file uploads and downloads, editing, and sharing, Microsoft SharePoint Online handles change tracking, too. Keeping a log of who made edits and letting you download previous editions of your document to review changes or return to a previous revision lends quite a bit of flexibility to Microsoft SharePoint Online. Users can also configure alerts on a document library to receive a notification when changes are made. Documents can be checked out to prevent issues with multiple people editing the same file, though co-authoring (multiple users editing the same file simultaneously) is also supported for modern Office docs, assuming all users are using Office 2010 or later.
Microsoft offers some fairly hefty security and compliance features in Office 365, many of which can be leveraged against Microsoft SharePoint Online. Most other business-grade document management suites, such as Ascensio System OnlyOffice (40.00 Per Year at ONLYOFFICE)(Opens in a new window) , offer basic security features such as permissions and audit tracking. Microsoft SharePoint Online supports these features as well but also includes features an order of magnitude above the competition. For starters, you can create policies that handle things such as document labels, data loss prevention, document retention, and supervisor access. Document labels are used to identify documents that may have sensitive data, such as credit cards, social security numbers, or customer data.

SharePoint offers tools to not only create and manage these labels, but to apply them to existing documents (or even emails if you’re using a full Office 365 suite) based on the contents of the document. Similar tools are available to prevent data loss, such as preventing documents that contain sensitive information from being shared outside your organization. Tools are also available for managing document retention, both for preventing users from permanently deleting documents and for enforcing archival or removal after a defined period of time. In many of these cases, Microsoft offers a predefined set of filters designed to facilitate compliance with regulations in various countries, including the Health Insurance Portability and Accountability Act(Opens in a new window) (HIPAA) and corresponding regulations. You can also configure policy application by using search terms. These policies can be applied across Microsoft SharePoint Online or to individual sites.
Audit logging can be used to track user-level activities or administrative changes. Once logging is enabled, you can search the audit log for specific actions, users, time windows, or search terms in order to focus your view. One feature that was mentioned previously, eDiscovery, is a comprehensive tool for locating documents related to a specific topic such as a legal action. By using eDiscovery, you can locate documents that are within the scope of a subpoena or other information request, protect relevant documents by placing a hold on them, or even export relevant files in order to comply with the request.
If these security features sound intimidating, then know that they are completely optional and won’t get in your way if you choose to ignore them. More standard security options, such as group- or user-based permissions, are also available. You also can configure how file sharing outside your organization works, such as sharing only to authenticated users, setting expiration periods for anonymous access, or using groups to manage who can share documents externally. You can also configure share links to provide read-only access to your documents.

Microsoft SharePoint has supported workflow creation for some time now and SharePoint Online is no exception. While most document management systems with a workflow module support basic document management tasks, such as review routing or signature requests, Microsoft SharePoint Online tightly integrates with Microsoft Flow, a tool for creating workflows between disparate platforms. By using Flow, you can send approval requests when documents are loaded to a library, integrate with Microsoft Forms to route information to the appropriate contact, or link a document library with a third-party cloud storage platform such as DropBox Business or Google Drive for Work. Microsoft offers over a hundred Flow templates, or you can roll your own.
Another area Microsoft is pushing as an integration point into their holistic information management platform is Microsoft PowerApps (7.00 Per User Per Month at PowerApps)(Opens in a new window) . The intent behind PowerApps is that users can very easily (in relative terms) create an app that can capture and store data in Microsoft SharePoint (even using Flow). These PowerApps can be listed in the Microsoft Store and used for either internal business processes or as customer-facing portals.
Speaking of apps, Microsoft’s long-standing relationship with developers means that the Microsoft SharePoint app catalog is chock-full of apps you can integrate into your Microsoft SharePoint sites. These apps come in both paid and free options but you should be aware that even the free apps come with a price. Microsoft SharePoint app permissions are much like you’d expect on your mobile device, and each app includes a list of things that the app will be able to do with your documents and data, so they should be handled with the appropriate care. Under the right circumstances, Microsoft SharePoint apps can add functionality such as enhanced notification capabilities, additional calendar options, integration into other services, and a host of other options.
Microsoft SharePoint’s reputation for being difficult to manage or even use is the result of its focus on features over usability. With Microsoft’s increased focus on making Office 365 and its various components accessible to part-time admins (particularly those thrust into the role out of necessity), Microsoft SharePoint Online is a revelation in terms of offering advanced features that appeal to enterprise customers without delivering a solution so complex it can’t be used by smaller IT shops. To be fair, there are still some intricacies of managing Microsoft SharePoint, including multiple locations for administrative settings. But most of the features are much more straightforward to configure than they’ve been in the past.
Microsoft’s Office 365 pricing is aggressive and Microsoft SharePoint Online can be had for a very reasonable $5 per user per month. But that’s still a far cry from the $1-per-user-per-month pricing you can get from competitor Ascensio System OnlyOffice, our other Editors’ Choice. Still, based on its new ease-of-use focus, the advanced collaboration and security features offered with Flow, eDiscovery, and the wide variety of configurable management policies, Microsoft SharePoint Online easily earns one of our Editors’ Choice awards in the document management category.
An effective combination of workflow, team collaboration, and document management, Microsoft SharePoint Online is an easy pick for our Editors' Choice designation. But make sure you need all this power because its price can be significant.
Sign up for Lab Report to get the latest reviews and top product advice delivered right to your inbox.

This newsletter may contain advertising, deals, or affiliate links. Subscribing to a newsletter indicates your consent to our Terms of Use and Privacy Policy. You may unsubscribe from the newsletters at any time.
Your subscription has been confirmed. Keep an eye on your inbox!
Advertisement
Tim Ferrill is an IT professional and writer living in Southern California. Follow him on Twitter @tferrill.
Read Tim’s full bio
Advertisement
PCMag.com is a leading authority on technology, delivering lab-based, independent reviews of the latest products and services. Our expert industry analysis and practical solutions help you make better buying decisions and get more from technology.
© 1996-2022 Ziff Davis. PCMag Digital Group
PCMag, PCMag.com and PC Magazine are among the federally registered trademarks of Ziff Davis and may not be used by third parties without explicit permission. The display of third-party trademarks and trade names on this site does not necessarily indicate any affiliation or the endorsement of PCMag. If you click an affiliate link and buy a product or service, we may be paid a fee by that merchant.

source

Content, analytics, functional, and utility APIs
FactSet and third-party concorded datasets
Integrated software solutions across front, middle and back office
Ebook: Solving the ESG Data Challenge
Join a team of highly motivated, talented individuals who are empowered to find answers through creative technology.
By FactSet Insight  |  November 10, 2022
Environmental, Social, and Governance (ESG) has been a growing focus for Asset Managers over the past few years going quickly from Alternative data to Essential in the overall investment process. The continued appetite from investors and a changing regulatory landscape have all contributed to estimates that ESG will surpass $41 trillion in 2022 and $50 trillion by 2025.
(more…)

Software for Small Contractors  For Construction Pros
source

The Board Management Software Market Research | Sponsored | state-journal.com  State-Journal.com
source

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.
Advertisement
Carousel with three slides shown at a time. Use the Previous and Next buttons to navigate three slides at a time, or the slide dot buttons at the end to jump three slides at a time.
02 November 2022
Wolfgang Lugmayr, Vadim Kotov, … Thomas C. Marlovits
14 June 2019
Jakob Runge, Sebastian Bathiany, … Jakob Zscheischler
24 October 2022
V. E. Ambruş, L. Bazzanini, … R. Tripiccione
14 November 2022
Robert Crystal-Ornelas, Charuleka Varadharajan, … Deborah A. Agarwal
14 October 2022
Trevor Barnes, Abhishek Shivakumar, … Taco Niet
29 October 2021
Gilles Maussion, Rhalena A. Thomas, … Thomas M. Durcan
11 August 2021
Maria Jardim Beira & Pedro José Sebastião
01 October 2021
Enrique Blanco, Mar González-Ramírez & Luciano Di Croce
09 August 2021
Marcin M. Jacak, Piotr Jóźwiak, … Janusz E. Jacak
Scientific Data volume 9, Article number: 700 (2022) Cite this article
636 Accesses
12 Altmetric
Metrics details
Research can be more transparent and collaborative by using Findable, Accessible, Interoperable, and Reusable (FAIR) principles to publish Earth and environmental science data. Reporting formats—instructions, templates, and tools for consistently formatting data within a discipline—can help make data more accessible and reusable. However, the immense diversity of data types across Earth science disciplines makes development and adoption challenging. Here, we describe 11 community reporting formats for a diverse set of Earth science (meta)data including cross-domain metadata (dataset metadata, location metadata, sample metadata), file-formatting guidelines (file-level metadata, CSV files, terrestrial model data archiving), and domain-specific reporting formats for some biological, geochemical, and hydrological data (amplicon abundance tables, leaf-level gas exchange, soil respiration, water and sediment chemistry, sensor-based hydrologic measurements). More broadly, we provide guidelines that communities can use to create new (meta)data formats that integrate with their scientific workflows. Such reporting formats have the potential to accelerate scientific discovery and predictions by making it easier for data contributors to provide (meta)data that are more interoperable and reusable.
Making Earth and environmental science data Findable, Accessible, Interoperable, and Reusable (FAIR)^1,2 contributes to research that is more transparent and reproducible³. Search engines and data repositories^2,4,5 have enabled advances in data preservation, findability, and accessibility. However, data interoperability and reuse remain major challenges in part due to the diversity of Earth science data, and because researchers may lack time and funding for data management or awareness of tools and resources to make data more reusable^5,6. This results in barriers to scientific research and knowledge generation; for example, synthesis of data across different sources can be extremely time-consuming when data and metadata are not standardized in a common, well-defined format.
Standards for data and metadata, hereafter referred to as (meta)data standards, have been proposed as important elements to make Earth and environmental science data easier to find, understand and reuse^7,8,9,10. Formal (meta)data standards are typically accredited by large governing bodies and emphasize making data broadly reusable¹¹. For example, the International Organization for Standardization (ISO) 8601 standard provides guidelines for formatting date and timestamps and has been adopted in a wide range of research and business sectors¹². The Open Geospatial Consortium’s Sensor Observation Service standard¹³ outlines standardized ways of pulling sensor data from web interfaces. Such accredited standards are extraordinarily useful, but are available only for a few environmental data types and can take over a decade to build governing processes and consensus.
In contrast, reporting formats are community efforts aimed at harmonizing diverse environmental data types without the oversight of the governing protocols or working groups that maintain vocabularies and extensive documentation. There are reporting formats for different research domains and data types including water quality¹⁴ and meteorological data¹⁵. Reporting formats are typically more focused within scientific domains—for example, marine observations¹⁶ or solid earth geoscience¹⁷. Reporting formats can enable efficient collection and harmonization of information needed to understand and reuse specific types of data within a research community. For example, the use of FLUXNET’s half-hourly flux and meteorological reporting format¹⁸ enables both access and reuse of consistently formatted carbon, water, and energy flux data from thousands of sampling locations across the world. However, reporting formats do not exist for most environmental data types, and even if they do, complexity and lack of resources can limit their adoption⁹.
There are many scientific benefits when research communities adopt reporting formats, ranging from organizing data collection in the field or lab to more efficient data reuse in synthesis and modeling efforts. Reporting formats can facilitate data sharing within a group, provide guidelines for consistent data collection, enable streamlined scientific workflows, and enable long-term preservation of knowledge that may not be typically stored or reported with the data^19,20. Moreover, research disciplines are beginning to operationalize and implement practices^21,22 to achieve the original FAIR guiding principles^21,22. Reporting formats developed by the research communities for which they are intended are seen as a critical step toward achieving greater data interoperability and reuse²².
A variety of multidisciplinary data are generated in research sponsored by the U.S. Department of Energy (DOE) and stored in the Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) data repository^4,23. Integration and analysis of diverse data types such as hydrological, geological, ecological, biological, and climatological data is an essential element of complex environmental systems science (ESS) research. However, such interdisciplinary data integration presents unique challenges, such as inconsistent use of terms, formats, and metadata across disciplines²⁴. In this manuscript, we describe and harmonize 11 diverse and complementary (meta)data reporting formats that our interdisciplinary team developed for commonly used data types in ESS research to enable their archival following FAIR principles in general purpose repositories such as ESS-DIVE. These include guidelines to format and describe general research elements (e.g., general file metadata, tabular data, physical samples, model data), as well as guidelines developed for more specific data types relevant to interdisciplinary research (e.g., biogeochemical samples, soil respiration, leaf-level gas exchange). As part of this process, we adopted or used components of existing reporting formats or standards to the greatest extent possible, and also developed new reporting formats for some data types. These can be used individually or collectively in scientific workflows, and many of the formats are widely applicable for environmental research. Moreover, the process we used for developing the formats—including our approach to obtain community consensus, mirror documentation across several web platforms, and track community feedback—can be used by other research communities to develop reporting formats for their own purposes.
Our community-centric approach to developing reporting formats had four key outcomes that are broadly important to making scientific data more reusable. First, the teams reviewed a total of 112 pre-existing data standards and other data resources (data repositories, data systems, datasets, projects) to create (meta)data crosswalks (Supplementary Files 1–20). Such crosswalks provide a tabular map of existing resources related to each data type, allowing the teams to identify gaps in existing standards, and determine which variables, terms, and metadata were essential to harmonize and incorporate into their reporting formats. At the onset of the review process, ESS-DIVE recommended adopting existing standards to the extent possible. However, we found that for all 11 data types, none entirely met ESS research community needs, and this necessitated development of all 11 reporting formats.
Second, we created 11 reporting formats (Supplementary Table 1) that encompass a range of complex and diverse ESS (meta)data fields that can be used when researchers upload data to ESS-DIVE. Six of the reporting formats created by our community of scientists are cross-domain reporting formats (Fig. 1a), which apply broadly to data across different scientific disciplines. These reporting formats were developed to help researchers more consistently format their (meta)data for interdisciplinary science applications and include basic dataset metadata for citation and findability²⁵, file-level metadata²⁶, guidelines for formatting comma separated value (CSV) files²⁷, sample metadata²⁸, terrestrial model data archiving guidelines²⁹, and research locations metadata³⁰. The remaining five reporting formats apply to different domain data types (Fig. 1b) and include microbial amplicon abundance tables³¹, leaf-level gas exchange³², soil respiration³³, sample-based water and soil chemistry measurements³⁴, and water level and sonde-based hydrologic measurements³⁵. All reporting formats have a minimal set of required metadata fields necessary for programmatic data parsing and optional fields that provide detailed spatial/temporal context about the sample useful to downstream scientific analyses. Throughout development, we aimed to strike a balance between pragmatism for the scientists reporting data and machine-actionability that is emblematic of FAIR data. A comparison between FAIR guiding principles and our reporting formats (Supplementary Table 2) highlights how a community-centric effort like ours can move data archiving towards achieving many FAIR data principles (though see discussion for limitations).
Workflow to help determine which (meta)data reporting formats apply to datasets. The set of 11 ESS-DIVE (meta)data formats are either (a) cross-domain guidelines that can be applied to many data types or (b) are data type-specific. For those archiving data with ESS-DIVE, researchers can upload data through the ESS-DIVE web user interface¹⁵⁵ or programmatically through an API.
Together, these 11 reporting formats are part of a flexible, modular, and integrated framework (Fig. 1) that can accommodate new reporting formats in the future, and enable their findability and accessibility individually or collectively. As part of the framework development, all teams created templates with harmonized terms and formats to be internally consistent as much as possible. For example, dates are always reported in YYYY-MM-DD format. Whenever reporting formats include spatial data, the variables are harmonized as “latitude” and “longitude” and reported in decimal degrees with common bounds (−90 to 90 and −180 to 180, respectively). All formats that require CSV files adopted as many recommendations from the CSV reporting format as possible. Data collected using the water and soil chemistry, and amplicon reporting formats have an option to report a persistent identifier for associated samples [International Generic Sample Number (IGSN)], to enable effective tracking across online data systems, as outlined in the Sample ID reporting format.
The third outcome is related to how we shared and archived all reporting formats in three ways, each with a distinct use. First, all reporting formats are published as datasets in the ESS-DIVE repository, which enables direct, public download and citation upon use. Second, each reporting format is hosted on the version control platform GitHub, which enables ongoing edits and versioning while also allowing users to provide feedback³⁶. Third, the most up-to-date reporting format content from GitHub is rendered as a project website through the service GitBook³⁷. We mirrored the reporting format instructions and templates across several web platforms to ensure the documentation is available in a variety of digital formats to serve the needs of various user groups and stakeholders. GitHub is likely a more familiar platform and user interface for software engineers and informatics specialists, for example, while GitBook websites may be preferred by Earth science researchers.
Lastly, we formulated guidelines (Box 1) for research communities that want to replicate our model of community-centric (meta)data reporting format development. We encourage (1) reviewing existing standards, (2) developing a crosswalk of terms across relevant standards or ontologies of interest, (3) iteratively developing templates and documentation with feedback from prospective users, (4) assembling a minimum set of (meta)data required for reuse, and (5) hosting finalized documentation on platforms that can be publicly accessed and updated easily.
1. Research existing (meta)data standards and other data resources across agencies and organizations both within the US and internationally.
2. Create a (meta)data crosswalk (Supplementary Files 1–10) to define how other standards and data resources translate to the proposed reporting format.
3. Work with the scientific community to iteratively develop and obtain feedback (see Fig. 2) on (meta)data reporting format.
4. Develop documentation (instructions, templates, variables, descriptions, units, metadata) to support the format. Consider appropriate file formats for any templates.
5. Archive finalized version of the reporting format in a long-term data repository as well as a version control platform (e.g., GitHub³⁷).
Many scientific journals and funders require data deposition in long-term repositories. However, in many cases, data are submitted to repositories in bespoke formats with little (meta)data standardization⁵. Community-led (meta)data reporting formats like the set described in this paper can enable archived data to be more reusable and interoperable^21,22. Our scientist-centric approach to creating the formats helped to determine workflows that are most useful and practical for researchers to adopt. Here we discuss important aspects that need to be considered in development and use of such reporting formats.
Reporting formats can help researchers organize and synthesize their own (meta)data for their research purposes. It can be challenging for small teams, or even individuals to keep track of data collected over multi-year field campaigns or laboratory experiments^19,20. Early adoption of a consistent way of compiling data can help individuals or research teams avoid ad hoc data collection practices and also help researchers efficiently integrate their data, particularly when multiple analyses or teams are involved.
Moreover, community reporting formats can lead to greater data accessibility and reuse. For example, researchers in the Ameriflux network³⁸ organize flux data in the Flux Processing (FP-in) reporting format¹⁸. When participants in the network agree to provide their flux data in this format³⁹, benefits include: 1) access to data services such as automated QA/QC of datasets and value-added ONEFlux data processing⁴⁰, 2) Digital Object Identifier assignment which helps to track dataset citation and reuse, and 3) potential to increase findability of their data. Similarly, when contributors upload datasets on ESS-DIVE, they are offered automated metadata quality assessments, and published data are assigned DOIs and made searchable across the DataONE network. In another example, the Watershed Function Scientific Focus Area project⁴¹ adopted ESS-DIVE’s water and soil chemistry reporting format as an initial step towards establishing a field data workflow in a community observatory where diverse hydrological, geochemical, geophysical, ecological, and remote sensing datasets are collected⁴². The use of the reporting format will make it possible for researchers to synthesize data on chemical concentrations both within and across field locations.
Application of the reporting formats also allows for the use of tools and services that enhance data curation, findability and reuse. As an example, some of the fields in ESS-DIVE’s dataset metadata reporting format²⁵ allow programmatic metadata quality validation, which checks for field presence, format, and length. Because these metadata can be mapped to a variety of machine-readable metadata formats including JSON-LD and the U.S. Department of Energy’s Office of Scientific and Technical Information (OSTI) reporting formats⁴³. This further enabled transforming and disseminating ESS-DIVE datasets across other platforms such as Google Dataset Search, DataONE, OSTI and DataCite.
The development of these reporting formats was driven by the scientific need for practical tools for data management, while improving the potential for data reuse achieving many of the FAIR guiding principles (Supplementary Table 2). We made several pragmatic choices to ensure that the reporting formats would have a low barrier to adoption by time-limited researchers. This included investigating whether using pre-existing reporting formats “off the shelf” would meet project and researcher’s scientific needs and workflows. Although it is desirable to use existing formats whenever possible, we found that there were many circumstances when they do not directly apply to a scientific community’s research (meta)data needs. For example, although the Water Quality Exchange format¹⁴ is used within the United States to report water quality monitoring data by local, state, and federal agencies, the format was not entirely suitable for ESS-DIVE’s purposes. Some of the concerns raised by the community included: 1) the structure of the data and metadata templates that are used for regulatory reporting were considered to be cumbersome and inefficient for scientific use (e.g., containing redundant elements of sampling and analytical methodology along with the data) and 2) the required vocabularies (as specified in the template dictionary) were found to be difficult to use because they included several terms that were unnecessary, while missing terms for specific analytes of interest to the community.
To address these concerns, we developed the ESS-DIVE reporting format for sample-based water and soil chemistry³⁴ that is more suitable for files typically generated in scientific laboratories. It borrows elements from the WQX standard, but provides flexibility in format and terminology, while capturing sufficient metadata and vocabularies to enable data exploration and reuse including the ability to use scripts to compare and combine different datasets³⁴. In this way, the water and soil chemistry reporting format achieves some component of FAIR guiding principle “I2” that suggests using ontologies, while still being responsive to a research community that desired flexibility in research terminology (Supplementary Table 2).
Similarly, when creating the sample ID metadata reporting format, we decided to extend the existing IGSN sample identifier template and guidelines in ESS-DIVE’s Sample ID reporting format to meet researchers’ need to link interdisciplinary environmental and biological samples, and to minimize effort in providing information for sample collections⁴⁴. In this case, incorporating IGSNs ensures that researchers using this format achieve FAIR principle “F3” and have globally unique identifiers for their data products, which facilitates tracking associated sample data across multiple online data systems. In an effort to be pragmatic, we decided to lower the threshold for adoption of the sample ID reporting format (and nearly all others; Supplementary Table 2) by compromising on elements that would achieve FAIR principle “I3” related to machine readable knowledge representation. All reporting formats encourage users to define variables in a data dictionary. Though this may not be fully machine readable according to the FAIR principles²¹, this method of defining variables is a key step toward reusable and machine actionable data. The feedback gathered when creating our Sample ID reporting format was then provided to the broader IGSN community to help improve the IGSN metadata template for interdisciplinary science^45,46.
Through the process, we learned that many (meta)data standards are not accessible to a typical researcher and require a significant learning curve to become fluent in the informatics terminology used by established data standards. For example, the Open Geospatial Consortium’s data standard for environmental sensors¹³ is a detailed schema described over 100 pages, which is challenging for a typical scientific researcher to understand and implement. Hence, we had to make several pragmatic choices to ensure that the reporting formats would be amenable to adoption by time-limited researchers. Once choice involved replacing terms in existing standards with words that were more intuitive to scientists. For example, whilst there was no reporting format for leaf-level gas exchange data, a crosswalk of the instrument output from a relatively small number of instrument manufacturers quickly identified a common terminology that already had broad acceptance and use by the scientific community (Supplementary File 7). By using crosswalks (Supplementary Files 1–10) our teams were able to map ESS-DIVE’s reporting formats to many existing (meta)data standards and other data resources, and, in the future, will allow building tools that enable interoperability with different systems. We also simplified the reporting format templates and instructions to the greatest extent possible by specifying a few required fields and several more optional fields to provide additional details.
Our model and guidelines of supporting and empowering the scientific community to develop (meta)data reporting formats that meet their needs can enable other communities to undertake these internal data standardization efforts that make their data even more useful beyond the purpose for which they were collected (Box 1). We acknowledge that other research infrastructures have made important strides toward data standardization within research communities though they can still take dozens of years to manifest¹⁷. We found value in including a broad range of stakeholders in the process, and included field personnel who make the measurements, instrument manufacturers, and scientists who use the data in models or synthesis activities⁴⁷.
There are incentives that can help promote widespread adoption of these or other formats to justify the time investment required for individual researchers or teams into scientific workflows. First, involving data collectors and reusers at the core of the development process makes the resulting formats more pragmatic and scientifically useful. Importantly, the domain scientists involved in the reporting format development became community ambassadors and helped engage their use by fellow researchers through conference presentations and peer-reviewed papers^44,47,48,49. Second, we expanded our user community by sharing information about the reporting formats through a series of webinars, documentation, tutorials, and personalized community outreach. These incentives have had some success, as evidenced by the datasets submitted to ESS-DIVE using one or more of the reporting formats within a few months after they were finalized (Table 1).
We identify some future work that can potentially lower the barrier to adopting reporting formats, provide added benefits to those who use the formats, and make (meta)data FAIRer¹⁰. Currently, ESS-DIVE applies a set of manual checks to datasets uploaded to ESS-DIVE that follow the reporting format. However, development of automated formatting checkers⁵⁰ would help users instantly validate their datasets against reporting format guidelines. Other types of software can also be built around the reporting formats. For example, software could be developed to automatically convert sensor or instrument-derived data into the units requested by a reporting format. As a starting point for this work, the file-level metadata reporting format already includes an open-source script⁵¹ that enables reading and parsing data files submitted in that format. The leaf-level gas exchange reporting format includes a detailed translation table matching the reporting format data variables with standard outputs from 10 commonly used, commercially produced instruments. This could provide the foundation for development of conversion software to automatically format data with the recommended variable names and units. ESS-DIVE is also planning a data integration and fusion component of the repository that will facilitate synthesizing and analyzing datasets that adhere to any of the 11 ESS-DIVE reporting formats. Enabling advanced queries within the files will require development of software and data parsers so that a great number of reporting formats achieve FAIR principle “F4” which calls for data to be fully searchable.
With more data being generated than ever, reusable data can have substantial societal, economic, and scientific impacts. But for Earth and environmental science data, which are complex and heterogeneous, achieving reusability will require concentrated effort at (meta)data standardization within research communities. Our work to develop 11 community (meta)data reporting formats is a critical step to making Earth and environmental science data more reusable because we emphasize human readability that is compatible with machine readability. We hope that our model of empowering research communities to self-organize and create their own (meta)data reporting formats will enable other communities to undertake these internal data standardization efforts that make their data even more useful beyond the purpose for which they were collected.
Earth and environmental science data are complex, multi-scale, and span diverse research domains such as geology, hydrology, climate, ecology, and biology. At ESS-DIVE, we initiated a community-centric model that engaged domain scientists to develop formats for common Earth science data types. The objective was to create formatting guidelines and templates that would gather the minimum but sufficient metadata or data necessary for data interpretation and reuse.
Each team conducted a review of existing standards (Supplementary Table 1), involving both literature searches and exploring resources from informatics groups (e.g., Research Data Alliance and Earth Science Information Partners) or agencies working with similar data, to identify whether any existing data standards or conventions could be used directly or to inform their reporting format. Based on this review, each team created tabular ‘crosswalks’ (Supplementary Files 1–10) to map related terminology from relevant standards. This process helped identify gaps in existing standards, and determine important elements that had to be present, and variations in terminology used across different standards that required harmonization. For example, some existing standards report date and time under the column name ‘datetime’ while another reports the same information, as ‘ValueDateTime’ (see example of a terminology crosswalk³⁵). Here, we provide a brief narrative of methods for each reporting format with details on existing data standards and other data resources reviewed during reporting format development. For further details on the technical aspects of each reporting format, please refer to ESS-DIVE’s community space on GitHub³⁶ or view the datasets for each reporting format submitted to ESS-DIVE (Supplementary Table 1).
Each team created instructions and (meta)data templates for their reporting formats. The teams piloted the formats within their research groups and communities to ensure the templates were practical and useful for scientists who collect and reuse data (Fig. 2). In total, 247 individuals representing 128 institutions provided input at various stages of the reporting format development process. As the reporting format instructions and templates reached a final stage, they published the “ready-to-use” reporting formats in three locations each with distinct benefits for the end-users: GitHub³⁷, GitBook, and the ESS-DIVE data repository to enable findability and long-term preservation.
Each of the 11 ESS-DIVE (meta)data reporting formats were developed in cross-functional teams that often involved domain scientists, software engineers, and informatics specialists.
The goal for creating the dataset metadata reporting format was to ensure that any dataset submitted to ESS-DIVE would have complete and descriptive metadata to enable its findability and citation upon use. The ESS-DIVE team reviewed machine and human-readable metadata standards including the Ecological Metadata Language⁵² as well as JSON for Linking Data⁵³. The ESS-DIVE metadata reporting format follows existing metadata standards as much as possible (e.g., ‘title’ in Ecological Metadata Language is also ‘title’ for ESS-DIVE’s metadata).
The file-level metadata reporting format was developed for users to provide details about the individual files contained within a dataset. The review of existing standards²⁶ included file-level metadata used across 6 organizations (e.g., USGS, NEON).
The CSV reporting format was developed to provide guidelines for more consistently formatting tabular data²⁷. The intention was to make this a domain agnostic set of guidelines so that anyone who works with tabular data can use the format in their research to make tabular data more interoperable and machine-readable. The team reviewed existing standards and guidelines (Supplementary Table 1) including recommendations from the Environmental Data Initiative (e.g., do not mix data types in a column) and the ORNL DAAC (e.g., indicating missing numeric values with −9999).
The ESS-DIVE Sample ID reporting format²⁸ aligns as much as possible with extensive work on IGSN⁵⁴ with the goal of standardizing sample collection metadata and more efficiently tracking physical samples sent to different collaborators, labs, data systems, etc. This work also reviewed 12 different standards and data resources to provide recommendations for improving interoperability of biological^8,55 and environmental samples¹⁴.
The model data archiving reporting format²⁹ was informed by input from the DOE’s land modeling community and other guidelines from the American Geophysical Union and National Science Foundation Earthcube communities. In developing the guidelines⁴⁹, the goal was to help modelers make decisions about which components of their terrestrial models should be archived in a long-term data repository. The guidelines were developed with input on which model data were most useful to archive, how long they remained useful, and what scientific purpose they would serve.
The goal of developing the location metadata reporting format was to provide generalized guidelines for describing locations used in research. The review of existing standards included metadata templates from specific projects at some of the DOE’s National Labs to understand the different field sampling strategies of large interdisciplinary projects. The review also included known standards and guidelines for recording locations such as Climate and Forecast Conventions⁵⁶, the Federal Geographic Data Committee’s Content Standard for Digital Geospatial Metadata⁵⁷ and the Open Geospatial Consortium⁵⁸.
In addition to the set of 6 cross-domain reporting formats described above, we also developed 5 formats that are tailored to specific data types commonly used in the terrestrial and subsurface ecosystem research community. ESS-DIVE’s goal was to engage Earth and environmental scientists to determine practical reporting formats that data contributors are willing to use while at the same time ensuring a high potential for data reuse.
The reporting format for amplicon abundance table metadata was developed to facilitate consistent reporting of microbiome sample data with the format of these tables following ESS-DIVE’s CSV file guidelines. Required data (e.g., representative sequences) were chosen to support comparisons of abundance tables across studies. The reporting format distinguishes between sequencing metadata and bioinformatic processing metadata for amplicon abundance tables. As much as possible, the team aligned recommendations for sequencing metadata with the existing standards developed by the Genomic Standards Consortium for minimum information about a marker gene sequence and minimum information about any (x) sequence⁵⁵ (Supplementary File 6). In the absence of an existing standard for bioinformatic processing metadata, the reporting format contains a minimal set of fields to capture the data processing steps most relevant for comparing and combining amplicon counts across studies (Supplementary Table 1). The final set of sequencing and bioinformatic metadata fields selected were informed by a community of scientists involved with either the development of microbiome data pipelines or conducting microbiome studies in both field and lab settings.
The team working on this reporting format³² reviewed existing conventions used in plant trait databases, large data collections developed for synthesis papers, and the variable descriptions that are part of standard instrument outputs in order to determine the most suitable variable names to use to report leaf-level gas exchange data. Templates for formatting metadata about the methods and sample materials used in an experiment, as well as details on the instrumentation involved in collecting data were developed through an iterative process of input and review open to all interested stakeholders. The reporting format is designed to be flexible and modular, provides guidelines on the archive of raw and processed data, and seeks to capture experimental metadata needed to interpret and reuse these data types⁴⁷.
To create the soil respiration reporting format, this team reviewed and integrated recommendations from 9 existing guidelines and standards (Supplementary Table 1)³³. The review captured an array of how different standards format their general metadata and data (e.g., formatting date and timestamps) and also accounted for a range of soil-atmosphere gas exchange data types (e.g., GHGs or radiocarbon)⁴⁸.
The goal in creating a reporting format for water-soil-sediment data was to harmonize chemical concentration data that span several measurement types. The review included 15 standards (Supplementary Table 1) for related environmental chemistry measurements including metadata elements from the EPA’s WQX¹⁴ as well as EarthChem⁵⁹. Based on input from the potential ESS user community that included both data collectors, managers, and modelers, we developed a reporting format based on community input³⁴.
This reporting format harmonizes variables common to sonde-based hydrologic monitoring research including water level, temperature, and pH data. The existing standards and/or data sources included in the crosswalk for the hydrologic monitoring reporting format (Supplementary Table 1) were chosen for inclusion given their common use in the scientific community. They aligned generally on the types of hydrologic metadata to record (e.g., information about dates and times as well as information about data collection sites) but had different terminology across each of the resources³⁵. The development of the reporting format included a review of additional data sources and standards beyond those listed in the crosswalk (Supplementary Table 1).
Each data reporting format and all supporting documentation are hosted on our GitHub Community Space³⁶ and archived in the ESS-DIVE data repository^{25,26,27,28,29,30,31,32,33,34,35}. The supplementary information for this manuscript is also archived in ESS-DIVE^{60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147}.
We have made code available which enables file-level metadata extraction⁵¹ for files that adhere to the reporting format.
Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).
Article  PubMed  PubMed Central  Google Scholar 
Stall, S. et al. Advancing FAIR data in Earth, space, and environmental science. Eos 99 (2018).
Toelch, U. & Ostwald, D. Digital open science-Teaching digital tools for reproducible and transparent research. PLoS Biol. 16, e2006022 (2018).
Article  PubMed  PubMed Central  Google Scholar 
Varadharajan, C. et al. Launching an Accessible Archive of Environmental Data. Eos 100 (2019).
Tenopir, C. et al. Data sharing, management, use, and reuse: Practices and perceptions of scientists worldwide. PLoS One 15, e0229003 (2020).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Perrier, L., Blondal, E. & MacDonald, H. The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis. PLoS One 15, e0229182 (2020).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Sansone, S. A. et al. FAIRsharing as a community approach to standards, repositories and policies. Nat. Biotechnol. 37, 358–367 (2019).
Article  CAS  PubMed  PubMed Central  Google Scholar 
Wieczorek, J. et al. Darwin Core: an evolving community-developed biodiversity data standard. PLoS One 7, e29715 (2012).
Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 
Poisot, T., Bruneau, A., Gonzalez, A., Gravel, D. & Peres-Neto, P. Ecological Data Should Not Be So Hard to Find and Reuse. Trends Ecol. Evol. 34 (2019).
Michener, W. K. Ecological data sharing. Ecol. Inform. 29, 33–44 (2015).
Article  Google Scholar 
Lin, D. et al. The TRUST Principles for digital repositories. Sci. Data 7, 144 (2020).
Article  PubMed  PubMed Central  Google Scholar 
ISO. Date and Time Format (ISO Standard Number 8601-1:2019) (2019).
Bröring, A., Stasch, C. & Echterhoff, J. OGC Sensor Observation Service Interface Standard, Version 2.0. (2012).
Read, E. K. et al. Water quality data for national‐scale aquatic research: The Water Quality Portal. Water Resour. Res. 53, 1735–1745 (2017).
Article  ADS  Google Scholar 
AmeriFlux. BADM: Biological, Ancillary, Disturbance, and Metadata https://ameriflux.lbl.gov/data/badm/ (2020).
Dañobeitia, J. J. et al. Toward a Comprehensive and Integrated Strategy of the European Marine Research Infrastructures for Ocean Observations. Front. in Mar. Sci. 7 (2020).
Cocco, M. et al. The EPOS Research Infrastructure: a federated approach to integrate solid Earth science data and services. Ann. Geophys. 65, DM208–DM208 (2022).
Article  Google Scholar 
Flux Processing (FP-In). Half-Hourly/Hourly Data Upload Format https://ameriflux.lbl.gov/half-hourly-hourly-data-upload-format/ (2017).
Goodman, A. et al. Ten Simple Rules for the Care and Feeding of Scientific Data. PLoS Comput. Biol. 10, e1003542 (2014).
Article  PubMed  PubMed Central  Google Scholar 
Lowndes, J. S. S. et al. Our path to better science in less time using open data science tools. Nat. Ecol. Evol. 1, 0160 (2017).
Article  Google Scholar 
Jacobsen, A. et al. FAIR principles: Interpretations and implementation considerations. Data Intelligence 2, 10–29 (2020).
Article  Google Scholar 
Bailo, D. et al. Perspectives on the Implementation of FAIR Principles in Solid Earth Research Infrastructures. Front. Earth Sci. Chin. 8 (2020).
Environmental System Science Data Infrastructure for a Virtual Ecosystem. ESS-DIVE https://data.ess-dive.lbl.gov/data.
Hills, D. J. et al. Earth and space science informatics perspectives on integrated, coordinated, open, networked (ICON) science. Earth Space Sci. 9 (2022).
Agarwal, D. et al. ESS-DIVE Reporting Format for Dataset Package Metadata. ESS-DIVE https://doi.org/10.15485/1866026 (2022).
Velliquette, T. et al. ESS-DIVE Reporting Format for File-level Metadata. ESS-DIVE https://doi.org/10.15485/1734840 (2021).
Velliquette, T. et al. ESS-DIVE Reporting Format for Comma-separated Values (CSV) File Structure. ESS-DIVE https://doi.org/10.15485/1734841 (2021).
Damerow, J. et al. ESS-DIVE global sample numbers and metadata reporting format for Environmental Systems Science (IGSN-ESS). ESS-DIVE https://doi.org/10.15485/1660470 (2020).
Simmonds, M. B. et al. ESS-DIVE guidelines for archiving terrestrial model data. ESS-DIVE https://doi.org/10.15485/1813868 (2021).
Crystal-Ornelas, R. et al. ESS-DIVE Reporting Format for Location Metadata. ESS-DIVE https://doi.org/10.15485/1865730 (2022).
Weisenhorn, P. & Beilsmith, K. ESS-DIVE Reporting Format for Amplicon Abundance Tables. ESS-DIVE https://doi.org/10.15485/1865729 (2022).
Ely, K. S., Rogers, A. & Crystal-Ornelas, R. ESS-DIVE reporting format for leaf-level gas exchange data and metadata. ESS-DIVE https://doi.org/10.15485/1659484 (2020).
Bond-Lamberty, B., Christianson, D. S., Crystal-Ornelas, R., Mathes, K. & Pennington, S. C. ESS-DIVE reporting format for field measurements of soil respiration. ESS-DIVE https://doi.org/10.15485/1798520 (2021).
Boye, K. et al. ESS-DIVE Reporting Format for Sample-based Water and Soil Chemistry Measurements. ESS-DIVE https://doi.org/10.15485/1865731 (2022).
Goldman, A. E., Ren, H., Torgeson, J. & Zhou, H. ESS-DIVE Reporting Format for Hydrologic Monitoring Data and Metadata. ESS-DIVE https://doi.org/10.15485/1822940 (2021).
ESS-DIVE Community Space. ESS-DIVE Community Space https://github.com/ess-dive-community (2021).
Crystal-Ornelas, R. et al. A guide to using GitHub for developing and versioning data standards and reporting formats. Earth Space Sci. 8 (2021).
Novick, K. A. et al. The AmeriFlux network: A coalition of the willing. Agric. For. Meteorol. 249, 444–456 (2018).
Article  ADS  Google Scholar 
AmeriFlux Data Policy. AmeriFlux Data Policy https://ameriflux.lbl.gov/data/data-policy/ (2021).
Onboarding and Orientation for new site teams. https://ameriflux.lbl.gov/sites/onboarding-and-orientation-for-new-site-teams/ (2017).
Hubbard, S. S. et al. The East River, Colorado, watershed: A mountainous community testbed for improving predictive understanding of multiscale hydrological–biogeochemical dynamics. Vadose Zone J. 17, 1–25 (2018).
Article  Google Scholar 
Kakalia, Z. et al. The Colorado East River community observatory data collection. Hydrol. Process. 35 (2021).
OSTI. Instructions for announcement of U.S. Department of Energy (DOE) publicly available scientific research datasets https://www.osti.gov/elink/F2416instruct.jsp (2017).
Damerow, J. et al. Sample identifiers and metadata to support data management and reuse in multidisciplinary ecosystem sciences. Data Sci. J. 20, 1–19 (2021).
Article  Google Scholar 
Klump, J. et al. Towards globally unique identification of physical samples: Governance and technical implementation of the IGSN global sample number. Data Sci. J. 20 (2021).
Richard, S. M. et al. Internet of samples. Proc. Assoc. Inf. Sci. Technol. 58, 813–815 (2021).
Article  Google Scholar 
Ely, K. S. et al. A reporting format for leaf-level gas exchange data and metadata. Ecol. Inform. (2021).
Bond-Lamberty, B., Christianson, D. S., Crystal-Ornelas, R., Mathes, K. & Pennington, S. C. A reporting format for field measurements of soil respiration. Ecol. Inform. (2021).
Simmonds, M. B. et al. Guidelines for Publicly Archiving Terrestrial Model Data to Enhance Usability, Intercomparison, and Synthesis. Data Sci. J. 21 (2022).
Fowler, D., Barratt, J. & Walsh, P. Frictionless data: Making research data quality visible. Int. J. Digit. Curation 12, 274–285 (2018).
Article  Google Scholar 
McNelis, J., Crow, M. & Devarakonda, R. ESS-DIVE File Level Metadata Extractor. DOE Code https://doi.org/10.11578/DC.20201103.5 (2020).
Jones, M. et al. Ecological Metadata Language version 2.2.0. KNB Data Repository https://doi.org/10.5063/F11834T2 (2019).
Sporny, M., Longley, D., Kellogg, G., Lanthaler, M. & Lindström, N. JSON-LD 1.0. W3C recommendation 16, 41 (2014).
Google Scholar 
Lehnert, K. A., Klump, J., Wyborn, L. & Ramdeen, S. IGSN: Trustworthy and Sustainable Services for FAIR Samples. In Geophysical Research Abstracts 21 (2019).
Yilmaz, P. et al. Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications. Nat. Biotechnol. 29, 415–420 (2011).
Article  CAS  PubMed  PubMed Central  Google Scholar 
CF Conventions and Metadata. https://cfconventions.org/index.html (2020).
Federal Geographic Data Committee. Content Standard for Digital Geospatial Metadata. https://ci.nii.ac.jp/naid/10016800076/ (1998).
Observations and Measurements. Observations and Measurements https://www.ogc.org/standards/om (2011).
Walker, J. D., Lehnert, K. A., Hofmann, A. W., Sarbas, B. & Carlson, R. W. EarthChem: International Collaboration for Solid Earth Geochemistry in Geoinformatics. in vol. 2005 IN44A-03 (2005).
Crystal-Ornelas, R. et al. Data from: “Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats”. ESS-DIVE https://doi.org/10.15485/1866606 (2022).
JSON for Linking Data. JSON for Linking Data https://json-ld.org/ (2016).
Brase, J. DataCite – A Global Registration Agency for Research Data. In 2009 Fourth International Conference on Cooperation and Promotion of Information Resources in Science and Technology 257–261 (2009).
Evans, K. et al. ASCII File Format Guidelines for Earth Science Data. (2016).
Federal Geographic Data Committee. National Geospatial Data Assets (NGDA) Metadata Guidelines. (2016).
Shafranovich, Y. Common Format and MIME Type for Comma-Separated Values (CSV) Files. (2005).
USGS Data Dictionaries. https://www.usgs.gov/data-management/data-dictionaries (2020).
EDI. Five phases of data publishing – Phase 2: Format and QC data tables https://environmentaldatainitiative.org/five-phases-of-data-publishing/phase-2/ (2019).
Pepler, S. & Parton, G. BADC-CSV Format for Data Exchange. https://help.ceda.ac.uk/article/105-badc-csv (2009).
Hsu, L. How “clean” should an Excel file be to be considered machine readable. https://my.usgs.gov/confluence/pages/viewpage.action?pageId=559852026 (2016).
NEON. NEON file naming conventions. https://data.neonscience.org/file-naming-conventions (2022).
Tarboton, D. G., Horsburgh, J. S. & Maidment, D. R. CUAHSI community Observations Data Model (ODM) version 1.1 design specifications. Des Doc (2008).
StreamPulse uploading data. http://pulseofstreams.weebly.com/uploading_data.html (2022).
Cerf, V. ASCII format for Network Interchange. (1969).
Newell, D. B. & Tiesinga, E. The International System of Units (SI) (2019 Edition). (National Institute of Standards and Technology, 2019).
EPSG. WGS 84. (1984).
ORNL DAAC CSV Standards. https://daac.ornl.gov/submit/csvstandards/ (2018).
Data Quality Review Checklist. https://daac.ornl.gov/submit/qachecklist/https://daac.ornl.gov/submit/qachecklist/ (2022).
USGS Data Templates. https://www.usgs.gov/products/data-and-tools/data-management/data-templates (2022).
National Archives. https://www.archives.gov/records-mgmt/policy/transfer-guidance-tables.html#structuredata (2022).
Klyne, G. & Newman, C. Date and Time on the Internet: Timestamps. (2002).
Loescher, H. AmeriFlux BASE CR-Lse La Selva. AmeriFlux https://doi.org/10.17190/AMF/1246013 (2016).
Torn, M. & Dengel, S. AmeriFlux US-NGB NGEE Barrow. AmeriFlux https://doi.org/10.17190/AMF/1436326 (2018).
Brokaw, N. Luquillo Forest Dynamics Plot (LFDP) Liana Data. Environmental Data Initiative https://doi.org/10.6073/PASTA/EABE6E15723324EA3938B456D5BB02C2 (2017).
Bueno de Mesquita, C. P. Plant colonization of moss-dominated soils in the alpine: Microbial and biogeochemical implications. Environmental Data Initiative https://doi.org/10.6073/PASTA/C0CACD100CD89DA258B40A77FBB2FD4C (2019).
Brooks, S. East Fork Poplar Creek sonde data at kilometer 16.2 water year 2019. Oak Ridge National Laboratory (ORNL) https://doi.org/10.12769/1569821 (2019).
Riscassi, A. & Brooks, S. East Fork Poplar Creek discharge at kilometer 5.4 water year 2012. Oak Ridge National Laboratory (ORNL) https://doi.org/10.12769/1489524 (2019).
National Ecological Observatory Network (NEON). Coarse downed wood bulk density sampling (DP1.10014.001). National Ecological Observatory Network (NEON) https://doi.org/10.48443/Z0TG-QS14 (2020).
Salmon, V., Iversen, C., Childs, J. & VanderStel, H. NGEE arctic plant traits: Soil cores, Kougarok road mile marker 64, Seward peninsula, Alaska, 2016. NGEE Arctic, Oak Ridge National Laboratory (ORNL) https://doi.org/10.5440/1346200 (2019).
Philben, M. et al. Results of experimental additions of organic nitrogen on soil organic matter decomposition, teller road site, Seward peninsula, 2017 and 2018. NGEE Arctic, Oak Ridge National Laboratory (ORNL) https://doi.org/10.5440/1454263 (2019).
Yaffar, D., Lugo, A. E., Silver, W., Cuevas, E. & Molina Colon, S. Plant root trait measurements raw data, 1962–2018, Island of Puerto Rico. Next-Generation Ecosystem Experiments Tropics; Oak Ridge National Laboratory https://doi.org/10.15486/NGT/1558773 (2019).
Norby, R. et al. Root-soil depth profile in Luquillo Experimental Forest, Puerto Rico, February, 2019. Next-Generation Ecosystem Experiments Tropics; Oak Ridge National Laboratory https://doi.org/10.15486/NGT/1574087 (2019).
Griffiths, N. & Sebestyen, S. SPRUCE porewater chemistry data for experimental plots beginning in 2013. Oak Ridge National Lab’s Terrestrial Ecosystem Science Scientific Focus Area (ORNL TES SFA) https://doi.org/10.3334/CDIAC/SPRUCE.028 (2016).
McPartland, M. Y. et al. SPRUCE: NDVI data from selected SPRUCE experimental plots, 2016–2018. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States) https://doi.org/10.25581/SPRUCE.057/1490190 (2019).
Croteau, M. N., Sikder, M., Poulin, B. A. & Baalousha, M. Laboratory data to assess the effect of nanoparticle size and natural organic matter composition on the bioavailability of platinum nanoparticles to a model freshwater invertebrate species. U.S. Geological Survey https://doi.org/10.5066/P9G18URX (2020).
Danczak, R. E. et al. WHONDRS 48 Hour Diel Cycling Study at the Altamaha River in Georgia, USA. Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS); ESS-DIVE https://doi.org/10.15485/1577263 (2019).
Stegen, J. C. et al. WHONDRS 48 hour Diel cycling study at HJ Andrews Experimental Forest Watershed 1 (WS1). Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS); ESS-DIVE https://doi.org/10.15485/1509695 (2019).
Article  Google Scholar 
SESAR. SESAR Batch Registration Quick Guide. Zenodo https://doi.org/10.5281/zenodo.3874923 (2020).
IGSN Descriptive Metadata Schema. IGSN metadata https://github.com/IGSN/metadata (2017).
Weibel, S., Kunze, J., Lagoze, C. & Wolf, M. Dublin core metadata for resource discovery. Internet Engineering Task Force RFC 2413, 132 (1998).
Google Scholar 
ISO 19156:2011. Geographic information — Observations and measurements https://www.iso.org/standard/32574.html (2011).
Joint Genome Institute Genome Online Database. https://genome.jgi.doe.gov/portal/ (2022).
USGS National Digital Catalog. https://www.usgs.gov/programs/national-geological-and-geophysical-data-preservation-program/national-digital-catalog (2022).
Geologic Materials Repository Working Group. The U.S. Geological Survey Geologic Collections Management System (GCMS)—A master catalog and collections management plan for U.S. Geological Survey geologic samples and sample collections. (2015).
NEON Biorepository Data Portal. https://biorepo.neonscience.org/portal/ (2022).
Hanson, B. Data policies and practices for AGU publications for models and model output. (2020).
Williams, D. N., Lawrence, B. N., Lautenschlager, M., Middleton, D. & Balaji, V. The Earth System Grid Federation: Delivering globally accessible petascale data for CMIP5. Proceedings of the Asia-Pacific Advanced Network 32, 121 (2011).
Article  Google Scholar 
Dryad. https://datadryad.org/ (2022).
Zenodo. https://zenodo.org/ (2022).
DAAC. https://earthdata.nasa.gov/eosdis/daacs (2021).
NCAR. https://rda.ucar.edu/ (2022).
EOL. https://www.eol.ucar.edu/about-eol (2022).
Arctic Data Center. https://arcticdata.io/ (2016).
Federal Geographic Data Committee. Content Standard for Digital Geospatial Metadata Part 1: Biological Data Profile. (1999).
Christianson, D. S. et al. A metadata reporting framework (FRAMES) for synthesis of ecohydrological observations. Ecol. Inform. 42, 148–158 (2017).
Article  Google Scholar 
USGS. https://dashboard.waterdata.usgs.gov/ (2022).
SNOTEL. https://wcc.sc.egov.usda.gov/reportGenerator/ (2022).
Earth Microbiome Project. https://earthmicrobiome.org/protocols-and-standards/metadata-guide/ (2022).
National Center for Biotechnology Information. SRA Metadata and Submission Overview. https://www.ncbi.nlm.nih.gov/sra/docs/submitmeta/ (2019).
Kattge, J. et al. TRY plant trait database – enhanced coverage and open access. Glob. Chang. Biol. 26, 119–188 (2020).
Article  ADS  PubMed  Google Scholar 
LeBauer, D. et al. BETYdb: a yield, trait, and ecosystem service database applied to second generation bioenergy feedstock production. Glob. Change Biol. Bioenergy 10, 61–71 (2018).
Article  Google Scholar 
Maitner, B. S., Boyle, B., Casler, N. & Condit, R. The bien r package: A tool to access the Botanical Information and Ecology Network (BIEN) database. Methods Ecol. Evol. (2018).
ICOS. https://www.icos-cp.eu/ (2022).
Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci Data 7, 225 (2020).
Article  PubMed  PubMed Central  Google Scholar 
Dorich, C. D. et al. Improving N2O emission estimates with the global N2O database. Curr. Opin. Environ. Sustain. 47, 13–20 (2020).
Article  Google Scholar 
Lawrence, C. R. et al. An open-source database for the synthesis of soil radiocarbon data: International Soil Radiocarbon Database (ISRaD) version 1.0. Earth Syst. Sci. Data 12, 61–76 (2020).
Article  ADS  Google Scholar 
Schädel, C. et al. Decomposability of soil organic matter over time: the Soil Incubation Database (SIDb, version 1.0) and guidance for incubation procedures. Earth Syst. Sci. Data 12, 1511–1524 (2020).
Article  ADS  Google Scholar 
Jian, J. et al. A restructured and updated global soil respiration database (SRDB-V5). Earth Syst. Sci. Data 13, 255–267 (2021).
Article  ADS  Google Scholar 
Ojima, D., Mosier, A., Del Grosso, S. & Parton, W. J. TRAGNET analysis and synthesis of trace gas fluxes. Global Biogeochem. Cycles 14, 995–997 (2000).
Article  ADS  CAS  Google Scholar 
Hibbard, K. A., Law, B. E., Reichstein, M. & Sulzman, J. An analysis of soil respiration across northern hemisphere temperate ecosystems. Biogeochemistry 73, 29–70 (2005).
Article  Google Scholar 
Horsburgh, J. S. et al. Observations Data Model 2: A community information model for spatially discrete Earth observations. Environ. Model. Soft. 79, 55–74 (2016).
Article  Google Scholar 
NEON Protocols & Standardized Methods. https://www.neonscience.org/data-collection/protocols-standardized-methods (2020).
EarthChem Data Templates. https://www.earthchem.org/ecl/templates/ (2021).
SLAC. https://www-ssrl.slac.stanford.edu/sfa/ (2022).
WFSFA. https://eesa.lbl.gov/projects/watershed-function-sfa/ (2022).
River Corridor SFA. https://www.pnnl.gov/projects/river-corridor (2022).
AWHSFA. https://www.anl.gov/bio/subsurface-biogeochemical-research (2022).
LLNL Seaborg. https://seaborg.llnl.gov/research/environmental-radiochemistry (2022).
Mercury SFA. https://www.esd.ornl.gov/programs/rsfa/ (2021).
WHONDRS. https://www.pnnl.gov/projects/WHONDRS (2022).
Ameriflux. https://ameriflux.lbl.gov/ (2022).
NEON. https://www.neonscience.org/ (2020).
Vogel, T. M. et al. TerraGenome: a consortium for the sequencing of a soil metagenome. Nat. Rev. Microbiol. 7, 252–252 (2009).
Article  CAS  Google Scholar 
CUAHSI-HIS. Master Controlled Vocabulary Registry for ODM 1.1 http://his.cuahsi.org/mastercvreg/cv11.aspx (2008).
Blodgett, D., Lucido, J. & Kreft, J. Progress on water data integration and distribution: a summary of select US Geological Survey data systems. J. hydroinformatics 18, 226–237 (2016).
Article  Google Scholar 
NWIS Inventory. https://waterdata.usgs.gov/nwis/inventory (2022).
NEON. https://www.neonscience.org/data-samples/data-management/data-formats-conventions (2020).
WQP User Guide. https://www.waterqualitydata.us/portal_userguide/ (2022).
Garayburu-Caruso, V. A. et al. FTICR, NPOC, TN, and moisture of variably inundated sediment across 48 north American rivers. ESS-DIVE https://doi.org/10.15485/1834208 (2021).
Alves, R. J. E. et al. Kinetic and temperature sensitivity properties of soil exoenzymes through the soil profile down to one-meter depth at a temperate coniferous forest (Blodgett, CA). ESS-DIVE https://doi.org/10.15485/1830417 (2021).
Rogers, A., Ely, K. & Serbin, S. Leaf Photosynthetic Parameters: Quantum Yield, Convexity, Respiration, Gross CO2 Assimilation Rate and Raw Gas Exchange Data, Utqiagvik (Barrow), Alaska, 2016. NGEE Arctic Data Search https://www.osti.gov/biblio/1482338 (2021).
Goldman, A. E., Emani, S. R., Pérez-Angel, L. C., Rodríguez-Ramos, J. A. & Stegen, J. C. Perceived costs and benefits of ICON science and foundational documents associated with “Integrated, Coordinated, Open, and Networked (ICON) science to Advance the Geosciences: Introduction and Synthesis of a Special Collection of Commentary Articles”. (2022).
Roebuck, A. et al. FTICR-MS, Sensor, and Environmental Data from 5 Streams Impacted by the 2020 Holiday Farm Fire Associated with: “Spatiotemporal controls on the delivery of dissolved organic matter to streams following a wildfire”. ESS-DIVE https://doi.org/10.15485/1869708 (2022).
Allison, S. & Martiny, J. B. H. Fungal and bacterial growth variation due to drought and nitrogen addition experimental treatments. Loma Ridge Experimental Project. 2010–2012. ESS-DIVE https://doi.org/10.15485/1828589 (2021).
Dove, N., Torn, M., Hart, S. & Tas, N. Chemistry data from soils and soil incubation experiments from the whole-soil warming experiment at Blodgett Forest, CA, 2018, from: “Metabolic capabilities mute positive response to direct and indirect impacts of warming throughout the soil profile”. ESS-DIVE https://doi.org/10.15485/1866269 (2022).
ESS-DIVE. ESS-DIVE https://ess-dive.lbl.gov/ (2022).
Download references
Robert Crystal-Ornelas, Charuleka Varadharajan, Dylan O’Ryan, Madison Burrus, Shreyas Cholia, Joan Damerow, Valerie C. Hendrix, Zarine Kakalia, Fianna O’Brien, Emily Robles, Maegen Simmonds, Karen Whitenack, and Deborah A. Agarwal were funded through the ESS-DIVE repository by the U.S. DOE’s Office of Science Biological and Environmental Research under contract number DE-AC02-05CH11231. Kim S. Ely and Alistair Rogers were supported through the US Department of Energy contract number DE-SC0012704 to Brookhaven National Laboratory. Michael Crow, Susan Heinz, Terri Velliquette, and Jessica N. Welch were supported through the US Department of Energy contract number DE-AC05-1008 00OR22725 to Oak Ridge National Laboratory. We acknowledge the work of Diana Swantek in producing the Fig. 2 illustration. Reporting format development was supported by through the Office of Biological and Environmental Research in the Department of Energy, Office of Science.
Maegen Simmonds
Present address: Pivot Bio, 2910 Seventh Street, Berkeley, CA, 94710, USA
Robert Crystal-Ornelas
Present address: Github, San Francisco, CA, 94107, USA
Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
Robert Crystal-Ornelas, Charuleka Varadharajan, Dylan O’Ryan, Madison Burrus, Joan Damerow, Zarine Kakalia, Emily Robles, Maegen Simmonds & Karen Whitenack
Environmental Studies Department, California State University, Sacramento, 6000 Jed Smith Dr, Sacramento, CA, 95819, USA
Dylan O’Ryan
Argonne National Laboratory, Lemont, IL, 60439, USA
Kathleen Beilsmith & Pamela Weisenhorn
Pacific Northwest National Laboratory, Joint Global Change Research Institute at the University of Maryland–College Park, College Park, MD, 20740, USA
Benjamin Bond-Lamberty & Stephanie C. Pennington
Environmental Geochemistry Group, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA, 94025, USA
Kristin Boye
Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
Shreyas Cholia, Danielle S. Christianson, Valerie C. Hendrix, Fianna O’Brien & Deborah A. Agarwal
Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA
Michael Crow, Susan L. Heinz, Terri Velliquette & Jessica Nicole Welch
Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11973, USA
Kim S. Ely & Alistair Rogers
Pacific Northwest National Laboratory, Richland, WA, 99354, USA
Amy E. Goldman
Integrated Life Sciences, Virginia Commonwealth University, Richmond, VA, 23284, USA
Kayla Mathes
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
You can also search for this author in PubMed Google Scholar
Conceptualization: D.A.A., C.V., Data curation: R.C.O., C.V., D.O., K.B., B.B.L., K.B., M.C., J.D., K.S.E., A.E.G., S.L.H., K.M., S.C.P., A.R., M.S., T.V., P.W., J.N.W., D.A.A., Funding Acquisition: D.A.A., C.V., Methodology: D.A.A., C.V., R.C.O., J.E.D., K.B., B.B.L., K.B., M.C., J.D., K.S.E., A.E.G., S.L.H., K.M., S.C.P., A.R., M.S., T.V., P.W., J.N.W. Project Administration: D.A.A., K.W. Resources: D.A.A. Software: M.C. Supervision: D.A.A., C.V. Visualization: R.C.O., C.V. Writing – original draft: R.C.O., C.V., J.E.D. Writing – review and editing: R.C.O., C.V., D.O., K.B., B.B.L., K.B., M.B., S.C., D.S.C., M.C., J.D., K.S.E., A.E.G., S.L.H., V.C.H., Z.K., K.M., F.O., S.C.P., E.R., A.R., M.S., T.V., P.W., J.W.N., K.W., D.A.A.
Correspondence to Charuleka Varadharajan.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Reprints and Permissions
Crystal-Ornelas, R., Varadharajan, C., O’Ryan, D. et al. Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats. Sci Data 9, 700 (2022). https://doi.org/10.1038/s41597-022-01606-w
Download citation
Received: 16 May 2022
Accepted: 01 August 2022
Published: 14 November 2022
DOI: https://doi.org/10.1038/s41597-022-01606-w
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative
Advertisement
Scientific Data (Sci Data) ISSN 2052-4463 (online)
© 2022 Springer Nature Limited
Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

source