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Presentation on Open Science, FAIR principles, Research Data Management, FAIR Software, delivered during BOSSConf 2022doi:10.5281/zenodo.6490583 fatcat:lafxoxt5ordvxiangc545jeda4
BHKi Seminars: Bioinformatics, software engineering and Data Management- Data Management Planning 19th July 2022doi:10.5281/zenodo.6860636 fatcat:4gtojlttajapnibjvalltstee4
Material for 1.5 hour workshop on FAIR principles with regards to software, and what is currently known, best practices for FAIRness. Scope Definition: In the scope of this session, the term software is used to refer to algorithms, scripts, source codes and packages used in research, but not software such as matlab or word processing software etc... Here I the term software refers specifically to source code as it is often the most easiest form to apply the FAIR4RS Principles. Feedback: Pleasedoi:10.5281/zenodo.6574092 fatcat:6xz3xkfztrgwdex335v24xnm4i
more »... eel free to get in touch if you have any comments or would like to contribute to update this material firstname.lastname@example.org Resources: Special Issue: Emerging FAIR Practices. Issue Editors: Barend Mons, Erik Schultes & Annika Jacobsen https://direct.mit.edu/dint/issue/2/1-2 - Software vs. data in the context of citation: DOI 10.7287/peerj.preprints.2630v1 - Software citation principles DOI https://doi.org/10.7717/peerj-cs.86 - Sharing interoperable workflow provenance: A review of best practices and their practical application in CWLProv DOI https://doi.org/10.1093/gigascience/giz095 - Five recommendations for FAIR Software https://fair-software.eu/ - FAIR Principles for Research Software (FAIR4RS Principles) https://rd-alliance.org/group/fair-research-software-fair4rs-wg/outcomes/fair-principles-research-software-fair4rs - Taking a fresh look at FAIR for research software DOI https://doi.org/10.1016/j.patter.2021.100222 - The role of metadata in reproducible computational research DOI https://doi.org/10.1016/j.patter.2021.100322 - RDA Webinar: FAIR Principles for Research Software (FAIR4RS WG) DOI https://doi.org/10.5281/zenodo.5524726 - Toward Better Research Software DOI https://doi.org/10.5281/zenodo.4551441 - FAIR4RS WG subgroup community consultation March 2021 DOI https://doi.org/10.5281/zenodo.4635410 - From FAIR research data toward FAIR and open research software DOI https://doi.org/10.1515/itit-2019-0040 - FAIR for Research Software [...]
Talk from Open Science FAIR Symposium covering the following topics: Overview of Research Data Management (RDM) What is Data? FAIR Principles Where do we start? RDM best practices FAIR vs Open Open Data challengesdoi:10.5281/zenodo.5562793 fatcat:2cpy6z6vejfpjb35jc56y6taaq
Materials for 1 full-day workshop on Research Data Management basics covering the following topics: Part 1: Overview of the RDM unit services and support Part 2: What is Data? Part 3: FAIR data Part 4: Open Data Part 5: Reuse and Reproducibility Part 6:Where do we start? Part 7: Documentation - Electronic lab notebooks - Metadata - README files Examples of breakout room exercises and discussion questions included. Resources: Research data management: Making the Case for Research Data Managementdoi:10.5281/zenodo.4562630 fatcat:immairgkjveqxow43flrnej2ou
more »... https://www.dcc.ac.uk/guidance/briefing-papers/making-case-rdm What is Research Data? https://www2.le.ac.uk/services/research-data/old-2019-12-11/documents/UoL_ReserchDataDefinitions_20120904.pdf New England Collaborative Data Management Curriculum https://library.umassmed.edu/resources/necdmc/modules Science Europe- RDM guide: https://scienceeurope.org/our-resources/practical-guide-to-the-international-alignment-of-research-data-management/ Roberts Lab Handbook- Data management in life sciences https://robertslab.github.io/resources/Data-Management/ Research data management (RDM) open training materials https://zenodo.org/communities/dcc-rdm-training-materials/?page=1&size=20 What is data & FAIR data: Research Libraries' https://www.liberquarterly.eu/article/10.18352/lq.9173/ Zenodo-FAIR principles: https://about.zenodo.org/principles/ "A love letter to your future self": What scientists need to know about FAIR data https://www.natureindex.com/news-blog/what-scientists-need-to-know-about-fair-data Invest 5% of research funds in ensuring data are reusable https://www.nature.com/articles/d41586-020-00505-7 FAIRaware- FAIR assessment https://fairaware.dans.knaw.nl/ H2020 Programme Guidelines on FAIR Data Management in Horizon 2020 https://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf FAIRsFAIR Europe https://www.fairsfair.eu/ How to FAIR https://howtofair.dk/ Go FAIR https://www.go-fair.org/ FAIR sharing https://fairsharing.org/ Open Data & reuse, reproduc [...]
Open Life Science presentation on Research data management https://openlifesci.org/ Week 6 cohort 4.doi:10.5281/zenodo.5579455 fatcat:4xwq34kimvasjldo5gxgxjktle
In molecular and cell biology, most of the data presented in published papers are not available in accessible formats that would allow for analysis and systematic mining. Here we present SourceData (http://sourcedata.embo.org), a platform that allows researchers and publishers to share scientific figures and, when available, the underlying source data in a way that is machine-readable and findable. SourceData has therefore developed tools to generate machine-readable descriptive metadata fromdoi:10.1101/058529 fatcat:47qwc7sezzc53krln54wn2r4uu
more »... gures in published manuscripts. Experimentally tested hypotheses are represented as directed relationships between standardized biological entities, which can be connected into a searchable data-oriented ′knowledge graph′. SourceData focuses on the core of scientific evidence - data presented in figures - and makes papers searchable based on their data content. By coupling data availability to improved discoverability, SourceData aims at establishing a self-reinforcing data ′ecosystem′ that bridges the conventional visual and narrative description of research findings with a machine-readable representation of data and hypotheses.
Speakers: Sara El-Gebali, Chris Erdmann, Annajiat Alim Rasel, Donny Winston ...doi:10.5281/zenodo.5116337 fatcat:bfgs6cgr55h2lc7fxhhb55b4vq
The criteria for choosing relevant cell lines among a vast panel of available intestinal-derived lines exhibiting a wide range of functional properties are still ill-defined. The objective of this study was, therefore, to establish objective criteria for choosing relevant cell lines to assess their appropriateness as tumor models as well as for drug absorption studies. Results: We made use of publicly available expression signatures and cell based functional assays to delineate differencesdoi:10.1186/1471-2164-13-274 pmid:22726358 pmcid:PMC3412164 fatcat:wyvooz2n6zh55jl2r67qjh4eja
more »... en various intestinal colon carcinoma cell lines and normal intestinal epithelium. We have compared a panel of intestinal cell lines with patient-derived normal and tumor epithelium and classified them according to traits relating to oncogenic pathway activity, epithelial-mesenchymal transition (EMT) and stemness, migratory properties, proliferative activity, transporter expression profiles and chemosensitivity. For example, SW480 represent an EMT-high, migratory phenotype and scored highest in terms of signatures associated to worse overall survival and higher risk of recurrence based on patient derived databases. On the other hand, differentiated HT29 and T84 cells showed gene expression patterns closest to tumor bulk derived cells. Regarding drug absorption, we confirmed that differentiated Caco-2 cells are the model of choice for active uptake studies in the small intestine. Regarding chemosensitivity we were unable to confirm a recently proposed association of chemo-resistance with EMT traits. However, a novel signature was identified through mining of NCI60 GI50 values that allowed to rank the panel of intestinal cell lines according to their drug responsiveness to commonly used chemotherapeutics. Conclusions: This study presents a straightforward strategy to exploit publicly available gene expression data to guide the choice of cell-based models. While this approach does not overcome the major limitations of such models, introducing a rank order of selected features may allow selecting model cell lines that are more adapted and pertinent to the addressed biological question.
The last few years have witnessed significant changes in Pfam (https://pfam.xfam.org). The number of families has grown substantially to a total of 17,929 in release 32.0. New additions have been coupled with efforts to improve existing families, including refinement of domain boundaries, their classification into Pfam clans, as well as their functional annotation. We recently began to collaborate with the RepeatsDB resource to improve the definition of tandem repeat families within Pfam. Wedoi:10.1093/nar/gky995 pmid:30357350 pmcid:PMC6324024 fatcat:q5wtvxfqzzhobpis5p4d2g6wue
more »... ried out a significant comparison to the structural classification database, namely the Evolutionary Classification of Protein Domains (ECOD) that led to the creation of 825 new families based on their set of uncharacterized families (EUFs). Furthermore, we also connected Pfam entries to the Sequence Ontology (SO) through mapping of the Pfam type definitions to SO terms. Since Pfam has many community contributors, we recently enabled the linking between authorship of all Pfam entries with the corresponding authors' ORCID identifiers. This effectively permits authors to claim credit for their Pfam curation and link them to their ORCID record.
In contrast to these results, Sara et al.  and Anneren et al.  reported low levels of IGF-I in patient with DS during childhood and adolescence. Similarly, Anneren et al. ...doi:10.1016/j.ejmhg.2014.01.007 fatcat:tyskxrojpjdyvnuhszykbyent4
The InterPro database (http://www.ebi.ac.uk/ interpro/) classifies protein sequences into families and predicts the presence of functionally important domains and sites. Here, we report recent developments with InterPro (version 70.0) and its associated software, including an 18% growth in the size of the database in terms on new InterPro entries, updates to content, the inclusion of an additional entry type, refined modelling of discontinuous domains, and the development of a new programmaticdoi:10.1093/nar/gky1100 pmid:30398656 fatcat:ccbjkaabarhazk3yclk2vjjqoq
more »... nterface and website. These developments extend and enrich the information provided by InterPro, and provide greater flexibility in terms of data access. We also show that InterPro's sequence coverage has kept pace with the growth of UniProtKB, and discuss how our evaluation of residue coverage may help guide future curation activities.
Participants: Yo Yehudi (Ethos of Open), Sara El Gebali (Open Data), James Powell, Cameron Riddell (Open Software), Shilaan Alzahawi (Open Science Tools & Resources), Natasha Batalha (Open Results), Chelle ...doi:10.5281/zenodo.6617538 fatcat:hgvutn7bwfde3fb54gqwwpnsga
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