2020 ESIP Winter Meeting

Several professional organizations have become increasingly concerned about the loss of reusable data from primary sources such as individual researchers, projects, and agencies. DataAtRisk.org aims to connect people with data in need, to data expertise, and is a response to the clear need for a community building application. This “Data at Risk” commons will allow individuals to submit and request help with threatened datasets and connect these datasets to experts who can provide resources and skills to help rescue data through a secure, professional mechanism to facilitate self-identification and discovery.

This session will provide an overview of the current status of the DataAtRisk.org project, and aims to expand the network of individuals involved in the development and implementation of DataAtRisk.org

How to Prepare for this Session: Please check out https://dataatrisk.org/ for some background on the activities.

Presentations: http://bit.ly/303gig7, https://doi.org/10.6084/m9.figshare.11536317.v1
Link to use case / user scenario: https://tinyurl.com/yh4rnk7b

View Recording: https://youtu.be/96NMQwx_EtI

Takeaways

• Perfection is the enemy of getting stuff done
• Something is better than nothing
• Triage will be necessary at several places in the process
  
  
  

At the ESIP Summer 2019, many ESIP community members offered their feedback on the range and importance of skills and competencies for data specialists whose job responsibilities focus upon offering data "advise" (e.g., from data curators) and data "service providers" (e.g., from data librarians). By means of an interactive poster, participants were asked to choose whether a competency was of high, medium, low or no importance from a subset of competencies identified by a European Open Science Cloud (EOSC) project. In this session, session leaders will present the results of the ESIP community feedback within the context of the full list of EOSC competencies, and visualized from both a poster synthesis and a research data lifecycle point of view. Session leaders are hoping to have the audience participate by providing feedback and engaging in discussion on the data and views presented. One outcome of this work will be a "Career Compass" to be published by the American Geoscience Institute for students interested in becoming data stewards. How to Prepare for this Session:

Presentations:

View Recording: https://youtu.be/1s1L3Jter8w

Takeaways

COPDESS, the Coalition for Publishing Data in the Earth and Space Sciences (https://copdess.org/), was established in October 2014 as a platform for Earth and Space Science publishers and data repositories to jointly define, implement, and promote common policies and procedures for the publication and citation of data and other research results (e.g., samples, software, etc.) across Earth Science journals. In late 2018, COPDESS became a cluster of ESIP to give the initiative the needed sustainability to support a long-term FAIR publishing workflow ecosystem and be a springboard to pursue future enhancements of it.

In 2017, with funding from the Arnold Foundation, the ‘Enabling FAIR Data Project’ (https://copdess.org/enabling-fair-data-project/) moved mountains towards implementing the policies and standards that connect researchers, publishers, and data repositories in their desire to accelerate scientific discovery through open and FAIR data. Implementation of the new FAIR policies has advanced rapidly across Earth, Space, and Environmental journals, but supporting infrastructure, guidelines, and training for researchers, publishers, and data repositories has yet to catch up. The primary challenges are:

• Repositories struggle to keep up with the demands of researchers, who want to be able to instantly deposit data and obtain a DOI, without considering the data quality/data ingest requirements and review procedures of individual repositories - producing a situation where data publication is inconsistent in quality and content.
• Many publishers who have signed the Commitment Statement for FAIR Data (https://copdess.org/enabling-fair-data-project/commitment-statement-in-the-earth-space-and-environmental-sciences/) agree with it at a high, conceptual level. However, many journal editors and reviewers lack clarity on how to validate that datasets, which underpin scholarly publications, conform with the Commitment Statement.
• Researchers experience confusion, and in some cases barriers to publication of their papers whilst they try and meet the requirements of the commitment statement. Clarity of requirements, timelines, and criteria for selecting repositories are needed to minimize the barriers to the joint publication of papers and associated data.

Funders have a role to play, in that they need to allow for time and resources required to curate data and ensure compliance, particularly WRT to the assignment of valid DOIs. Funders can also begin to reward those researchers who do take the effort to properly manage and make their data available, in a similar way to how they reward scholarly publications and citation of those publications.

The goal of this session is to start a conversation on developing an integrated publishing workflow ecosystem the seamlessly integrates researchers, repositories, publishers and funders. Perspectives from all viewpoints will be presented.

Notes document: https://docs.google.com/document/d/12M0F6mcUZSn2GdBN-Id__smXhYxbLzKDrAViPAgnH6w/edit?usp=sharing

Presentations:

View Recording: https://youtu.be/x6a1QRNbifQ

Takeaways

• COPDESS has moved to ESIP as a cluster to ensure the sustainability of the project to address the publishing & citation of research data

NASA, NOAA and USGS are in the process of moving data onto the cloud. While they have discussed what types of services are available and future plans of what data can be found, it is not completely clear what datasets users can currently access. This session will go over what datasets are currently up in the cloud and what data to expect in the near future. This way as users are transitioning to the cloud for their compute, they can also know what data are available to them on the cloud as well. There will also be presentations from AWS. Speakers:
Katie Baynes - NASA/EOSDIS
Jon O'Neil - NOAA
Jeff de La Beaujardiere - NCAR
Kristi Kliene - USGS/EROS
Joe Flasher - AWS

Presentations: See attached.

View Recording: https://youtu.be/yssgXB7iaxw

Takeaways

• Petabyte scale data is being moved into the cloud. This is concentrated in AWS, Google Cloud and Microsoft depending on the agency and dataset
• Some concern around partnerships with companies (AWS most discussed) in terms of long term relationships, moving data etc. and how those things might impact access or data use
• Need to make clear the authoritative source of the data, who is stewarding it, and any modifications done when copying to cloud. Users should exercise due diligence in selecting and using data.

In the Fall of 2019, the Information Quality Cluster (IQC) published a white paper entitled “Understanding the Various Perspectives of Earth Science Observational Data Uncertainty”. The intention of this paper is to provide a diversely sampled exposition of both prolific and unique policies and practices, applicable in an international context of diverse policies and working groups, made toward quantifying, characterizing, communicating and making use of uncertainty information throughout the diverse, cross-disciplinary Earth science data landscape; to these ends, the IQC addressed uncertainty information from the following four perspectives: Mathematical, Programmatic, User, and Observational. These perspectives affect policies and practices in a diverse international context, which in turn influence how uncertainty is quantified, characterized, communicated and utilized. The IQC is now in a scoping exercise to produce a follow-on paper that is intended to provide a set of recommendations and best practices regarding uncertainty information. It is our hope that we can consider and examine additional areas of opportunity with regard to the cross-domain and cross-disciplinary aspects of Earth science data. For instance, the existing white paper covers uncertainty information from the perspective of satellite-based remote sensing well, but does not adequately address the in situ or airborne (i.e., sub-orbital) perspective. This session intends to explore such opportunities to expand the scope of the IQC’s awareness of what is being done with regard to uncertainty information, while also providing participants and observers with an opportunity to weigh in on how best to move forward with the follow-on paper. How to Prepare for this Session:

• ESIP 2017 Summer Meeting Plenary Uncertainty Session
• Understanding the Various Perspectives of Earth Science Observational Data Uncertainty
• Ensuring and Improving Information Quality for Earth Science Data and Products
• ESIP IQC Presentation Archive

Agenda:

1. "IQC Uncertainty White Paper Status Summary and Next Steps" - Presented by: David Moroni (15 minutes)
2. "Uncertainty quantification for in situ ocean data: The S-MODE sub-orbital campaign" - Presented by: Fred Bingham (15 minutes)
3. "Uncertainty Quantification for Spatio-Temporal Mapping of Argo Float Data" - Presented by Mikael Kuusela (20 minutes)
4. Panel Discussion (35 minutes)
5. Closing Comments (5 minutes)

Notes Page: https://docs.google.com/document/d/1vfYBK_DLTAt535kMZusTPVCBAjDqptvT0AA5D6oWrEc/edit?usp=sharing

Presentations:
https://doi.org/10.6084/m9.figshare.11553681.v1

View Recording: https://youtu.be/vC2O8FRgvck

Takeaways

• Session notes: https://docs.google.com/document/d/1vfYBK_DLTAt535kMZusTPVCBAjDqptvT0AA5D6oWrEc/edit
• Uncertainty is time and space scale dependent, in-situ ocean measurements are also depth dependent
• More speakers for monthly telecon from areas outside of ocean observations (land cover and other)

In recent years, the integration of physical collections and samples into digital data infrastructure has received increased attention in the context of Open Science and FAIR research results. In order to support open, transparent, and reproducible science, physical samples need to be uniquely identified, findable in online catalogues, well documented, and linked to related data, publications, people, and other relevant digital information. Substantial progress has been made through wide-spread implementation of the IGSN as a persistent unique identifier. What is missing is the development and implementation of protocols and best practices for sample metadata. Effort to do this have shown that it is impossible to develop a common vocabulary that describes all samples collected: one size does not fit all and each domain e.g. soil scientists, volcanologists, cosmochemists, paleoclimate scientists, and granite researchers – to name a few examples - all have their own vocabularies. Yet there is a minimum set of attributes that are common to all samples, the ‘Bull’s Eye of sample metadata’. This session invites participants from all walks of earth and environmental science to help define what is the minimum set of attributes needed to describe physical samples that are at the heart of much of Earth and environmental research.

How to Prepare for this Session:
Participations should come with a list of the mimimum metadata requirements for their institutions or domains.  They should be prepared to give a brief introduction to their needs.

Session Agenda:

1. Introduction to the issue
2. Review of existing examples and discussion of the limitations
3. Discuss minimal requirements; propose changes/addition
4. Summarize outcomes and discuss next steps

Google doc with the current metadata list and proposed changes

Presentations: ​​​​

View Recording: https://youtu.be/bxhTmrNqkCA

Takeaways

We will describe our project to upload a 2.4 PB dataset encapsulated into ~80K fused files from the 5 instruments on the Terra satellite into NASA AWS S3.
We will share the bottlenecks points and lessons learned during this process and expect to share experiences with similar projects in order to understand the best practices and collect guidelines for future projects that are adopting cloud solutions for their data needs.

We'll discuss data volumes, data integrity strategies for migration, S3 bucket organization, metadata curation, transfer rates, transfer pipelines, etc. We will also discuss and share data access patterns, costs, and architectures and how we can construct guidelines for access to these datasets efficiently.

We encourage the discussion among different projects that faced similar processes or are looking to migrate their datasets into the cloud.

https://drive.google.com/file/d/1fts06XDM2dbZxxljBTpplCEMSiTqfp6t/view?usp=sharing

Presentations:
https://doi.org/10.6084/m9.figshare.11553147.v1

View Recording: https://youtu.be/1xVJghJI4Gg

Takeaways

• Project required/used a combination of NSF, NASA and AWS resources. Some interesting discussion around AWS or other cloud services as a stand in or follow on to limited term NSF assets
• Some interesting discussion of tailoring to appropriate end users- wide range of potential users and thus requirements for the dataset. This includes access guidelines, user capabilities etc.
• Project aimed to make a paradigm shift from understanding/observing physical processes to a full climate observing objective

Acquisition and analysis of data in the laboratory are pervasive in the Earth, environmental, and planetary sciences. Analytical and experimental laboratory data, often acquired with sophisticated and expensive instrumentation, are fundamental for understanding past, present, and future processes in natural systems, from the interior of the Earth to its surface environments on land, in the oceans, and in the air, to the entire solar system. Despite the importance of provenance information for analytical data including, for example, sample preparation or experimental set up, instrument type and configuration, calibration, data reduction, and analytical uncertainties, there are no consistent community-endorsed best practices and protocols for describing, identifying, and citing laboratory instrumentation and analytical procedures, and documenting data quality. This session is intended as a kick-off working session to engage researchers, data managers, and system engineers, to contribute ideas how to move forward with and accelerate the development of global standard protocols and the promulgation of best practices for analytical laboratory data. How to Prepare for this Session:

Presentations:

View Recording: https://youtu.be/LOfb_4r7DBA

Takeaways

• Analytical and experimental data are collected widely in both the field and laboratory settings from a variety of earth environmental and planetary sciences, spanning a variety of disciplines. FAIR use of such data is dependent of data provenance. 
• Need community exchange of such data consider use of data is broader than the original use of data in the domain. Brings to mind interoperability of such data. Need networks of these data to be plugged into evolving CI systems. In seismology a common standard for data implemented by early visionaries was a massive boon to the field. 
• Documentation of how analytical data were generated is time consuming for data curators providers etc. Having standards/protocols for data exchange protocols is urgently required for emerging global data networks. OneGeochemistry as example use case for international research group to establish a global network for discoverable geochemical data.
  
  

The ESIP Information Quality Cluster (IQC) has formally defined information quality as a combination of the following four aspects of quality, spanning the full life cycle of data products: scientific quality, product quality, stewardship quality, and service quality. Focus of the IQC has been quality of Earth science data captured by scientists/experts. For example, the whitepaper “Understanding the Various Perspectives of Earth Science Observational Data Uncertainty”, published by IQC in the fall of 2019, mainly addresses uncertainty information from the perspective of satellite-based remote sensing. With the advance of mobile computing technologies, including smart phones, Citizen Science (CS) data have been increasingly becoming more and more important sources for Earth science research. CS data have their own unique challenges regarding data quality, compared with data captured through traditional scientific approaches. The purpose of this session is to broaden the scope of IQC efforts, present the community with the state-of-the-art of research on CS data quality, and foster a collaborative interchange of technical information intended to help advance the assessment, improvement, capturing, conveying, and use of quality information associated with CS data. This session will summarize the scope of what we mean by CS data (including examples of platforms/sensors commonly used in collecting CS data) and include presentations from both past and current CS projects focusing on the topics such as challenges with CS data quality; strategies to assess, ensure, and improve CS data quality; approaches to capturing CS data quality information and conveying it to users; and use of CS data quality information for scientific discovery. 

Agenda (Click titles to view presentations)

1. Introduction - Yaxing Wei - 5 mins
2. Citizen Science Data Quality: The GLOBE Program – Helen M. Amos (NASA GSFC) – 18 (15+3) mins.
3. Can we trust the power of the crowd? A look at citizen science data quality from NOAA case studies - Laura Oremland (NOAA) – 18 (15+3) mins.
4. Turning Citizen Science into Community Science - Stephen C. Diggs (Scripps Institution of Oceanography / UCSD) and Andrea Thomer (University of Michigan)  – 18 (15+3) mins.
5. Earth Challenge 2020: Understanding and Designing for Data Quality at Scale - Anne Bowser (Wilson Center) – 18 (15+3) mins.
6. Discussion and Key Takeaways – All – 13 mins.
   
   View Recording: https://youtu.be/xaTLP4wqwe8
   
   Takeaways

Notes Page:
https://docs.google.com/document/d/1lRp19SF9U727ureKjY38PHOF3EGUgE-BixYDs2KlmII/edit?usp=sharing

Presentation Abstracts

• Citizen Science Data Quality: The GLOBE Program - Helen M. Amos (NASA GSFC)

The Global Learning and Observations to Benefit the Environment (GLOBE) Program is an international program that provides a way for students and the public to contribute Earth system observations. Currently 122 countries, more than 40,000 schools, and 200,000 citizen scientists are participating in GLOBE. Since 1995, participants have contributed 195 million observations. Modes of data collection and data entry have evolved with technology over the lifetime of the program, including the launch of the GLOBE Observer mobile app in 2016 to broaden access and public participation in data collection. GLOBE must meet the data needs of a diverse range of stakeholders, from elementary school classrooms to scientists across the globe, including NASA scientists. Operational quality assurance measures include participant training, adherence to standardized data collection protocols, range and logic checks, and an approval process for photos submitted with an observation. In this presentation, we will discuss the current state of operational data QA/QC, as well as additional QA/QC processes recently explored and future directions. 

• Can we trust the power of the crowd? A look at citizen science data quality from NOAA case studies - Laura Oremland (NOAA)

NOAA has a rich history in citizen science dating back hundreds of years.  Today NOAA’s citizen science covers a wide range of topics such as weather, oceans, and fisheries with volunteers contributing over 500,000 hours annually to these projects. The data are used to enhance NOAA’s science and monitoring programs.   But how do we know we can trust these volunteer-based efforts to provide data that reflect the high standards of NOAA’s scientific enterprise? This talk will provide an overview of NOAA’s citizen science, describe the data quality assurance and quality control processes applied to different programs, and summarize common themes and recommendations for collecting high quality citizen science data. 

• Earth Challenge 2020: Understanding and Designing for Data Quality at Scale - Anne Bowser (Wilson Center)

April 22nd, 2020 marks the 50th anniversary of Earth day.  In recognition of this milestone Earth Day Network, the Woodrow Wilson International Center for Scholars, and the U.S. Department of State are launching Earth Challenge 2020 as the world’s largest coordinated citizen science campaign.  For 2020, the project focuses on six priority areas: air quality, water quality, insect populations, plastics pollution, food security, and climate change.  For each of these six areas, one work stream will focus on collaborating with existing citizen science projects to increase the amount of open and findable, accessible, interoperable, and reusable (FAIR) data.  A second work stream will focus on designing tools to support both existing and new citizen science activities, including a mobile application for data collection; an open, API-enabled data integration platform; data visualization tools; and, a metadata repository and data journal.
A primary value of Earth Challenge 2020 is recognizing, and elevating, ongoing citizen science activities.  Our approach seeks first to document a range of data quality practices that citizen science projects are already using to help the global research and public policy community understand these practices and assess fitness-for-use.  This information will be captured primarily through the metadata repository and data journal.  In addition, we are leveraging a range of data quality solutions for the Earth Challenge 2020 mobile app, including designing automated data quality checks and leveraging a crowdsourcing platform for expert-based data validation that will help train machine learning (ML) support.  Many of the processes designed for Earth Challenge 2020 app data can also be applied to other citizen science data sets, so maintaining information on processing level, readiness level, and provenance is a critical concern.  The goal of this presentation is to offer an overview of key Earth Challenge 2020 data documentation and data quality practices before inviting the ESIP community to offer concrete feedback and support for future work.

Political, economic, social or scientific decision making is often based on integrated data from multiple sources across potentially many disciplines. To be useful, data need to be easy to discover and integrate.
This session will feature presentations highlighting recent breakthroughs and lessons learned from experimentation and implementation of open knowledge graph, linked data concepts and Discrete Global Grid Systems. Practicality and adoptability will be the emphasis - focusing on incremental opportunities that enable transformational capabilities using existing technologies. Best practices from the W3C Spatial Data on the Web Working Group, OGC Environmental Linked Features Interoperability Experiment, ESIP Science on Schema.org; implementation examples from Geoscience Australia, Ocean Leadership Consortium, USGS and other organisations will featured across the entire session.
This session will highlight how existing technologies and best practices can be combined to address important and common use cases that have been difficult if not impossible until recent developments. A follow up session will be used to seed future collaborative development through co-development, github issue creation, and open documentation generation.

How to Prepare for this Session: Review: https://opengeospatial.github.io/ELFIE/, https://github.com/ESIPFed/science-on-schema.org, https://www.w3.org/TR/sdw-bp/, and http://locationindex.org/.

Notes, links, and attendee contact info here.

View Recording: https://youtu.be/-raMt2Y1CdM

Session Agenda:
1.  2.00- 2.10,  Sylvain Grellet, Abdelfettah Feliachi, BRGM, France
'Linked data' the glue within interoperable information systems
“Our Environmental Information Systems are exposing environmental features, their monitoring systems and the observation they generate in an interoperable way (technical and semantic) for years. In Europe, there is even a legal obligation to such practices via the INSPIRE directive. However, the practice inducing data providers to set up services in a "Discovery > View > Download data" pattern hides data behind the services. This hinders data discovery and reuse. Linked Data on the Web Best Practices put this stack upside down and data is now back in the first line. This completely revamp the design and capacities of our Information Systems. We'll highlight the new data frontiers opened by such practices taking examples on the French National Groundwater Information Network”
View Slides: https://doi.org/10.6084/m9.figshare.11550570.v1

2.  2.10 - 2.20,  Adam Leadbetter, Rob Thomas, Marine Institute, Ireland
Using RDF Data Cubes for data visualization: an Irish pilot study for publishing environmental data to the semantic web
The Irish Wave and Weather Buoy Networks return metocean data at 5-60 minute intervals from 9 locations in the seas around Ireland. Outside of the Earth Sciences an example use case for these data is in supporting Blue Economy development and growth (e.g. renewable energy device development). The Marine Institute, as the operator of the buoy platforms, in partnership with the EU H2020 funded Open Government Intelligence project has published daily summary data from these buoys using the RDF DataCube model[1]. These daily statistics are available as Linked Data via a SPARQL endpoint making these data semantically interoperable and machine readable. This API underpins a pilot dashboard for data exploration and visualization. The dashboard presents the user with the ability to explore the data and derive plots for the historic summary data, while interactively subsetting from the full resolution data behind the statistics. Publishing environmental data with these technologies makes accessing environmental data available to developers outside those with Earth Science involvement and effectively lowers the entry bar for usage to those familiar with Linked Data technologies.
View Slides: https://doi.org/10.6084/m9.figshare.11550570.v1

3. 2.20 - 2.30,  Boyan Brodaric, Eric Boisvert, Geological Survey of Canada, Canada; David Blodgett, USGS, USA
Toward a Linked Water Data Infrastructure for North America
We will describe progress on a pilot project using Linked Data approaches to connect a wide variety of water-related information within Canada and the US, as well as across the shared border
View Slides: https://doi.org/10.6084/m9.figshare.11541984.v1

4.  2.30 - 2.40,  Dalia Varanka, E. Lynn Usery, USGS, USA
The Map as Knowledge Base; Integrating Linked Open Topographic Data from The National Map of the U.S. Geological Survey
This presentation describes the objectives, models, and approaches for a prototype system for cross-thematic topographic data integration based on semantic technology. The system framework offers a new perspectives on conceptual, logical, and physical system integration in contrast to widely used geographic information systems (GIS).
View Slides: https://doi.org/10.6084/m9.figshare.11541615.v1

5.  2.40 – 2.50,  Alistair Ritchie, Landcare, New Zealand
ELFIE at Landcare Research, New Zealand
Landcare Research, a New Zealand Government research institute, creates, manages and publishes a large set of observational and modelling data describing New Zealand’s land, soil, terrestrial biodiversity and invasive species. We are planning to use the findings of the ELFIE initiatives to guide the preparation of a default view of the data to help discovery (by Google), use (by web developers) and integration (into the large environmental data commons managed by other agencies). This integration will not only link data about the environment together, but will also expose more advanced data services. Initial work is focused on soil observation data, and the related scientific vocabularies, but we anticipate near universal application across our data holdings.
View Slides: https://doi.org/10.6084/m9.figshare.11550369.v1

6.  2.50 - 3.00,  Irina Bastrakova, Geoscience Australia, Australia
Location Index Project (Loc-I) – integration of data on people, business & the environment
Location Index (Loc-I) is a framework that provides a consistent way to seamlessly integrate data on people, business, and the environment.
Location Index aims to extend the characteristics of the foundation spatial data of taking geospatial data (multiple geographies) which is essential to support public safety and wellbeing, or critical for a national or government decision making that contributes significantly to economic, social and environmental sustainability and linking it with observational data. Through providing the infrastructure to suppo

Session leaders will describe tools being developed to assess the learning resources in the ESIP"s Data Management Training Clearinghouse (DMTC) from the perspectives of both instructors and students. The feedback collected through these tools will aid in identifying and choosing resources appropriate for their needs. First efforts have been focused on using DataONE's EEVA tool to identify and adapt questions. Feedback will be requested from participants to help guide the content, look and feel of the tool. How to Prepare for this Session: Visiting ESIP's Data Management Training Clearinghouse (https://dmtclearinghouse.esipfed.org) would be helpful but not required for productive participation in the session.

Presentations:

View Recording: https://youtu.be/uc4tbjyePpI

Takeaways

Citizen science is scientific data collection and research performed primarily or in part by non-professional and amateur scientists. Citizen science data has been used in a variety of the physical sciences, including physics, ecology, biology, and water quality. As volunteer-contributed datasets continue to grow, they represent a unique opportunity to collect and analyze earth-science data on spatial and temporal scales impossible to achieve by individual researchers. This session will explore the ways open citizen science data sets can be used in earth science research and some of the associated challenges and opportunities for the ESIP community to use and partner with citizen science organizations.

Speakers:

• Ruth Duerr, Citizen Science Examples and Issues https://tinyurl.com/ydszbeqv
  View Slides:https://doi.org/10.6084/m9.figshare.11549256.v1
• Rebecca Forkner, Improving `big unstructured’ citizen data to monitor diversity
• Lourdes Vera
• Brian Hostetler
• Alexis Garretson, Citizen Science Data Management Challenges and Opportunities
  View Slides: https://doi.org/10.6084/m9.figshare.11559081.v1

View Recording: https://youtu.be/jTNgWZI6Cik

Takeaways


How to Prepare for this Session: https://www.nationalgeographic.org/encyclopedia/citizen-science/
http://www.earthsciweek.org/citizen-science

This working session will follow on the "Advancing Data Integration approaches of the structured data web” session and the Coverage Analytics sprint as an opportunity for those interested in building linked data information products that integrate spatial features, coverage data, and more. As such, inspiration will be drawn from projects like science on schema.org, the Environmental Linked Features Interoperability Experiment, the Australian Location Index, and those that session attendees take part in. Participants will self organize into use-case or technology focused groups to discuss and synthesize the outcomes of the sprint and structured data web session. Session outcomes could take a number of forms: linked data and web page mock ups, ideas and issues for OGC, W3C, or ESIP groups to consider, example data or use cases for relevant software development projects to consider, or work plans and proposals for suture ESIP work. The session format is expected to be fluid with an ideation and group formation exercise followed by structured discussion to explore a set of ideas then narrow on a focused valuable outcome. Participants will be encouraged to work together prior to the meeting to design and plan the session structure. Outcomes of the session will be reported at an Information Technology and Interoperability webinar in early 2020. How to Prepare for this Session: Attend the coverage sprint and the "Advancing Data Integration approaches of the structured data web" session.

Shared document for session here.

Full Notes: https://doi.org/10.6084/m9.figshare.11559087.v1

Presentations:

View Recording: https://youtu.be/u2x3I0cr46A

• Takeaways
  Breakout session information interoperability committee and webinar series. See notes: https://docs.google.com/document/d/1LpcTMwP0mAD4G4Gb8mStI5uSDV61_qWPUkQ9nI1x1cI/edit?usp=sharing
• Foster cross-project consistency via breakouts. Such as dealing with science on schema.org issue of Links to “in-band” linked (meta)data and “out of band” linked data. Content negotiation and in-band and out of band links Use blank nodes with link properties for rdf elements that are URI for out of band content. Identify in band links with sdo @id, out of band links with sdo:URL
• Incorporating Spatial Coverages in Knowledge Graphs; Next Steps? Need to explore more on tessellations as an intermediate index. Will carry forward some of these ideas at the EDR SWG Will represent some of these ideas to the OGC-API Coverages SWG Will mention these ideas to the UFOKN Role of ‘spatial’ knowledge graphs Will spatial data analysis and transformation tools grow to adopt/support RDF as an underlying data structure for spatial information or will RDF continue to be a ‘view’ of existing (legacy) spatial data in GI systems?
• 
  

This session is a working group for the 2020-2021 year for the Data Stewardship committee. We will discuss priorities for the next year, potential collaborative outputs, and review the work in progress from the last year. 

Notes Document: https://docs.google.com/document/d/1B_0K5jGnFgH72U3P2-oGr5vEqHOGU8CWU-IkZ6pjXbM/edit?ts=5e174588

Presentations

View Recording: https://youtu.be/am-ZLfHgM4w

Takeaways

• Wow, the members of the Committee really are active! Practically everyone has their own cluster or two!
• Six activities proposed for the upcoming year have champions who will lead the effort to define the outputs of their selected activity.
  
  

Many repositories are seeing an increase in the use and diversity of licenses and other intellectual property management (IPM) tools applied to externally-created data submissions and software developed by staff. However, adding a license to data files may have unexpected or unintended consequences in the downstream use or redistribution of those data. Who “owns” the intellectual property rights to data collected by university researchers using Federal and State (i.e., public) funding that must be deposited at a Federal repository? What license is appropriate for those data and what — exactly — does that license allow and disallow? What kind of license or other IPM instrument is appropriate for software written by a team of Federal and Cooperative Institute software engineers? Is there a significant difference between Creative Commons, GNU, and other ‘open source licenses’?

We have invited a panel of legal advisors from Federal and other organizations to discuss the implications of these questions for data stewards and the software teams that work collaboratively with those stewards. We may also discuss the latest information about Federal data licenses as it applies to the OPEN Government Data Act of 2019. How to Prepare for this Session: Consider what, if any, licenses, copyright, or other intellectual property rights management you apply or think applies to your work. Also consider Federal requirements such as the OPEN Government Data Act of 2019, Section 508 of the Rehabilitation Act of 1973.

Speakers:
Dr. Robert J. Hanisch is the Director of the Office of Data and Informatics, Material Measurement Laboratory, at the National Institute of Standards and Technology in Gaithersburg, Maryland. He is responsible for improving data management and analysis practices and helping to assure compliance with national directives on open data access. Prior to coming to NIST in 2014, Dr. Hanisch was a Senior Scientist at the Space Telescope Science Institute, Baltimore, Maryland, and was the Director of the US Virtual Astronomical Observatory. For more than twenty-five years Dr. Hanisch led efforts in the astronomy community to improve the accessibility and interoperability of data archives and catalogs.
Henry Wixon is Chief Counsel for the National Institute of Standards and Technology (NIST) of the U.S. Department of Commerce. His office provides programmatic legal guidance to NIST, as well as intellectual property counsel and representation to the Department of Commerce and other Department bureaus. In this role, it interacts with principal developers and users of research, including private and public laboratories, universities, corporations and governments. Responsibilities of Mr. Wixon’s office include review of NIST Cooperative Research and Development Agreements (CRADAs), licenses, Non-Disclosure Agreements (NDAs) and Material Transfer Agreements (MTAs), and the preparation and prosecution of the agency’s patent applications. As Chief Counsel, Mr. Wixon is active in standing Interagency Working Groups on Technology Transfer, on Bayh-Dole, and on Research Misconduct, as well as in the Federal Laboratory Consortium. He is a Certified Licensing Professional and a Past Chair of the Maryland Chapter of the Licensing Executives Society, USA and Canada (LES), and is a member of the Board of Visitors of the College of Computer, Mathematical and Natural Sciences of the University of Maryland, College Park.

Presentations
See attached

View Recording: https://youtu.be/5Ng5FDW1LXk.

Takeaways

ESIP has published guidelines for citing data and for citing software and services. These have been important and influential ESIP products. Now a new cluster is working to address the issues of “research object” citation writ large. The cluster has been working to identify the various types of research objects that could or should be cited such as samples, instruments, annotations, and other artifacts. We have also been examining the various concerns that may be addressed in citing the objects such as access, credit or attribution, and scientific reproducibility. We find that citation of different types of objects may need to address different concerns and that different approaches may be necessary for different concerns and objects. We have, therefore, been working through a matrix that attempts to map all the various objects and citation concerns.

In this working session, we will provide a brief overview of the cluster's work to date on determining when different research objects get IDs. We will then work in small groups to determine when different research objects need to be identified to ensure reproducibility or validity of a result. For this purpose, we define reproducibility as the ability to independently recreate or confirm a result (not the data). A result could be a finding in a scientific paper, a legal brief, a policy recommendation, a model output or derived product — essentially any formal, testable assertion. This is essentially a provenance use case. It is very broad, but distinct from the credit and even the access concerns of citation. This is primarily about unambiguous reference. When does an object become a first-class research object?
To approach the problem, we will break up into 4-5 groups to define and give examples of different clusters of research objects and then work to answer When or under what circumstance is it necessary to identify an object to enable reproducibility. 
Potential groups include:

1. Literature and related objects (not to be discussed)
2. Software and related objects — Dan Katz
3. Data and related objects — Mark Parsons
4. Samples — Sarah Ramdeen
5. Ontologies and vocabularies — Ruth Duerr
6. Complex research objects (esp. but not exclusively learning resources) — Nancy Hoebelheinrich
7. Instruments and facilites — Mike Daniels
8. Organizations 
9. Activities

We encourage everyone  to start drafting definitions and examples in the spreadsheet now: https://docs.google.com/spreadsheets/d/1VEYPLgTsCR_zbMUbThonBrqaYqBiMT4e525NzFi7ql8/edit#gid=1494916301
Our goal is to have a draft recommendation or complete matrix by the end of the meeting as well as potential follow-on activities for the cluster.

How to Prepare for this Session: Participants should be familiar with existing ESIP citation guidelines and have reviewed the minutes of the last several meetings, especially the "Objects and Concerns Matrix". See http://wiki.esipfed.org/index.php/Research_Object_Citation

Presentations

View Recording: https://youtu.be/5MXzBLu7hjg (abbreviated due to breakout group emphasis of session).

Takeaways

• What is a ‘thing’? ‘Research object’ is a defined term in other communities. Our conception is broader. Perhaps we need a new term, but much of the issue is defining when something becomes a ‘thing’ that is named and located.
• The particular citation use case matters a lot. Reproducibility demands different considerations than credit. The cluster will consider more use cases.
• There appears to be classes of things that can be treated similarly, but we haven’t sorted that out yet.
  
  
  

This workshop session will follow up on the OGC Coverage Analytics sprint, focusing specifically on advanced services for spatio-temporal datacubes. In the Earth sciences datacubes are accepted as an enabling paradigm for offering massive spatio-temporal Earth data analysis-ready, more generally: easing access, extraction, analysis, and fusion. Also, datacubes homogenizes APIs across dimensions, allowing unified wrangling of 1-D sensor data, 2-D imagery, 3-D x/y/t image timeseries and x/y/z geophysics voxel data, and 4-D x/y/z/t climate and weather data.
Based on the OGC datacube reference implementation we introduce datacube concepts, state of standardization, and real-life 2D, 3D, and 4D examples utilizing services from three continents. Ample time will be available for discussion, and Internet-connected participants will be able to replay and modify many of the examples shown. Further, key datacube activities worldwide, within and beyond Earth sciences, will be related to.
Session outcomes could take a number of forms: ideas and issues for OGC, ISO, or ESIP to consider; example use cases; challenges not yet addressed sufficiently, and entirely novel use cases; work and collaboration plans for future ESIP work. Outcomes of the session will be reported at the next OGC TC meeting's Big Data and Coverage sessions. How to Prepare for this Session: Introductory and advanced material is available from http://myogc.org/go/coveragesDWG

Presentations
https://doi.org/10.6084/m9.figshare.11562552.v1

View Recording: https://youtu.be/82WG7soc5bk

Takeaways

• Abstract coverage construct defines the base which can be filled up with a coverage implementation schema. Important as previously implementation wasn’t interoperable with different servers and clients. 
• Have embedded the coordinate system retrieved from sensors reporting in real time into their xml schema to be able to integrate the sensor data into the broader system. Can deliver the data in addition to GML but JSON, and RDF which could be used to link into semantic web tech. 
• Principle is send HTTP url-encoded query to server and get some results that are extracted from datacube, e.g., sources from many hyperspectral images.