D5.10 White Paper on Remote Access to Sensitive Data in the Social Sciences and Humanities: 2021 and beyond
Notice bibliographique
Résumé
This white paper provides the necessary basis for understanding the requirements and specifications for remote access to sensitive data (data with potentially harmful effects in the event of their disclosure) in the social sciences and the humanities (SSH). It is result of the work implemented in SSHOC Task 5.4 Remote Access to Sensitive Data. It is intended to provide guidance and recommendations to the EOSC stakeholders for future infrastructure investment for remote access to sensitive data in the SSH. To ensure that this guidance can, in fact, be implemented, the recommendations are based on the knowledge of numerous data professionals who have direct experience planning, implementing, managing, and sustaining diverse forms of remote access and secure facilities. In doing so, our goal has been to maintain the vision of expanding such infrastructure, while remaining grounded in the practicalities of operating such facilities in a sustainable manner. In this domain, it is now recognized that the ideal of “open data” needs to be balanced with privacy and other factors that can require moderating access to sensitive data, as reflected in the EU Commission’s (2016) stance of “as open as possible, as closed as necessary.” Developments in the past five years have advanced data access, primarily through “safe enclaves”, i.e., physical rooms that provide security for data access (see Glossary). This represents a major improvement for data accessibility, but international, comparative, efficient research requires augmenting the research infrastructure by enabling remote access to data from a researcher’s desktop. Solutions have operated for several years (e.g., UK Data Archive Secure Lab, ICPSR Virtual Data Enclave), but most of these still face limitations on the scope of data available, geographic limitations, etc. More recently, new infrastructures are being developed, some spanning several countries. These efforts are commendable and represent major improvements. However, limited resources, and complex legal variations (national implementations of GDPR), as well as other factors, have prevented implementation of a broader solution. As countries across Europe look at the emerging multi-national infrastructures, it is crucial to address the need for a European answer, at scale, with sustainable funding. The recommendations offered here are guided by our observations that most successful infrastructures embody two features: 1) they are human as well as technical, and 2) they are neither purely centralised nor decentralised, but well-crafted hybrids.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,000 |
| Communication savante | 0,012 | 0,010 |
| Science ouverte | 0,006 | 0,021 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».