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Record W2097961224 · doi:10.5194/essd-7-239-2015

The IPY 2007–2008 data legacy – creating open data from IPY publications

2015· article· en· W2097961224 on OpenAlexaboutno aff
Amelie Driemel, Hannes Grobe, Michael Diepenbroek, Herbert Grüttemeier, Stefanie Schumacher, Rainer Sieger

Bibliographic record

VenueEarth system science data · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsMetadataUploadVisibilityTundraBiomass (ecology)Environmental scienceMeteorologyArcticComputer scienceOceanographyGeographyGeologyWorld Wide Web

Abstract

fetched live from OpenAlex

Abstract. The International Polar Year (IPY) 2007–2008 was a synchronized effort to simultaneously collect data from polar regions. Being the fourth in a series of IPYs, the demand for interdisciplinarity and new data products was high. However, despite all the research done on land, people, ocean, ice and atmosphere and the large amount of data collected, no central archive or portal was created for IPY data. In order to improve the availability and visibility of IPY data, a concerted effort between PANGAEA – Data Publisher for Earth and Environmental Science, the International Council for Science (ICSU) World Data System (WDS), and the International Council for Scientific and Technical Information (ICSTI) was undertaken to extract data resulting from IPY publications for long-term preservation. Overall, 1380 IPY-related references were collected. Of these, only 450 contained accessible data. All data were extracted, quality checked, annotated with metadata and uploaded to PANGAEA. The 450 articles dealt with a multitude of IPY topics – plankton biomass, water chemistry, ice thickness, whale sightings, Inuit health, alien species introductions by travellers or tundra biomass change, to mention just a few. Both the Arctic and the Antarctic were investigated in the articles, and all realms (land, people, ocean, ice and atmosphere) and a wide range of countries were covered. The data compilation can now be found with the identifier doi:10.1594/PANGAEA.150150, and individual parts can be searched using the PANGAEA search engine (www.pangaea.de) and adding "+project:ipy". With this effort, we hope to improve the visibility, accessibility and long-term storage of IPY data for future research and new data products.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesScholarly communication, Open science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.776
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0040.015
Open science0.0350.008
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.176
GPT teacher head0.326
Teacher spread0.150 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2015
Admission routes1
Has abstractyes

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