Difference between revisions of "Pre-ESIP Workshop"
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<big><center> '''Developing Community Guidelines for Consistently Curating '''</center></big> | <big><center> '''Developing Community Guidelines for Consistently Curating '''</center></big> | ||
<big><center> '''and Representing Dataset Quality Information'''</center></big> | <big><center> '''and Representing Dataset Quality Information'''</center></big> | ||
<center>Virtual, 13 July 2020</center> | <center>Virtual, 13 July 2020</center> | ||
| − | <center>POCs: Ge Peng, ESIP IQC and CISESS/NCSU; gpeng@ncsu.edu | + | <center>'''POCs:" Ge Peng, ESIP IQC and CISESS/NCSU; gpeng@ncsu.edu</center> |
<center> Carlo Lacagnina, BSC; carlo.lacagnina@bsc.es </center> | <center> Carlo Lacagnina, BSC; carlo.lacagnina@bsc.es </center> | ||
<center>'''Organizing Committee:''' Ge Peng, Carlo Lacagnina, Robert Downs, Ivana Ivánová, </center> | <center>'''Organizing Committee:''' Ge Peng, Carlo Lacagnina, Robert Downs, Ivana Ivánová, </center> | ||
| − | <center>Gilles Larnicol, David Moroni, Hampapuram “Rama” Ramapriyan, and Yaxing Wei | + | <center>Gilles Larnicol, David Moroni, Hampapuram “Rama” Ramapriyan, and Yaxing Wei</center> |
| − | </center> | ||
| − | '''About''' | + | '''About''' <br> |
Curating standards-based quality metadata and consistent quality descriptive information at the dataset level is fundamental for helping establish the credibility and trustworthiness of individual datasets and for providing sufficient guidance for users to address their specific needs, including machine learning. However, there are currently no community standards or guidelines on how to consistently curate and represent the dataset quality information that is machine- and human-readable. <br> | Curating standards-based quality metadata and consistent quality descriptive information at the dataset level is fundamental for helping establish the credibility and trustworthiness of individual datasets and for providing sufficient guidance for users to address their specific needs, including machine learning. However, there are currently no community standards or guidelines on how to consistently curate and represent the dataset quality information that is machine- and human-readable. <br> | ||
ESIP Information Quality Cluster (IQC) and the Evaluation and Quality Control (EQC) Team of Barcelona Supercomputing Center (BSC) have co-organized a pre-ESIP workshop to bring together national and international subject matter experts on dataset quality to kick off the development of community guidelines for consistently curating and representing dataset quality information that is findable, accessible, interoperable, and reusable, aka, FAIR. <br><br> | ESIP Information Quality Cluster (IQC) and the Evaluation and Quality Control (EQC) Team of Barcelona Supercomputing Center (BSC) have co-organized a pre-ESIP workshop to bring together national and international subject matter experts on dataset quality to kick off the development of community guidelines for consistently curating and representing dataset quality information that is findable, accessible, interoperable, and reusable, aka, FAIR. <br><br> | ||
Revision as of 09:17, June 4, 2020
About
Curating standards-based quality metadata and consistent quality descriptive information at the dataset level is fundamental for helping establish the credibility and trustworthiness of individual datasets and for providing sufficient guidance for users to address their specific needs, including machine learning. However, there are currently no community standards or guidelines on how to consistently curate and represent the dataset quality information that is machine- and human-readable.
ESIP Information Quality Cluster (IQC) and the Evaluation and Quality Control (EQC) Team of Barcelona Supercomputing Center (BSC) have co-organized a pre-ESIP workshop to bring together national and international subject matter experts on dataset quality to kick off the development of community guidelines for consistently curating and representing dataset quality information that is findable, accessible, interoperable, and reusable, aka, FAIR.
