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Linden Oak [clear filter]
Tuesday, January 7

11:00am EST

Creating a Data at Risk Commons at DataAtRisk.org
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

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

avatar for Denise Hills

Denise Hills

Director, Energy Investigations, Geological Survey of Alabama
Long tail data, data preservation, connecting physical samples to digital information, geoscience policy, science communication.ORCID:  0000-0001-9581-4944

Tuesday January 7, 2020 11:00am - 12:30pm EST
Linden Oak
  Linden Oak, Working Session

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


avatar for Lesley Wyborn

Lesley Wyborn

Honorary Professor, Australian National University
avatar for Kerstin Lehnert

Kerstin Lehnert

President, IGSN e.V.
Kerstin Lehnert is Doherty Senior Research Scientist at the Lamont-Doherty Earth Observatory of Columbia University and Director of the Interdisciplinary Earth Data Alliance that operates EarthChem, the System for Earth Sample Registration, and the Astromaterials Data System. Kerstin... Read More →

Sarah Ramdeen

Data Curator, Columbia University

Tuesday January 7, 2020 4:00pm - 5:30pm EST
Linden Oak
  Linden Oak, Working Session
Thursday, January 9

12:00pm EST

Research Object Citation Cluster Working Session
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


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

  • 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.

avatar for Jessica Hausman

Jessica Hausman

NASA HQ / ASRC Federal
avatar for Mark Parsons

Mark Parsons

Editor in Chief, Data Science Journal

Thursday January 9, 2020 12:00pm - 1:30pm EST
Linden Oak
  Linden Oak, Working Session