DataStudies Newsletters

Research Presentations

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Open Data

All interviewees in our projects have been given the option to make their transcripts available as open data (see consent form). These data will be published under the links below.

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  • pdf Meyer, D. (2018) Case studies on Open Science in the context of ERC projects - Set 1. The first of five Sets, this one considers the DATA_SCIENCE project as one of three case studies in their ‘Study on open access to publications and research data management and sharing within ERC projects’(study procured by the ERC Executive Agency under contract number ERCEA/A1/2016/06).
  • pdf Public Policy & Management Institute (PPMI), Digital Curation Centre (DCC) & University of Göttingen (forthcoming, 2017) Case Study: Epistemology of Data-Intensive Science. A study on open access to publications and research data management and sharing within ERC projects. (Research paper commissioned by the ERCEA taking the DATA_SCIENCE project as case study for best practice.)
  • pdf Tempini, N. and Leonelli, S. (2015) Workshop Report “What Is Data-Intensive Science?”.
  • pdf Levin, N. and Leonelli, S. (2014) Workshop Report “The Value of Open Science”. EASST Review 33(1): 15–17.
  • Bezuidenhout, L. and Donaghy, J. (2012) Workshop Report “Making Data Accessible to All”. EASST Review 31(3).
  • Leoneli, S. and Bastow, R. (2012) Report for GARNet/BBSRC: “Making Data Accessible to All”.

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Policy Documents

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  • Gazetta di Modena profile of Sabina, on the occasion of winning the Talented Young Italians prize (category: Research and Innovation) awarded by the Italian Chamber of Commerce in the UK.
  • Harvard Bill of Health blog symposium: “What is Citizen Science anyway?” (introduction plus articles by the presenters of the Citizen Science conference, including Sabina, May 2017)
  • OZY magazine: “Doctors Swear to ‘Do No Harm’. Why Don’t Data Scientists?” (Article by Tom Cassauwers with some quotes from Sabina, 13 October 2017)
  • LSE Impact Blog: “To what are we opening science? Reform of the publishing system is only a step in a much broader re-evaluation” (opinion by Sabina Leonelli and Barbara Prainsack, April 2015)
  • LSE Impact Blog: “What constitutes trustworthy data changes across time and space?” (interview with Sabina Leonelli, January 2015)
  • Mendelspod podcast: “Myths of Big Data” (interview with Sabina Leonelli, March 2014)
  • The Reasoner article: “Philosophy of Scientific Practice and Information” (interview with Sabina Leonelli, September 2013)
  • Bulletin of STS podcast: “Why the Current Insistence on Open Access to Scientific Data? Big Data, Knowledge Production, and the Political Economy of Contemporary Biology” (interview with Sabina Leonelli, September 2013)

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Data Imagery

  • XKCD on data privacy
  • Brian Moore with various Data cartoons, from an IT/corporate angle

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Creative Commons Attribution-only (CC BY) illustrations

The following illustrations were made for the DataStudies project by Michel Durinx, and released under the ‘Attribution only’ Creative Commons licence, CC BY 4.0.

  1. Pixel-based image format (.jpg) allows for simple unaltered reuse, where vector-based versions (.pptx) allow for easy alterations (font, font-size, label translation, colour scheme, ... ).
  2. The chosen formats do not imply an endorsement of those formats, and sometimes aren't correct from an idealistic FOSS point of view. Specifically, for vector graphics the .pptx format was chosen as the best available real-world option, since practically everybody planning the file's reuse is familiar with its editing software and has that installed already (Microsoft PowerPoint, Apple Keynote, LibreOffice Draw, ... ); FOSS alternatives like Inkscape or SVG-Edit would impede re-use for most users by demanding installation of and familiarization with new software.

One of the underlying aims is to let users easily blend the illustrations with their own illustrations and content, for example by re-arranging items to fit a (landscape-oriented) presentation or (portrait-oriented) article, or harmonizing font(size)s, colours/gradients, and line-styles.

  • Cell cycle: .jpg image (2497 x 1904 pixels, 350KB) or .pptx file (100KB).
  • DNA microarray: .jpg image (3767 x 1800 pixels, 1MB). Note the microarray image is in the public domain, courtesy of the NIH, while the rest are trivial additions (including the approximate colour chart).
  • Gene Ontology (visualisation of part of the GO, based on screenshot taken in Dec 2010): .jpg image approximating website (2450 x 2250 pixels, 250KB) or .jpg image with white background (2200 x 2200 pixels, 350KB); or .pptx with comparison to original, or .pdf with white background.

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