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Data Curation The Current Landscape Carly Strasser, PhD California Digital Library Microsoft eScience Workshop @carlystrasser Oct 2012 From Flickr by DW0825 From Flickr by Flickmor Digital data Digital From Flickr by deltaMike www.woodrow.org C.Strasser Courtesey of WHOI From Flickr by US Army Environmental Command Digital data + Complex workflows From Calisphere via San Jose Public Library From Flickr by diylibrarian www blog.order2disorder.com From Flickr by Flickr From csessums Data Metadata by Flickr From csessums Recreated from Klump et al. 2006 From Flickr by diylibrarian www Data www Metadata From Flickr by torkildr Recreated from Klump et al. 2006 The Fallout Data Reuse Data Sharing Data Management UGLY TRUTH Many (most?) researchers… btrgroup.com are not taught data management are not effectively managingWHY? or organizing their data don’t share data publicly or store it in an archive aren’t convinced they should share data don’t know about tools that can help them Hurdles to Data Stewardship iowa_spirit_walker From Flickr From Flickr by • Cost • Confusion about standards • Disparate datasets • Lack of training • Fear of lost rights or benefits • No incentives From Flickr by diylibrarian www ? Data www Metadata From Flickr by torkildr Recreated from Klump et al. 2006 Who cares? From Flickr by Redden-McAllister www.rba.gov.au Data are first class products of research Moross From Flickr by Richard Richard by Flickr From Data are first class products of research Moross Requiring Data Management Richard by Flickr From Journal publishers Journal publishers Funders Data management requirements Data are first class products of research Moross Publishing Data Richard by Flickr From Data Publication Data are first class products of research Moross Citing Data Richard by Flickr From Data Citation Example: Sidlauskas, B. 2007. Data from: Testing for unequal rates of morphological diversification in the absence of a detailed phylogeny: a case study from characiform fishes. Dryad Digital Repository. doi:10.5061/dryad.20 Data are first class products of research Moross Creating Incentives Richard by Flickr From Incentivizing www.rba.gov.au bthomso From Flickr by by Flickr From Data are first class products of research Moross Providing Infrastructure & Tools Richard by Flickr From NSF funded DataNet Project Office of Cyberinfrastructure www.dataone.org B A C B A C B A C B A C B A C B A C Intercept researchers where they already work 29 Investigator Toolkit Support Plan Analyze Collect Kepler Integrate Assure Discover Describe Preserve Facilitate Archiving Data Data Reuse & management Sharing & organization Reproducibility Publishing Open Source Tool Add-in & Web Application Earth, environmental, ecological researchers Requirements Features Best practices check Generate metadata Generate citation Post data to repository Data Repository for Anyone | Anywhere B A C C dataup.cdlib.org Build community Add repositories Add metadata animationresources.org From From schema dataup.cdlib.org @DataUpCDL facebook.com/DataUpCDL Data Management Plans: dmp.cdlib.org carlystrasser.net @carlystrasser [email protected] Jeff Dozier Researcher Bill Michener Project Lead Chris Mentzel Funder Dave Vieglais Cyberinfrastructure dude Stephanie Wright Librarian Data quality problem: How to identify questionable measurements Dana Meadows snow pillow, evening of June 25 2009 4 “ SWE -8 “ SWE 12” SWE decline after removal (J. Lundquist) .

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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.
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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.
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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.
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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.
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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.
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Automating Data Curation Processes Reagan W
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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.
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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.
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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.
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Data Curation Network
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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.
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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 .
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