A Brief Introduction to the Data Curation Profiles

Total Page:16

File Type:pdf, Size:1020Kb

A Brief Introduction to the Data Curation Profiles Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success A Brief Introduction to the Data Curation Profiles Jake Carlson Data Services Specialist Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success Setting the stage Since 2004: Purdue Interdisciplinary Research Initiative revealed many data needs on campus What faculty said… • Not sure how or whether to share data • Lack of time to organize data sets • Need help describing data for discovery • Want to find new ways to manage data • Need help archiving data sets/collections Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success “Investigating Data Curation Profiles across Research Domains” This lead to a research award from IMLS • Goals of the project: – A better understanding of the practices, attitudes and needs of researchers in managing and sharing their data. – Identify possible roles for librarians and the skill sets they will need to facilitate data sharing and curation. – Develop “data curation profiles” – a tool for librarians and others to gather information on researcher needs for their data and to inform curation services. – Purdue: Brandt—PI, Carlson—Proj Mgr, Witt—coPI; GSLIS UIUC: Palmer—co-PI, Cragin—Proj Mgr Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success Data Curation Profiles Methodology • Initial interview— based on “Conducting a Data Interview” poster, then pre-profile based on model of data in a few published papers • Interviews were transcribed, reviewed • Coding, wanted more detail -> 2nd Interview • Developed and revised a template for Profile “In our experience, one of the most effective tactics for eliciting datasets for the collection is a simple librarian- researcher interview. In this poster, we share a set of ten questions…” Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success What the interviews asked… • Research Data Lifecycle (story of the data) • Data Management / Storage • Disposition of the Data • Data Dissemination and Sharing • Data Preservation and Repositories • Roles for Libraries and Librarians Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success Interview areas: 20 faculty, 12 disciplines Agronomy & Soil Science (Purdue & UIUC), Anthropology (UIUC), Biochemistry (Purdue), Biology (Purdue), Civil Engineering (Purdue), Earth & Atmospheric Sciences (Purdue & UIUC), Electrical & Computer Engineering (Purdue), Food Science (Purdue), Geology (UIUC), Horticulture & Plant Science (Purdue & UIUC), Kinesiology (UIUC), Speech and Hearing (UIUC) Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success Prioritize your needs for the following types of services The ability for researchers within my discipline to easily find this dataset The ability for researchers outside of my discipline to easily find this dataset n=19 The ability to cite this dataset in my publications The ability for people to easily discover this dataset using Google Witt, M. (2009, May 18). Eliciting Faculty Requirements for Research Data Repositories 4th Int’l Conference on Open Repositories. Georgia Tech, Atlanta, GA. Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success Prioritize your needs for the following types of services The ability of the repository to provide version control for the data The process of submitting this dataset to a repository is automated n=19 The ability for me to submit this dataset to a repository myself The ability to make these data accessible in multiple formats Witt, M. (2009, May 18). Eliciting Faculty Requirements for Research Data Repositories 4th Int’l Conference on Open Repositories. Georgia Tech, Atlanta, GA. Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success • Cragin, M.H., Palmer, C.L., Carlson, J.R., & Witt, M. (2010). “Data Sharing, Small Science, and Institutional Repositories.” Philosophical Transactions of the Royal Society A, 368(1926). • “If cyberinfrastructure is ―principally about data: how to get it, how to share it, how to store it, and how to leverage it for scientific discovery and learning (Edwards, Jackson, Bowker and Knobel 2007, p. 31), then advancing cyberinfrastructure is dependent on our understanding of how to support data practices and needs.” Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success The Data Curation Profile • A means to capture requirements for specific data generated by a single scientist or lab, based on their reported their needs and preferences for these data. • Represents the researcher’s needs and perspectives. • A concise, structured document suitable for sharing and annotation. • A resource for Librarians, Archivists, IT Professionals, Data Mangers, and others. Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success Characteristics of the DCP • Tells “the story” of the data • Focused on a specific data set – provides depth not breadth • Interview based • Meant to be “discipline neutral” and widely applicable to different types of data • Modular – allows for flexibility and tailoring to specific situations and uses Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success DCP Sections • Information about the Data and its Context – Overview of the Research • Focus • Intended Audience • Funding – Data Kinds and Stages • Data Narrative (data lifecycle) • Target Data for Sharing • Use/re-use Value • Contextual Narrative Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success Data Stage Output Typical File Size Format Other / Notes Primary Data Sensor 100k in 1 file per proprietary to FTP downloads are Raw data day the sensor mostly automated. Sensor Data are formatted data – into .csv before bring Processing open/acces reformatted into a Stage 1 sible format Roughly 6kb .csv / .xls mySQL database. Data are extracted 800 records per from the mySQL Data intersection per database for analysis Processed vectors day. SQL / .xls purposes. charts and graphs charts/ used for Analyzed Graphs .xls / .emf interpretation. charts/ Data are presented Published graphs .ppt via power point. Ancillary Data Stills taken .gif /.jpg / Images generated Image from video .ppt from video. Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success More DCP Sections . Information about Needs –Intellectual –Tools Property –Interoperability –Organization and description –Measuring of data Impact –Ingest –Data –Access Management –Discovery –Preservation Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success The DCP Toolkit The Data Curation Profile Toolkit consists of 4 components: • User Guide • Interviewer’s Manual • Interview Worksheet • DCP Template Photo from: http://www.flickr.com/photos/neilt/2517652/sizes/m/in/photostream / Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success Scenarios for using DCP across campus Research proposal development: data mgmt plans Resource data collections: Research data sets: data of use to community Reference data local data management collections: Research data sets: data useful in long term local data assessment Helping faculty in preparation of sharing data — Helping faculty in preparation of archiving data in repository — —In general liaison role, seeking to understand specific faculty research — Used during a collaboration or research project to understand data mgmt needs — Participating with VP for Research Office to understand NSF “mandate” needs Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success How does Purdue use the Profiles? • Strategy 1: D2C2 provides expert consultant/collaborator D2C2 DCP reputation with researchers. • Strategy 2: D2C2 creates tools to help liaison librarians engage librarians researchers; Data Curation Profiles help give structure to conversations about data and facilitate information gathering. researchers Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success Questions? Jake Carlson Data Services Specialist .
Recommended publications
  • Module 8 Wiki Guide
    Best Practices for Biomedical Research Data Management Harvard Medical School, The Francis A. Countway Library of Medicine Module 8 Wiki Guide Learning Objectives and Outcomes: 1. Emphasize characteristics of long-term data curation and preservation that build on and extend active data management ● It is the purview of permanent archiving and preservation to take over stewardship and ensure that the data do not become technologically obsolete and no longer permanently accessible. ○ The selection of a repository to ensure that certain technical processes are performed routinely and reliably to maintain data integrity ○ Determining the costs and steps necessary to address preservation issues such as technological obsolescence inhibiting data access ○ Consistent, citable access to data and associated contextual records ○ Ensuring that protected data stays protected through repository-governed access control ● Data Management ○ Refers to the handling, manipulation, and retention of data generated within the context of the scientific process ○ Use of this term has become more common as funding agencies require researchers to develop and implement structured plans as part of grant-funded project activities ● Digital Stewardship ○ Contributions to the longevity and usefulness of digital content by its caretakers that may occur within, but often outside of, a formal digital preservation program ○ Encompasses all activities related to the care and management of digital objects over time, and addresses all phases of the digital object lifecycle 2. Distinguish between preservation and curation ● Digital Curation ○ The combination of data curation and digital preservation ○ There tends to be a relatively strong orientation toward authenticity, trustworthiness, and long-term preservation ○ Maintaining and adding value to a trusted body of digital information for future and current use.
    [Show full text]
  • Undergraduate Biocuration: Developing Tomorrow’S Researchers While Mining Today’S Data
    The Journal of Undergraduate Neuroscience Education (JUNE), Fall 2015, 14(1):A56-A65 ARTICLE Undergraduate Biocuration: Developing Tomorrow’s Researchers While Mining Today’s Data Cassie S. Mitchell, Ashlyn Cates, Renaid B. Kim, & Sabrina K. Hollinger Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA 30332. Biocuration is a time-intensive process that involves managerial positions. We detail the undergraduate extraction, transcription, and organization of biological or application and training processes and give detailed job clinical data from disjointed data sets into a user-friendly descriptions for each position on the assembly line. We database. Curated data is subsequently used primarily for present case studies of neuropathology curation performed text mining or informatics analysis (bioinformatics, entirely by undergraduates, namely the construction of neuroinformatics, health informatics, etc.) and secondarily experimental databases of Amyotrophic Lateral Sclerosis as a researcher resource. Biocuration is traditionally (ALS) transgenic mouse models and clinical data from ALS considered a Ph.D. level task, but a massive shortage of patient records. Our results reveal undergraduate curators to consolidate the ever-mounting biomedical “big biocuration is scalable for a group of 8-50+ with relatively data” opens the possibility of utilizing biocuration as a minimal required resources. Moreover, with average means to mine today’s data while teaching students skill accuracy rates greater than 98.8%, undergraduate sets they can utilize in any career. By developing a biocurators are equivalently accurate to their professional biocuration assembly line of simplified and counterparts. Initial training to be completely proficient at compartmentalized tasks, we have enabled biocuration to the entry-level takes about five weeks with a minimal be effectively performed by a hierarchy of undergraduate student time commitment of four hours/week.
    [Show full text]
  • Applying the ETL Process to Blockchain Data. Prospect and Findings
    information Article Applying the ETL Process to Blockchain Data. Prospect and Findings Roberta Galici 1, Laura Ordile 1, Michele Marchesi 1 , Andrea Pinna 2,* and Roberto Tonelli 1 1 Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, Italy; [email protected] (R.G.); [email protected] (L.O.); [email protected] (M.M.); [email protected] (R.T.) 2 Department of Electrical and Electronic Engineering (DIEE), University of Cagliari, Piazza D’Armi, 09100 Cagliari, Italy * Correspondence: [email protected] Received: 7 March 2020; Accepted: 7 April 2020; Published: 10 April 2020 Abstract: We present a novel strategy, based on the Extract, Transform and Load (ETL) process, to collect data from a blockchain, elaborate and make it available for further analysis. The study aims to satisfy the need for increasingly efficient data extraction strategies and effective representation methods for blockchain data. For this reason, we conceived a system to make scalable the process of blockchain data extraction and clustering, and to provide a SQL database which preserves the distinction between transaction and addresses. The proposed system satisfies the need to cluster addresses in entities, and the need to store the extracted data in a conventional database, making possible the data analysis by querying the database. In general, ETL processes allow the automation of the operation of data selection, data collection and data conditioning from a data warehouse, and produce output data in the best format for subsequent processing or for business. We focus on the Bitcoin blockchain transactions, which we organized in a relational database to distinguish between the input section and the output section of each transaction.
    [Show full text]
  • Biocuration - Mapping Resources and Needs [Version 2; Peer Review: 2 Approved]
    F1000Research 2020, 9(ELIXIR):1094 Last updated: 22 JUL 2021 RESEARCH ARTICLE Biocuration - mapping resources and needs [version 2; peer review: 2 approved] Alexandra Holinski 1, Melissa L. Burke 1, Sarah L. Morgan 1, Peter McQuilton 2, Patricia M. Palagi 3 1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK 2Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, Oxfordshire, OX1 3QG, UK 3SIB Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland v2 First published: 04 Sep 2020, 9(ELIXIR):1094 Open Peer Review https://doi.org/10.12688/f1000research.25413.1 Latest published: 02 Dec 2020, 9(ELIXIR):1094 https://doi.org/10.12688/f1000research.25413.2 Reviewer Status Invited Reviewers Abstract Background: Biocuration involves a variety of teams and individuals 1 2 across the globe. However, they may not self-identify as biocurators, as they may be unaware of biocuration as a career path or because version 2 biocuration is only part of their role. The lack of a clear, up-to-date (revision) report profile of biocuration creates challenges for organisations like ELIXIR, 02 Dec 2020 the ISB and GOBLET to systematically support biocurators and for biocurators themselves to develop their own careers. Therefore, the version 1 ELIXIR Training Platform launched an Implementation Study in order 04 Sep 2020 report report to i) identify communities of biocurators, ii) map the type of curation work being done, iii) assess biocuration training, and iv) draw a picture of biocuration career development. 1. Tanya Berardini , Phoenix Bioinformatics, Methods: To achieve the goals of the study, we carried out a global Fremont, USA survey on the nature of biocuration work, the tools and resources that are used, training that has been received and additional training 2.
    [Show full text]
  • Automating Data Curation Processes Reagan W
    Automating Data Curation Processes Reagan W. Moore University of North Carolina at Chapel Hill 216 Manning Hall Chapel Hill, NC 27599-3360 01 919 962 9548 [email protected] ABSTRACT • Data format (e.g. HDF5, NetCDF, FITS, …) Scientific data quality can be abstracted as assertions about the • Coordinate system (spatial and temporal locations) properties of a collection of data sets. In this paper, standard properties are defined for data sets, along with the policies and • Geometry (rectilinear, spherical, flux-based, …) procedures that are needed to enforce the properties. The assertions about the collection are verified through periodic • Physical variables (density, temperature, pressure) assessments, which are also implemented as policies and • Physical units (cgs, mks, …) procedures. Data quality curation can then be defined as the set of policies and procedures that verify the scientific data quality • Accuracy (number of significant digits) assertions. The assertions made by the creators of a collection are differentiated from the assertions about data quality needed by the • Provenance (generation steps, calibration steps) users of the data. The transformation of data into a useable form • Physical approximations (incompressible, adiabatic, …) requires well-defined procedures that need to be considered as part of the data curation process. The automated application of • Semantics (domain knowledge for term relationships) both digital curation and data transformation procedures is Additional properties can be derived from these categories. Thus essential for the management of large scientific data collections. the relevant time period may be defined in the temporal Categories and Subject Descriptors coordinate, and allowed transformations may be implicit in the type of variables and physical approximations that were made in D.4.7 [Operating Systems]: Organization and Design – creating the data collection.
    [Show full text]
  • Transitioning from Data Storage to Data Curation: the Challenges Facing an Archaeological Institution Maria Sagrario R
    Issues in Informing Science and Information Technology Volume 10, 2013 Transitioning from Data Storage to Data Curation: The Challenges Facing an Archaeological Institution Maria Sagrario R. Simbulan University of the Philippines, Diliman, Quezon City, Philippines [email protected] Abstract Each archaeological project generates substantial amounts of data from season to season, all of which need to be collected, analyzed, organized, and stored. After an archaeological project or season ends and all the accessioning-related and analytical activities are completed, all those arti- facts are stored away in shelves, boxes and crates, and the associated archaeological data will be stored as digital files and in file folders. There are two critical questions in a time of downsized organizations and shrinking budgets: How can the archaeological data and related scientific in- formation pertaining to each of these archaeological projects be preserved and made available to other researchers? How do we make sure that future generations are able to access these infor- mation resources? I ask these questions as a relative newcomer to archaeology. This paper is a quest to understand how archaeological institutions can transition from an individually curated, per project/per season information gathering and data storage approach to a centralized information resource manage- ment approach known as archaeological data curation. Specifically, this paper will look at the challenges that an archaeological institution would face as it attempts to shift from narrowly- focused, project-related data storage to a broader and long-term focus on the management of the archaeological information resources of the entire institution. Keywords: data curation, data repositories, archaeological data curation, digital curation Introduction Archaeological activities, whether in the field or in the laboratory, generate significant amounts of data.
    [Show full text]
  • How to Discover Requirements for Research Data Management Services Angus Whyte (DCC) and Suzie Allard (Dataone)
    A Digital Curation Centre ‘working level’ guide How to Discover Requirements for Research Data Management Services Angus Whyte (DCC) and Suzie Allard (DataONE) With contributions from: D. Scott Brandt (Purdue University) Susan Wells Parham (Georgia Institute of Technology) Sherry Lake (University of Virginia) Please cite as: Whyte, A and Allard, S. (Eds) 2014. ‘How to Discover Research Data Management Service Requirements’. DCC How-to Guides. Edinburgh: Digital Curation Centre. Available online: http://www.dcc.ac.uk/how-guide Digital Curation Centre, March 2014 This work is licensed under Creative Commons Attribution BY 2.5 Scotland 1 How to Discover Requirements for Research Data Management Services In section 3 we look at the contexts for RDM 1. Introduction requirements. We take as the starting point a high-level model of the services that are typically This guide is meant for people whose role involves needed. Then we consider why the research context developing services or tools to support research data is different from other areas of business process management (RDM) and digital curation, whether in change, and what challenges that presents for a Higher Education Institution or a project working identifying requirements. To help work around the across institutions. Your RDM development role might diversity and complexity of research, we suggest be embedded with the research groups concerned, three key areas likely to shape requirements; or at a more centralised level, such as a library or computing service. You will need a methodical • Ideas, artefacts and facilities: you will need to approach to plan, elicit, analyse, document and identify what kinds of things researchers consider prioritise a range of users’ requirements.
    [Show full text]
  • Data Cleansing & Curation
    A Look at Verinovum’s Approach to Data Cleansing & Curation FOR PAYERS A Look at Verinovum’s Approach to Gartner has named Verinovum in Data Cleansing & Curation reports for payers … “We’re honored that Verinovum has been recognized by For payers, Verinovum provides the ability to streamline Gartner in multiple reports over the past several years. clinical data acquisition, perform data cleansing and curation Verinovum is a leader in addressing one of the most vexing and flexibly integrate the resulting clean data into enterprise issues in the industry; unclean data. We believe these Gartner decision systems. This provides transparency into the quality recognitions validate our unique and singular focus on data of the data asset available as well as services to improve cleansing and curation to make data usable for practical quality and align to various business cases. use cases such as chronic disease management, population health, and analytics to help drive shared savings,” says Mark Gartner Clients can access the below reports in which McCurry, CEO of Verinovum. Verinovum is named, including: PAYERS 1. U.S. Healthcare Payer CIOs Should Avoid Data Lake Mistakes With Clinical Data Integration, Mandi Bishop – May 3, 2018 (refreshed October 16, 2019) “Payer CIOs want to know whether data lakes can deliver quick wins for business leaders hungry to derive actionable insights from unstructured and nonstandard clinical data. To avoid drowning in data, CIOs must first specify goals and critically compare internal capabilities to vendor solutions.” 2. Healthcare Payer CIOs, Leverage Vendor Partners to Succeed at Clinical Data Integration, Mandi Bishop – August, 2018 (refreshed February 3, 2020) “Healthcare payer IT teams typically lack the specific expertise needed to execute complete clinical data integration initiatives, leading to suboptimal business outcomes.
    [Show full text]
  • Data Curation Network
    Data Curation Network: Data Curation Terms and Activities Release Date: October 23, 2016 Authors: Lisa R Johnston (PI), Jake Carlson, Cynthia Hudson-Vitale, Heidi Imker, Wendy ​ Kozlowski, Robert Olendorf, and Claire Stewart. Data Curation Terms and Activities The DCN team drafted a dictionary of relevant terms used in data curation by consulting the following sources: SAA, CASRAI, DCC, RDA, DPC, RDC, ICPSR, and steps from ACRL book ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ “Curating Research Data Volume 2: Handbook of current practice” (forthcoming November 2016). The resulting activities will be used in our engagement sessions with researchers at each of the six institutions in the Data Curation Network as well as our 2017 ARL SPEC Kit survey. Definitions Data Curation: The encompassing work and actions taken by curators of a data ​ ​ repository in order to provide meaningful and enduring access to data. ​ Data Repository: A digital archive that provides services for the storage and retrieval of ​ digital content. Data: Facts, measurements, recordings, records, or observations about the world ​ collected by scientists and others, with a minimum of contextual interpretation. Data may be any format or medium (e.g., numbers, symbols, text, images, films, video, sound recordings, drawings, designs or other graphical representations, procedural manuals, forms, data processing algorithms, or statistical records.) (RDA). Data Curation Activities Data curation activities can be arranged around five steps of data curation life-cycle: Ingest, Appraise (Accept), Curate, Access, and Preserve. Ingest ● Authentication: The process of confirming the identity of a person, generally the depositor, who is contributing data to the data repository. (e.g., password authentication or authorization via digital signature).
    [Show full text]
  • Data Curation in Practice: Extract Tabular Data from PDF Files Using a Data Analytics Tool
    Journal of eScience Librarianship Volume 10 Issue 3 Data Curation in Practice Article 10 2021-08-11 Data Curation in Practice: Extract Tabular Data from PDF Files Using a Data Analytics Tool Allis J. Choi Penn State University Et al. Let us know how access to this document benefits ou.y Follow this and additional works at: https://escholarship.umassmed.edu/jeslib Part of the Scholarly Communication Commons, and the Scholarly Publishing Commons Repository Citation Choi AJ, Xin X. Data Curation in Practice: Extract Tabular Data from PDF Files Using a Data Analytics Tool. Journal of eScience Librarianship 2021;10(3): e1209. https://doi.org/10.7191/jeslib.2021.1209. Retrieved from https://escholarship.umassmed.edu/jeslib/vol10/iss3/10 Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 License. This material is brought to you by eScholarship@UMMS. It has been accepted for inclusion in Journal of eScience Librarianship by an authorized administrator of eScholarship@UMMS. For more information, please contact [email protected]. ISSN 2161-3974 JeSLIB 2021; 10(3): e1209 https://doi.org/10.7191/jeslib.2021.1209 eScience in Action Data Curation in Practice: Extract Tabular Data from PDF Files Using a Data Analytics Tool Allis J. Choi and Xuying Xin The Pennsylvania State University, University Park, PA, USA Abstract Data curation is the process of managing data to make it available for reuse and preservation and to allow FAIR (findable, accessible, interoperable, reusable) uses. It is an important part of the research lifecycle as researchers are often either required by funders or generally encouraged to preserve the dataset and make it discoverable and reusable.
    [Show full text]
  • Amplifying Data Curation Efforts to Improve the Quality of Life Science Data
    AMPLIFYING DATA CURATION EFFORTS TO IMPROVE THE QUALITY OF LIFE SCIENCE DATA A THESIS SUBMITTED TO THE UNIVERSITY OF MANCHESTER FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN THE FACULTY OF SCIENCE AND ENGINEERING 2018 By Mariam S. Alqasab School of Computer Science Contents Abstract 9 Declaration 11 Copyright 12 13 Acknowledgements 14 1 Introduction 15 1.1 Data Curation . 16 1.2 Data Curation in the Biomedical Domain . 17 1.3 Research problem . 17 1.3.1 Research Aims and objectives . 18 1.3.2 Research Questions . 19 1.3.3 Research Contributions . 19 1.4 List of Publications . 20 1.5 Thesis Structure . 20 2 Biocuration Literature Review 22 2.1 Biocuration Definition . 23 2.1.1 Biocuration Workflow . 23 2.1.1.1 Full Curation Workflow . 23 2.1.1.2 Literature Biocuration Workflows . 25 2.2 Biocuration Literature . 32 2.2.1 Triage and Bio-entity Literature . 32 2.2.1.1 Collaborative and Shared Curation . 32 2.2.1.2 Specific data extraction . 35 2 2.2.1.3 Special purpose approaches . 38 2.2.2 Annotating Data Expressions Literature . 40 2.2.2.1 Extracting Relationships . 40 2.2.2.2 Extracting Protein Interactions . 41 2.2.3 Evidence Extraction . 41 2.2.3.1 General Curation Platforms . 45 2.3 Conclusion . 45 3 BIOC-MM 47 3.1 Introduction to Maturity Models . 47 3.1.1 Literature on Maturity Models . 49 3.2 A Maturity Model for Biomedical Curation . 54 3.3 BIOC-MM Structure . 56 3.3.1 The BIOC-MM Maturity Levels .
    [Show full text]
  • Data Curation Approach to Management of Research Data
    Data Curation Approach to Management of Research Data. Use Cases for a Upgrade of the Thermophysical Database THERMAL Andrey Kosinov1, Adilbek Erkimbaev1, Geirgy Kobzev1, and Vladimir Zitserman1 1 Joint Institute for High Temperatures, Russian Academy of Sciences, Russia [email protected] Abstract. Procedures are considered to support extensive archives of digital da- ta called “data storage”. Particular attention is paid to the support of scientific data. It is shown that the activities aimed at updating the thermophysical THERMAL database correspond to the approaches provided by the “data cura- tion”. A communication system for metadata with external ontology is pro- posed. The new version of metadata provides the possibility of multilateral as- sessment of the origin, quality and status of scientific data. It is shown that the use of new metadata provides a significant increase in the value of these stud- ies. Keywords: Research Data, Data Curation, Data Quality, Thermophysical Da- tabase 1 Introduction Digital data uploaded to repositories or databases require permanent procedures that guarantee safety, quality top-level and enduring access to data. The set of such proce- dures is the subject of particular activity of managing the use of data called data cu- ration. This term is rarely used in the Russian literature, although all the necessary actions for data integrity and management are of course performed to support digital repositories or databases. The meaning and content of the concept of curation of data can be revealed by referring to the history of its appearance. This concept originates from the Museum's practice, which is traditionally based on the curator's work on preservation, renovation and description of exhibits.
    [Show full text]