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Record W2904673858 · doi:10.29173/iq944

Digital curation after digital extraction for data sharing

2018· article· en· W2904673858 on OpenAlexaboutno aff
Karsten Boye Rasmussen

Bibliographic record

VenueIASSIST Quarterly · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsnot available
Fundersnot available
KeywordsPreparednessDigital curationData curationDigital preservationLibrary scienceService (business)Digital libraryWorld Wide WebPolitical sciencePublic relationsBusinessComputer science

Abstract

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Welcome to the third issue of volume 42 of the IASSIST Quarterly (IQ 42:3, 2018). The IASSIST Quarterly presents in this issue three papers from geographically widespread countries. We call IASSIST ‘International’, so I am happy to present papers from three continents in this issue with papers from Zimbabwe, Italy and Canada. The paper 'The State of Preparedness for Digital Curation and Preservation: A Case Study of a Developing Country Academic Library' is by Phillip Ndhlovu, who works as the institutional repository librarian and liaison librarian, and Thomas Matingwina, who is a lecturer at the Department of Library and Information Service at the National University of Science and Technology (NUST) in Bulawayo, Zimbabwe. Modern day libraries have vast amounts of digital content and the authors noted that because these collections require very different management than the traditional paper-based materials, the new materials’ longevity is endangered. Their study assessed the state of preparedness of the NUST Library for digital curation and preservation, including the assessment of awareness, competencies, technology infrastructure, digital disaster preparedness, and challenges to digital curation and preservation. They found a lack of policies, lack of expertise by library staff, and lack of funding. You might conclude that investigating your own organization and reaching the very well known conclusion that 'we need more money!' is not so surprising. However, you have to take note that the Jeff Rothenberg statement from 1995 that 'Digital information lasts forever – or five years, whichever comes first' has not yet sunk in with politicians and administrators, who will immediately associate the term 'digital' with 'saving money'. This study shows them why this is not a valid connotation. It is a study of a single institution, and as the authors note it cannot be generalized even to other academic libraries in Zimbabwe. However, other libraries - also outside Zimbabwe - have here a good guide for making their own assessment of the digital preparedness of their institution. The second paper was - as was the paper above - presented at the IASSIST conference in 2018 and is also about the transition from media known for thousands of years to new media and digital forms. Peter Peller presented the paper 'From Paper Map to Geospatial Vector Layer: Demystifying the Process'. He is the Director of the Spatial and Numeric Data Services unit at Libraries and Cultural Resources at the University of Calgary in Canada. The conversion of raster images of maps to vector data is analogous to OCR technologies extracting words from scanned print documents. Thereby the map information becomes more accessible, and usable in geographic information systems (GIS). An illustrative example is that historical geospatial information can be overlaid in Google Earth. The description of the entire process incorporates examples of the various techniques, including different types of editing. Furthermore, descriptions of the software used in selected studies are listed in the appendix. It is mentioned that 'paper texture and ink spread' can be responsible for introducing noise and errors, so remember to keep the old maps. This is because what is considered noise in one context might become the subject for interesting future research. In addition the software for extracting information will most certainly improve. For once both the author and we at IASSIST Quarterly have been quite fast. The data for the third paper was collected in late 2017 and the results are presented here only a year later. In October 2017 a message appeared on the IASSIST mail list with the start of the sentence 'I would share the data but...' It quickly generated many ways of completing that sentence. Flavio Bonifacio - who works at Metis Ricerche srl in Torino, Italy - quickly launched a questionnaire sent to members of the mail list and to others from similar communities of interested individuals. The questionnaire was an extension of an earlier one concerning scientists' reuse and sharing of data. The paper includes many tabulations and models showing the background as well as the data sharing attitudes found in the survey. A respondent typology is developed based upon the level of propensity for sharing data and the level of experiencing problems in data sharing into a 2-by-2 table consisting of 'irreducible reluctant', 'reducible reluctant', 'problematic follower', and 'premium follower'. In the Nordic countries we tend to have the impression that certain services are publicly available and for free. This impression is plainly superficial because we Nordic people also know very well that 'there is no such thing as a free lunch'! All services must be paid for in one way or another. If you have many services that carry no direct cost, it is probably because you - and others - paid for them beforehand through taxation. Because of cuts in the public economy one of the things Flavio Bonifacio wanted to investigate was the question 'Is there a market for selling data-sharing services?' The results imply that 'reducible reluctants' can be a target for services that reduce the problems of that group. Submissions of papers for the IASSIST Quarterly are always very welcome. We welcome input from IASSIST conferences or other conferences and workshops, from local presentations or papers especially written for the IQ. When you are preparing such a presentation, give a thought to turning your one-time presentation into a lasting contribution. Doing that after the event also gives you the opportunity of improving your work after feedback. We encourage you to login or create an author login to https://www.iassistquarterly.com (our Open Journal System application). We permit authors 'deep links' into the IQ as well as deposition of the paper in your local repository. Chairing a conference session with the purpose of aggregating and integrating papers for a special issue IQ is also much appreciated as the information reaches many more people than the limited number of session participants and will be readily available on the IASSIST Quarterly website at https://www.iassistquarterly.com. Authors are very welcome to take a look at the instructions and layout: https://www.iassistquarterly.com/index.php/iassist/about/submissions Authors can also contact me directly via e-mail: kbr@sam.sdu.dk. Should you be interested in compiling a special issue for the IQ as guest editor(s) I will also be delighted to hear from you. Karsten Boye Rasmussen - November 2018

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score0.960

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.045
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0070.008
Science and technology studies0.0060.003
Scholarly communication0.0120.018
Open science0.0030.014
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.2870.167

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 head0.066
GPT teacher head0.269
Teacher spread0.203 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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