dataesr/scientific-paragraphs-categorization huggingface dataset:
Recommendations Below we present a specific list of recommendations for programmatic data. Numbers in parentheses after each recommendation correspond to the needs outlined in Table  7. Although the examples discussed above focus on large and/or complex data sets accessed via APIs, some improvements can also apply to databases that are accessed primarily through a web interface. Move to a federated model of data exchange. Many of the current challenges with data discovery and exchange could be addressed with a 'federated' model whereby a group of databases has voluntarily agreed to open their data for programmatic access to each other through an API. While the federation does not have the power to compel participation, it does give data providers another source of traffic to their sites and data.
At a time when database usage metrics are used by agencies for consideration of continued funding, this is an attractive inducement. Federation also addresses the API standardization problem. Also, the federation can then serve as a common point of advertisement for each participant's services, which in turn could encourage other providers to participate in the federation. ( Minimize authentication hurdles to data access. Authentication is the process of verifying the identity of a user attempting to log in to a database and insuring that the user is authorized to do so. While authentication is useful to databases because it allows usage tracking, it severely limits programmatic access to data. Therefore, for public data we recommend Genetic and Genomic Database (GDBs) do not require authentication.
If a database does require authentication to access data, GGB databases should move toward a single authentication service (or a small suite of authentication services), and the community should pick one or a few (e. g. OAuth or systems that use OAuth such as CyVerse, Github, ORCiD, Google). API specs must accept authentication details and be well documented. (Table  7 ; requirements 3, 8 and 9) Store data in a manner that is consistent with community standards and adopt appropriate ontologies to allow crossquery and integration across platforms. A specific focus on applying and maintaining appropriate metadata is critical. As experimental methods change, the information associated with raw data also changes. (.
Communication with researchers. Communication with researchers must occur in both directions. GGB databases need to effectively communicate their scope and requirements to users. For example, many researchers, however well intentioned, simply do not know best practices for data management and/or how to find long-term storage for their data. GGB databases need to be more proactive in communicating with researchers to learn the researchers' needs and to prepare for emerging research. GGB databases can also participate in communication between researchers and data librarians to help researcher craft realistic Data Management Plans. It may not be clear to the researcher how to reach out to the GGB database staff to provide feedback or that such feedback is welcome. Many GGB databases often do not have the funds to accomplish all of this on their own.
Communication with federal funding agencies. Federal funding agencies supporting GGB databases include USDA-ARS, USDA, NSF and NIH. While it is clear that these agencies value the role of GGB databases, there are several areas where increased communication with funding agencies would better enable our mission of facilitating scientific discovery and application in agriculture. Challenges occur on two levels: (i) how should proper data submission at GGB databases be encouraged and measured; and (ii) how can GGB databases communicate their challenges and the importance of their work to the funding agencies, in order to be considered for competitive grants. Communication with publishers. Publishers clearly have an important role to play in making data publicly available and some journals have formed a joint data archiving policy  (97).
Most publishers have DNA (genome or gene) data submission policies. In contrast, some forms of research data such as maps, QTL and markers cannot be submitted to primary repositories (such as the INSDC or NCBI), and few peer-reviewed journals require submission of this data to community databases ahead of publication. Even though journals have data submission policies, the majority only 'strongly encourage' data submission. Furthermore, some managing editors do not enforce the journals data policies. Additionally, many reviewers do not verify that the data has been submitted to the database if indicated in the manuscript. Furthermore, metadata required in primary repositories is often insufficient for appropriate reuse within custom databases.
Recommendations Recommendations for communication among GGB databases. Frequent communication among GGB databases is the primary way to ensure successful collaboration  (98). We recommend the following guidelines to improve communication among GGB databases: Join AgBioData. Individuals working at GGB Databases can join here: https://www. agbiodata. org. Members of the email list will receive monthly newsletters about upcoming AgBioData conference calls, meetings or events and interesting publications. Also, each member GGB database is listed on the website. Attend the monthly AgBioData calls. Each member GGB should have at least one representative on the calls. Each database can actively participate in the selection of topics and speakers. In addition to providing talks relevant to GGB databases, the calls also provide a good forum for discussion.
Presentations are recorded and posted at AgBioData. org and YouTube, however, members are encouraged attend the conference calls to participate in discussions. Share information and collaborate. This includes, but is not limited to the following: best practices; data standards and standard operation protocols; data (to reduce redundancy); code and code development; and strategic plans. Create an AgBioData Advisory Board to represent the databases at a higher level. Include federal, competitive programs, industry and researchers and have the AgBioData Steering Committee formally report through yearly reports on activities and progress toward improved communication, collaboration and funding acquisition.
Recommendations for communication with researchers. Mechanisms for outreach. There are several mechanisms for outreach to researchers. The most common form of outreach is meeting and conference attendance. With a large number of researchers at meeting and conferences GGB databases can use these opportunities for workshops, presentations or a database booth. GGB database brochures can be handed out during the meeting and conferences. However, there are a number of researchers that are unable to attend meeting and conferences so it is important that GGB database also use other forms of outreach. These include newsletters, mailing lists, blog posts and social media (i. e. Facebook and Twitter) to inform researchers about new tools or data, webinars, workshops and videos. These forms of outreach can be used together to reach a broader audience.
Using social media during conferences and meetings with the appropriate hashtag can send information about new tools and data to researchers who cannot attend the conference. A prime example of this is the Plant and Animal Genome Conference, which has a strong social media presence. Make it obvious who is doing the work. Many online resources and databases do not mention the people on their teams and only provide an anonymous contact form. Individuals working on a resources or database should be named on the website. Being anonymous creates a barrier to communication, and if contact/feedback forms don't generate a response, there is no further recourse for the researcher to get help. Providing individual staff contact information and even photographs makes it easier for researchers to target questions to the appropriate person.
Photos can enable researchers to find curators at meetings, and in general encourage communication by putting, literally, a human face on the GGB resources. Building in dedicated time at workshops for a 'meet the team' event, well advertized in advance to the research community, is also recommended to increase engagement opportunities. Respond quickly to feedback. Every GGB database should have a feedback form. The feedback should be replied to within a reasonable period of time, say 24 hours during business days, even if you simply acknowledge the receipt of the feedback and provide a date by which the issue can be resolved. Everyone wants to be heard too! Provide tutorials. Many GGB databases provide tutorials and FAQs. We propose having a central Tutorial Section at AgBioData. org, where tutorials relevant to all GGB Databases are available.
This could include tutorials on file formats, or the best repositories for specific data types. Communicate on data management. GGB databases should work with librarians to develop data management training courses. In addition, online data management modules can be developed to be used in graduate education, perhaps offering some type of certification for successful completion, including long-term assessment of training effectiveness. Set up an advisory panel. Implementing advisory panels comprised of active, elected representatives of the stakeholder community, with clear mechanisms for reporting activities and plans of work can allow for direct, constructive feedback from database stakeholders. Recommendations for communication with funding agencies. Form an interagency GGB Database Advisory Board.
The board will meet regularly to discuss and document challenges and opportunities for GGB databases, data submission, data availability, data curation, data sharing and sustainability. Collaborate on data management guidelines. GGB databases and funding agencies should discuss ways to develop more comprehensive and rigorous data management plans and methods to verify proper data submission to representative databases. Engage in joint social media activities to deliver effective impact statements to stakeholders, industry and lawmakers. Identify or create funding opportunities to support GGB database activities. Develop a clear understanding of the needs that both data providers and GGB databases face for effective data management and reuse, and highlight or implement funding mechanisms to support these needs.
Recommendations for communication with publishers. Enforcement and adherence of data policies. Data policies are only as good as the enforcement and adherence of those policies. The GGB database community and others in the research community need to remind publishers, managing editors and authors of the importance of public data. For publishers that do not have clear data availability policies, AgBioData and cohorts can strongly encourage the publishers to establish clear data availability policies. For the journals that do have data availability policies, the managing editors and reviewers must help enforce the data availability policies. If an author fails to follow the journals data availability policies the reviewer is within his/her right to reject the manuscript. Data curators who encounter a paper lacking publicly accessible data without obvious reason (e.
g. proprietary funding) should notify the journal editors. In addition, as a collective, AgBioData will negotiate with the publishers to change public data submission from 'strongly encouraged' to 'required'. Inter-agency sponsored workshop for journal publishers and editors. Overcoming the challenge of reliable data submission will require communication among representatives from the appropriate journals, GGB databases and funding agencies to establish guidelines and an easy-to-submit and police system for researchers and the journals/funding agencies and databases. This would likely be best initiated through an inter-agency sponsored workshop, followed up by regular meetings and assessment of effectiveness. Such a workshop could also develop ways to ensure journal publishers and editors are aware of all relevant GGB databases so they can direct authors of each accepted paper to the proper repository, nomenclature clearing house etc.
Overview The GGB databases that currently comprise the AgBioData Consortium were created to serve the needs of researchers for access to curated and integrated data and analysis/visualization tools to aid scientific discovery, translation and application. The funding for these databases, however, is limited and not stable. Maintaining these resources in the longer term so that invaluable data are kept up-to-date and do not get lost is a major issue facing almost all AgBioData databases, their researcher communities and funding agencies.
Challenges AgBioData databases are supported through a variety of sources. Generally these fall into one of four categories: (i) primarily supported through line-item government funding, such as the USDA-ARS databases MaizeGDB, SoyBase, GrainGenes, Legume Information System and GRIN; (ii) primarily supported through competitive federal grants, such as TreeGenes, Hardwood Genomics, Gramene, Planteome, Solanaceae Genomics Network and Araport; (iii) supported through a combination of competitive federal grants, commissions and industry, such as the Genome Database for Rosaceae, AgBase, PeanutBase, Ani-malQTLdb and CottonGen; and (iv) supported primarily through a user subscription model, such as TAIR. With long-term government funding, the USDA-ARS databases enjoy the most stable financial support of the AgBioData databases.
They typically represent high-value commodity crops serving a large research and industry community. While the level of support provided by USDA-ARS generally allows for continuation of base activities and curation, it typically does not provide resources for technical innovation or more resource-efficient systems to be implemented. For these, funding through competitive grants is increasingly necessary, as in the case of the NSFfunded Legume Federation award. At the other extreme lies TAIR, which after a phased withdrawal of support by NSF, successfully implemented a subscription-type funding model under a not-for-profit organizational structure  (99). As the model plant for functional genomics, TAIR also has a large user community making this funding option more feasible to implement than for the databases represented in categories 2 and 3.
Recommendations While the purpose of this white paper is not to specifically recommend solutions to increase or stabilize funding of AgBioData databases, it should serve to start a meaningful and informed dialog with funding sources and research communities on sustainability of these resources. All the AgBioData databases should develop sustainability plans through detailed strategic planning that actively includes stakeholders and funding agencies. These plans should detail and communicate the value proposition of their resource, construct budgets that plan for both subsistence-type operational costs and growth-type activities and even include exit plan strategies for the databases, should funding cease. The release of the 'Process Guide for Data Repository Sustainability' document by the Ecological Society of America following the NSF-sponsored Data Repository Sustainability Workshop held January 2018 should serve as a useful resource to develop these sustainability plans.
Federal funding should be provided to support an overall review of AgBioData databases sustainability options. This could be done by an organization that has already successfully gone through this process, such as Phoenix Bioinformatics for TAIR. One option might be to form a new not-for-profit organization, possibly through a partnership with Phoenix Bioinformatics, to create a process where researchers who budget support for data deposit and/or data access in their successful proposals can easily transfer these funds to their community databases through the notfor-profit. This would remove some of the current obstacles to accepting support from scientists that the databases face and provide a steady flow of funds once it become standard practice by scientists to include these in grants. Funding agencies should require a sustainability plan in proposals for new biological online resources.
Conclusions To enable all GGB databases to help their stakeholders utilize the increasingly complex biological data within and across the databases, we need to work together to adopt a common set of metadata, ontologies and communication practices; make it easy to share data; share curation practices; use common software platforms and components where reasonable; and provide solutions for longterm funding. The AgBioData consortium believes that the recommendations laid out in this white paper and continued communication between the people that power the many different GGB databases will allow us all to move forward in the right direction. Regular communication and transparency is the key for all these endeavors. While recognizing that it may be difficult for databases to comply with all of the recommendations outlined in this paper, a slow and steady push toward meeting the recommendations should be encouraged.
This will not only make the work of biocurators more productive, it will improve the experience for researchers. For all agriculturally relevant GGB database personnel, we recommend joining the AgBioData consortium, attending the monthly conference call seminars, joining a working group and working with your counterparts in other databases listed at agbiodata. org. A little time invested now in working together and applying common standards can save a lot of time later and prevent duplicated efforts in the development of tools and resources. While the next step for AgBioData members is to work toward compliance with the recommendations outlined in this paper, we also realize that working closely with researchers, journals and funders is critical. Just as no one author of this paper was aware of all the resources mentioned herein, the situation can be even more confusing for scientists who primarily work at the bench or in the field, especially those not working in genomics.
The Monarch Initiative. (https://monarchinitiative. org) integrates genotype and phenotype data from multiple species and sources. The resulting integrated knowledge graph, analytic tools and web services enable researchers to explore relationships between phenotypes and genotypes across species. Standardized metadata and formal ontologies are used to better enable data sharing, reproducibility and integration. Computer-readable data is shared using the Biolink API (https://github. com/biolink; https://api. monarchinitiative. org/api). Monarch also uses Phenopackets (https://github. com/phenopackets) for standardized data exchange of phenotype data.
Funding  US Department of Agriculture  ( 5030-21000-068-00D ,  2030-21000-021-00D ,  3625-21000-062-00D ,  5030-21000-061-00D ,  2014-2014-07898 ,  2014-51181-22376 ,  2015-70016-23028 ,  2016-67015-24767 ,  8062-21000-041-00D ,  AFRI-005515 ,  NRSP-10  and  NRSP-8 ); The  National Science Foundation  ( 1443040 ,  0735191 ,  1265383 ,  1444806 ,  1127112 ,  1340112 ,  1444573 ,  1564366 ); The  National Institutes of Health  ( 5R01HG004483 ,  5R01GM080203 ); The  US Department of Energy  (Contract No. DE-AC02-05CH11231 ); The  Bill and Melinda Gates Foundation  ( OP1052983 ,  OPP1048542 ); The  Computational Biology Institute of Montpellier  ( ANR-11-BINF-0002 ); The Consultative Group for  International Agricultural Research (CGIAR) Platform for Big Data in Agriculture, The Cotton Incorporated and Cotton Industry ,  Fondazione Edmund Mach (FEM) Research and Innovation Center , The  International Institute of Tropical Agriculture , The Northern Pulse Growers, The US  Dry Pea  and  Lentil Council , The  US Land Grant Universities ; and The  Washington Tree Fruit Research Commission.
that publishers require data and metadata submission to public resource as a requirement for publication 4. Recommend that funding agencies require data and metadata submission to public resources as a require- ment for funding. Funding agencies should hold grantees accountable by requiring proof of proper data submissions to a public resource. GGB databases can help by including funding sources and grant IDs in the metadata. 5. Recommend. Publishers should require authors to submit to archival resources and require proof of proper data submission to public resources before publication. This was very successful in the early stages of establishing the submission of germplasm to the Arabidopsis Biological Resource Center or requiring GenBank IDs for any sequence. GGB databases can help by providing best practices and support for ingestion of these data sets.
training for researchers on responsible data management interacting with GGB databases, how to attach a license to your data, how to ensure your data stays with your publication and more will be useful training tools. This information can be presented as short online videos or FAQs and as live presentations to researchers. Encouraging collaborations between biocu- rators and educators to integrate data management and stewardship strategies into graduate and undergraduate course materials allows for the training of the next generation of scientists in responsible data management practices. Course work of this type can be developed in collaboration with institutional librarians who have a similar mission to provide training in data stewardship. 10. Tutorials covering all aspects of data management, including file formats, the collection and publishing of high value metadata along with data,.
Ontology annotations in most GGB databases are limited to certain ontologies. Most through computation instead of through rigorous manual annotation. The use of ontologies could be facilitated if the list of applicable ontologies were readily avail- able. Within the agricultural domain there are many reference ontologies applicable to model and crop plants, livestock, arthropods and other animal species. Table 2 lists some of the ontologies that are applicable to agricultural GGB databases use GO (24) but fewer data. In supplementary material 3, we also describe use additional ontologies such as Plant Trait Ontology ontology libraries and registries, including description (TO) and PO (25-27) to describe their data. In addi- of the Planteome project (http://planteome. org), the Crop tion, with a few exceptions, these terms are assigned Ontology project (CO) (www.
Central curation (with plug ins for Noctua) CACAO community annotation tool used in https://gowiki. tamu. edu/wiki/index. php/Category: undergraduate competitions CACAO Table Editor application for easily editing https://incatools. github. io/table-editor/?config=. %2 spreadsheet-formatted data with Fconfig. yaml associated ontologies PhenoteFX phenotype curation http://phenotefx. readthedocs. io/en/latest/ Annotation data GAF2 file format for ontology annotation data http://geneontology. org/page/go-annotation-file- exchange formats exchange format-20 RDF Resource Description Framework Phenopackets an extensible data model and data exchange format for phenotype data https://github. com/phenopackets/phenopacketformat/wiki/Overview-of-Phenotype-Exchange-Format BioLink model schema for biological data and associations https://biolink.