Data Management in the Long Tail: Science, Software, and Service

Abstract

Scientists in all fields face challenges in managing and sustaining access to their research data. The larger and longer term the research project, the more likely that scientists are to have resources and dedicated staff to manage their technology and data, leaving those scientists whose work is based on smaller and shorter term projects at a disadvantage. The volume and variety of data to be managed varies by many factors, only two of which are the number of collaborators and length of the project. As part of an NSF project to conceptualize the Institute for Empowering Long Tail Research, we explored opportunities offered by Software as a Service (SaaS). These cloud-based services are popular in business because they reduce costs and labor for technology management, and are gaining ground in scientific environments for similar reasons. We studied three settings where scientists conduct research in small and medium-sized laboratories. Two were NSF Science and Technology Centers (CENS and C-DEBI) and the third was a workshop of natural reserve scientists and managers. These laboratories have highly diverse data and practices, make minimal use of standards for data or metadata, and lack resources for data management or sustaining access to their data, despite recognizing the need. We found that SaaS could address technical needs for basic document creation, analysis, and storage, but did not support the diverse and rapidly changing needs for sophisticated domain-specific tools and services. These are much more challenging knowledge infrastructure requirements that require long-term investments by multiple stakeholders.

Create synergies and inspire collaborations around the development of intelligent infrastructure for human‐centered communities

“…To address the above questions, this study employed qualitative research methods including ethnographic participant observation, document analysis, and semistructured interviews. Qualitative methods have been broadly and effectively deployed to study scientific work and data practices (for examples, Borgman et al, ; Hine, ). The mixed‐methods approach adopted here is especially useful for capturing typical scenarios, emergent issues, practical examples, and the significant experience of human subjects surrounding their interactions with built environments and smart technologies.…”

Tantangan Mekanisme Tata Kelola Data Dalam Mewujudkan Aplikasi Bersatu Lawan Covid (Blc)

“…Kedua, mekanisme tata kelola prosedural bertujuan untuk memastikan bahwa data dicatat secara akurat, disimpan dengan aman, digunakan secara efektif, dan dibagikan secara tepat (Borgman et al, 2016). Mekanisme prosedural terdiri dari (i) strategi data; (ii) kebijakan; (iii) standar; (iv) proses; (v) prosedur; (vi) perjanjian kontrak; (vii) pengukuran kinerja; (viii) pemantauan kepatuhan; dan (ix) manajemen masalah.…”

“…Ketiga, mekanisme tata kelola relasional memfasilitasi kolaborasi antara pemangku kepentingan (Borgman et al, 2016). Mekanisme tata kelola relasional meliputi (i) komunikasi; (ii) pelatihan; dan (iii) koordinasi pengambilan keputusan.…”

A Trust Framework for Online Research Data Services

“…In a recent publication, Borgman et al [38] have described the outcomes from a project which explored the ability of the so-called "long tail" of researchers, as exemplified by small and medium sized laboratories (SMLs), to manage their data. The results highlight the challenges of this target population in addressing the types of criteria mentioned by Demchenko and Wallis.…”