{ "types" : { "Bookmark" : { "pluralLabel" : "Bookmarks" }, "Publication" : { "pluralLabel" : "Publications" }, "GoldStandardPublication" : { "pluralLabel" : "GoldStandardPublications" }, "GoldStandardBookmark" : { "pluralLabel" : "GoldStandardBookmarks" }, "Tag" : { "pluralLabel" : "Tags" }, "User" : { "pluralLabel" : "Users" }, "Group" : { "pluralLabel" : "Groups" }, "Sphere" : { "pluralLabel" : "Spheres" } }, "properties" : { "count" : { "valueType" : "number" }, "date" : { "valueType" : "date" }, "changeDate" : { "valueType" : "date" }, "url" : { "valueType" : "url" }, "id" : { "valueType" : "url" }, "tags" : { "valueType" : "item" }, "user" : { "valueType" : "item" } }, "items" : [ { "type" : "Bookmark", "id" : "https://puma.ub.uni-stuttgart.de/url/e546bfd1de4fe9b39d88d13584899a9e/diglezakis", "tags" : [ "evaluation","forschungsdaten","framework","infrastruktur" ], "intraHash" : "e546bfd1de4fe9b39d88d13584899a9e", "label" : "Using RISE, the Research Infrastructure Self Evaluation Framework | Digital Curation Centre", "user" : "diglezakis", "description" : "", "date" : "2018-06-19 10:33:54", "changeDate" : "2018-06-19 08:33:54", "count" : 1, "url" : "http://www.dcc.ac.uk/resources/how-guides/RISE" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/263bc735a5e4636f62c50ead44a3525eb/diglezakis", "tags" : [ "forschungsdaten","framework","health","quality" ], "intraHash" : "63bc735a5e4636f62c50ead44a3525eb", "interHash" : "591e500ac426b3bbd6c03f77f5e6476f", "label" : "Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R", "user" : "diglezakis", "description" : "", "date" : "2024-06-18 16:31:36", "changeDate" : "2024-06-18 16:31:36", "count" : 1, "pub-type": "article", "journal": "BMC Medical Research Methodology", "year": "2021", "url": "https://doi.org/10.1186/s12874-021-01252-7", "author": [ "Carsten Oliver Schmidt","Stephan Struckmann","Cornelia Enzenbach","Achim Reineke","Jürgen Stausberg","Stefan Damerow","Marianne Huebner","Börge Schmidt","Willi Sauerbrei","Adrian Richter" ], "authors": [ {"first" : "Carsten Oliver", "last" : "Schmidt"}, {"first" : "Stephan", "last" : "Struckmann"}, {"first" : "Cornelia", "last" : "Enzenbach"}, {"first" : "Achim", "last" : "Reineke"}, {"first" : "Jürgen", "last" : "Stausberg"}, {"first" : "Stefan", "last" : "Damerow"}, {"first" : "Marianne", "last" : "Huebner"}, {"first" : "Börge", "last" : "Schmidt"}, {"first" : "Willi", "last" : "Sauerbrei"}, {"first" : "Adrian", "last" : "Richter"} ], "volume": "21","number": "1","pages": "63--","abstract": "No standards exist for the handling and reporting of data quality in health research. This work introduces a data quality framework for observational health research data collections with supporting software implementations to facilitate harmonized data quality assessments.", "issn" : "14712288", "refid" : "Schmidt2021", "doi" : "10.1186/s12874-021-01252-7", "bibtexKey": "schmidt2021facilitating" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2c7971d8d6a474e4c5bf8a30d62d04ede/diglezakis", "tags" : [ "forschungsdaten","framework","hpc-computing","metadata","repository","software","softwareLizenzMotivation","reproducibility" ], "intraHash" : "c7971d8d6a474e4c5bf8a30d62d04ede", "interHash" : "8aa55fa398b71ec36da3eb98d8fff42e", "label" : "AiiDA: automated interactive infrastructure and database for computational science", "user" : "diglezakis", "description" : "", "date" : "2017-09-12 15:52:49", "changeDate" : "2017-09-12 14:22:14", "count" : 2, "pub-type": "article", "journal": "Computational Materials Science", "year": "2016", "url": "http://www.sciencedirect.com/science/article/pii/S0927025615005820", "author": [ "Giovanni Pizzi","Andrea Cepellotti","Riccardo Sabatini","Nicola Marzari","Boris Kozinsky" ], "authors": [ {"first" : "Giovanni", "last" : "Pizzi"}, {"first" : "Andrea", "last" : "Cepellotti"}, {"first" : "Riccardo", "last" : "Sabatini"}, {"first" : "Nicola", "last" : "Marzari"}, {"first" : "Boris", "last" : "Kozinsky"} ], "volume": "111","pages": "218 - 230","abstract": "Abstract Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA\u2019s design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA\u2019s design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows.", "issn" : "0927-0256", "doi" : "https://doi.org/10.1016/j.commatsci.2015.09.013", "bibtexKey": "Pizzi2016218" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2645c97554bcfc72bac8aacda74218dd3/diglezakis", "tags" : [ "engineering","forschungsdaten","framework","largeData" ], "intraHash" : "645c97554bcfc72bac8aacda74218dd3", "interHash" : "6a797a7ed8f41a24ca0dfcffa047aa53", "label" : "signac - A Simple Data Management Framework", "user" : "diglezakis", "description" : "[1611.03543] signac - A Simple Data Management Framework", "date" : "2017-09-05 11:25:43", "changeDate" : "2017-09-05 09:25:43", "count" : 2, "pub-type": "misc", "year": "2016", "url": "http://arxiv.org/abs/1611.03543", "author": [ "Carl S. Adorf","Paul M. Dodd","Sharon C. Glotzer" ], "authors": [ {"first" : "Carl S.", "last" : "Adorf"}, {"first" : "Paul M.", "last" : "Dodd"}, {"first" : "Sharon C.", "last" : "Glotzer"} ], "note": "cite arxiv:1611.03543Comment: 14 pages, 6 figures","abstract": "Researchers in the field of computational physics, chemistry, and materials\r\nscience are regularly posed with the challenge of managing large and\r\nheterogeneous data spaces. The amount of data increases in lockstep with\r\ncomputational efficiency multiplied by the amount of available computational\r\nresources, which shifts the bottleneck within the scientific process from data\r\nacquisition to data post-processing and analysis. We present a framework\r\ndesigned to aid in the integration of various specialized data formats, tools\r\nand workflows. The signac framework provides all basic components required to\r\ncreate a well-defined and thus collectively accessible data space, simplifying\r\ndata access and modification through a homogeneous data interface, largely\r\nagnostic of the data source, i.e., computation or experiment. The framework's\r\ndata model is designed not to require absolute commitment to the presented\r\nimplementation, simplifying adaption into existing data sets and workflows.\r\nThis approach not only increases the efficiency with which scientific results\r\ncan be produced, but also significantly lowers barriers for collaborations\r\nrequiring shared data access.", "bibtexKey": "adorf2016signac" } ] }