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Tuesday, January 7
 

2:00pm EST

ESIP Geoscience Community Ontology Engineering Workshop (GCOEW)
"Brains! Brains! Give us your brains!""
- Friendly neighbourhood machine minds
The collective knowledge in the ESIP community is immense and invaluable. During this session, we'd like to make sure that this knowledge drives the semantic technology (ontologies) being developed to move data with machine-readable knowledge in Earth and planetary science.
What we'll do:

In the first half hour of this session, we'll a) sketch out how and why we build ontologies and b) show you how to request that your knowledge gets added to ontologies (with nanocrediting).
We'll then have a 30-minute crowdsourcing jam session, during which participants can share their geoscience knowledge on the SWEET issue tracker. With a simple post, you can shape how the semantic layer will behave, making sure it does your field justice! Request content and share knowledge here: https://github.com/ESIPFed/sweet/issues
In the last, 30 minutes we'll take one request and demonstrate how we go about ""ontologising"" it in ENVO and how we link that to SWEET to create interoperable ontologies across the Earth and life sciences.

Come join us and help us shape the future of Geo-semantics!

Stuff you'll need:
A GitHub account available at https://github.com/
An ORCID (for nanocrediting your contributions) available at https://orcid.org How to Prepare for this Session:

Presentations:

View Recording:
https://youtu.be/tr0coi5ZQvM

Takeaways
  • Working toward a future (5-10 year goal) of making an open Earth & Space Science Foundry (from SWEET) similar to the OBO (Open Biological and Biomedical Ontology) Foundry. “Humans write queries”. Class definitions need to be machine-readable for interoperability, but must remain human-readable for authoring queries, ontology reuse, etc.
  • Please feel free to add phenomena of interest to the SWEET https://github.com/ESIPFed/sweet/issues/ or ENVO https://github.com/EnvironmentOntology/envo/issues/ issue trackers. 
  • At AGU they added a convention for changes to ontologies. Class level annotation convention. Can get now get textual defs from DBpedia for SWEET terms. See https://github.com/ESIPFed/sweet/wiki/SWEET-Class-Annotation-Convention


Speakers
avatar for Lewis J. McGibbney

Lewis J. McGibbney

Chair, ESIP Semantic Technologies Committee, NASA, JPL
My name is Lewis John McGibbney, I am currently a Data Scientist at the NASA Jet Propulsion Laboratory in Pasadena, California where I work in Computer Science and Data Intensive Applications. I enjoy floating up and down the tide of technologies @ The Apache Software Foundation having... Read More →


Tuesday January 7, 2020 2:00pm - 3:30pm EST
Glen Echo
  Glen Echo, Working Session

2:00pm EST

Data Skills & Competencies Requirements for Data Stewards: Views from the ESIP Community & Beyond
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



Speakers
avatar for Karl Benedict

Karl Benedict

Director of Research Data Services & Information Technology, University of New Mexico
Since 1986 I have had parallel careers in Information Technology, Data Management and Analysis, and Archaeology. Since 1993 when I arrived at UNM I have worked as a Graduate Student in Anthropology, Research Scientist, Research Faculty, Applied Research Center Director, and currently... Read More →


Tuesday January 7, 2020 2:00pm - 3:30pm EST
Linden Oak
  Linden Oak, Breakout

2:00pm EST

COPDESS: Facilitating a Fair Publishing Workflow Ecosystem
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



Speakers
avatar for Karl Benedict

Karl Benedict

Director of Research Data Services & Information Technology, University of New Mexico
Since 1986 I have had parallel careers in Information Technology, Data Management and Analysis, and Archaeology. Since 1993 when I arrived at UNM I have worked as a Graduate Student in Anthropology, Research Scientist, Research Faculty, Applied Research Center Director, and currently... Read More →
avatar for Kerstin Lehnert

Kerstin Lehnert

President, IGSN e.V.
Kerstin Lehnert is Senior Research Scientist at the Lamont-Doherty Earth Observatory of Columbia University and Director of EarthChem, the System for Earth Sample Registration, and the Astromaterials Data System. Kerstin holds a Ph.D in Petrology from the University of Freiburg in... Read More →
avatar for Lesley Wyborn

Lesley Wyborn

Adjunct Fellow, Australian National University


Tuesday January 7, 2020 2:00pm - 3:30pm EST
Salon A-C
  Salon A-C, Breakout

4:00pm EST

Bringing Science Data Uncertainty Down to Earth - Sub-orbital, In Situ, and Beyond
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: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

Speakers
avatar for David Moroni

David Moroni

Data Stewardship and User Services Team Lead, Jet Propulsion Laboratory, Physical Oceanography Distributed Active Archive Center
I am a Senior Science Data Systems Engineer at the Jet Propulsion Laboratory and Data Stewardship and User Services Team Lead for the PO.DAAC Project, which provides users with data stewardship services including discovery, access, sub-setting, visualization, extraction, documentation... Read More →
avatar for Ge Peng

Ge Peng

Research Scholar, CISESS/NCEI
Dataset-centric scientific data stewardship, data quality management
FB

Fred Bingham

University of North Carolina at Wilmington
MK

Mikael Kuusela

Carnegie Mellon University


Tuesday January 7, 2020 4:00pm - 5:30pm EST
Forest Glen

4:00pm EST

Defining the Bull's Eye of Sample Metadata
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

Speakers
avatar for Lesley Wyborn

Lesley Wyborn

Adjunct Fellow, Australian National University
avatar for Kerstin Lehnert

Kerstin Lehnert

President, IGSN e.V.
Kerstin Lehnert is Senior Research Scientist at the Lamont-Doherty Earth Observatory of Columbia University and Director of EarthChem, the System for Earth Sample Registration, and the Astromaterials Data System. Kerstin holds a Ph.D in Petrology from the University of Freiburg in... Read More →


Tuesday January 7, 2020 4:00pm - 5:30pm EST
Linden Oak
  Linden Oak, Working Session
 
Wednesday, January 8
 

2:00pm EST

Citizen Science Data and Information Quality
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.

Speakers
avatar for David Moroni

David Moroni

Data Stewardship and User Services Team Lead, Jet Propulsion Laboratory, Physical Oceanography Distributed Active Archive Center
I am a Senior Science Data Systems Engineer at the Jet Propulsion Laboratory and Data Stewardship and User Services Team Lead for the PO.DAAC Project, which provides users with data stewardship services including discovery, access, sub-setting, visualization, extraction, documentation... Read More →
avatar for Ge Peng

Ge Peng

Research Scholar, CISESS/NCEI
Dataset-centric scientific data stewardship, data quality management
avatar for Yaxing Wei

Yaxing Wei

Scientist, Oak Ridge National Laboratory


Wednesday January 8, 2020 2:00pm - 3:30pm EST
Linden Oak
  Linden Oak, Breakout

2:00pm EST

Advancing Data Integration approaches of the structured data web
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

Speakers
avatar for Jonathan Yu

Jonathan Yu

Research data scientist/architect, CSIRO
Jonathan is a data scientist/architect with the Environmental Informatics group in CSIRO. He has expertise in information and web architectures, data integration (particularly Linked Data), data analytics and visualisation. Dr Yu is currently the technical lead for the Loc-I project... Read More →
avatar for Dalia Varanka

Dalia Varanka

Research Physical Scientist, U.S. Geological Survey
Principle Investigator and Project Lead, The Map as Knowledge Base
AR

Alastair Richie

Landcare Research NZ
AL

Adam Leadbetter

Marine Institute
RT

Rob Thomas

Marine Institute
BB

Boyan Brodaric

Natural Resources Canada
EB

Eric Boisvert

Natural Resources Canada
avatar for Irina  Bastrakova

Irina Bastrakova

Director, Spatial Data Architecture, Geoscience Australia
I have been actively involved with international and national geoinformatics communities for more than 19 years. I am the Chair of the Australian and New Zealand Metadata Working Group. My particular interest is in developing and practical application of geoscientific and geospatial... Read More →
avatar for David Blodgett

David Blodgett

U.S. Geological Survey


Wednesday January 8, 2020 2:00pm - 3:30pm EST
White Flint

4:00pm EST

Developing, Using and Testing Tools to Assess Learning Resources from two Perspectives: the Teacher and the Learner
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


Speakers
avatar for Karl Benedict

Karl Benedict

Director of Research Data Services & Information Technology, University of New Mexico
Since 1986 I have had parallel careers in Information Technology, Data Management and Analysis, and Archaeology. Since 1993 when I arrived at UNM I have worked as a Graduate Student in Anthropology, Research Scientist, Research Faculty, Applied Research Center Director, and currently... Read More →


Wednesday January 8, 2020 4:00pm - 5:30pm EST
Glen Echo
  Glen Echo, Working Session