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Scientist and data architect collaborate to curate and archive an inner ear electrophysiology data collection


Autoři: Brenda Farrell aff001;  Jason Bengtson aff002
Působiště autorů: Bobby R Alford Department of Otolaryngology and Head & Neck Surgery, Baylor College of Medicine, Houston, Texas, United States of America aff001;  K-State Libraries, Kansas State University, Manhattan, Kansas, United States of America aff002
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0223984

Souhrn

In the past scientists reported summaries of their findings; they did not provide their original data collections. Many stakeholders (e.g., funding agencies) are now requesting that such data be made publicly available. This mandate is being adopted to facilitate further discovery, and to mitigate waste and deficits in the research process. At the same time, the necessary infrastructure for data curation (e.g., repositories) has been evolving. The current target is to make research products FAIR (Findable, Accessible, Interoperable, Reusable), resulting in data that are curated and archived to be both human and machine compatible. However, most scientists have little training in data curation. Specifically, they are ill-equipped to annotate their data collections at a level that facilitates discoverability, aggregation, and broad reuse in a context separate from their creation or sub-field. To circumvent these deficits data architects may collaborate with scientists to transform and curate data. This paper’s example of a data collection describes the electrical properties of outer hair cells isolated from the mammalian cochlea. The data is expressed with a variant of The Ontology for Biomedical Investigations (OBI), mirrored to provide the metadata and nested data architecture used within the Hierarchical Data Format version 5 (HDF5) format. Each digital specimen is displayed in a tree configuration (like directories in a computer) and consists of six main branches based on the ontology classes. The data collections, scripts, and ontological OWL file (OBI based Inner Ear Electrophysiology (OBI_IEE)) are deposited in three repositories. We discuss the impediments to producing such data collections for public use, and the tools and processes required for effective implementation. This work illustrates the impact that small collaborations can have on the curation of our publicly-funded collections, and is particularly salient for fields where data is sparse, throughput is low, and sacrifice of animals is required for discovery.

Klíčová slova:

Electrophysiology – Membrane potential – Scientists – Metadata – Cochlea – Information architecture – Outer hair cells – Ontologies


Zdroje

1. Protein Data Bank, PDB [Internet]. Available from: http://www.wwpdb.org/.

2. Grabowski M, Minor W. Sharing Big Data. IUCrJ. 2017;4(Pt 1):3–4. doi: 10.1107/S2052252516020364 28250936

3. NCBI. Gene [Available from: https://www.ncbi.nlm.nih.gov/gene.

4. Agency National Research Council of Canada with support from Canadian Space. Canadian Astronomy Data Center CADC [Available from: http://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/en/.

5. Astrophysics Division of NASA's Science Mission Directorate. NASA's High Energy Astrophysics Science Archive Research Center [Available from: https://heasarc.gsfc.nasa.gov/docs/HHP_heasarc_info.html.

6. NOAA. National Center for Environmental Information [Available from: https://www.ncdc.noaa.gov/.

7. Nosek B. Opening Science. In: R B-DRJ, editor. Open: The Philosophy and Practices that are Revolutionizing Education and Science. London: Ubiquity Press; 2017.

8. Nosek BA, Alter G, Banks GC, Borsboom D, Bowman SD, Breckler SJ, et al. SCIENTIFIC STANDARDS. Promoting an open research culture. Science. 2015;348(6242):1422–5. doi: 10.1126/science.aab2374 26113702

9. Read KB, Sheehan JR, Huerta MF, Knecht LS, Mork JG, Humphreys BL, et al. Sizing the Problem of Improving Discovery and Access to NIH-Funded Data: A Preliminary Study. PLoS One. 2015;10(7):e0132735. doi: 10.1371/journal.pone.0132735 26207759

10. Sansone SA, Gonzalez-Beltran A, Rocca-Serra P, Alter G, Grethe JS, Xu H, et al. DATS, the data tag suite to enable discoverability of datasets. Sci Data. 2017;4:170059. doi: 10.1038/sdata.2017.59 28585923

11. Warren E. Strengthening Research through Data Sharing. N Engl J Med. 2016;375(5):401–3. doi: 10.1056/NEJMp1607282 27518656

12. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3:160018. doi: 10.1038/sdata.2016.18 26978244

13. Goodman SN, Fanelli D, Ioannidis JP. What does research reproducibility mean? Sci Transl Med. 2016;8(341):341ps12.

14. Freedman LP, Cockburn IM, Simcoe TS. The Economics of Reproducibility in Preclinical Research. PLoS Biol. 2015;13(6):e1002165. doi: 10.1371/journal.pbio.1002165 26057340

15. Nuttall AL, Dolan DF, Avinash G. Laser Doppler velocimetry of basilar membrane vibration. Hear Res. 1991;51(2):203–13. doi: 10.1016/0378-5955(91)90037-a 1827786

16. Sellick PM, Patuzzi R, Johnstone BM. Measurement of basilar membrane motion in the guinea pig using the Mossbauer technique. J Acoust Soc Am. 1982;72(1):131–41. doi: 10.1121/1.387996 7108035

17. Dong W, Olson ES. Detection of cochlear amplification and its activation. Biophys J. 2013;105(4):1067–78. doi: 10.1016/j.bpj.2013.06.049 23972858

18. Cody AR, Russell IJ. The response of hair cells in the basal turn of the guinea-pig cochlea to tones. J Physiol. 1987;383:551–69. doi: 10.1113/jphysiol.1987.sp016428 3656135

19. Dallos P. Response characteristics of mammalian cochlear hair cells. J Neurosci. 1985;5(6):1591–608. 4009248

20. Russell IJ, Kossl M. Voltage responses to tones of outer hair cells in the basal turn of the guinea-pig cochlea: significance for electromotility and desensitization. Proc Biol Sci. 1992;247(1319):97–105. doi: 10.1098/rspb.1992.0014 1349187

21. Fettiplace R, Kim KX. The physiology of mechanoelectrical transduction channels in hearing. Physiol Rev. 2014;94(3):951–86. doi: 10.1152/physrev.00038.2013 24987009

22. Yates GK, Kirk DL. Cochlear Electrically Evoked Emissions Modulated by Mechanical Transduction Channels. J Neurosci. 1998;18(6):1996–2003. 9482786

23. Zidanic M, Brownell WE. Fine structure of the intracochlear potential field. I. The silent current. Biophys J. 1990;57(6):1253–68. doi: 10.1016/S0006-3495(90)82644-8 2393707

24. Wilson JP, Johnstone JR. Basilar membrane and middle-ear vibration in guinea pig measured by capacitive probe. J Acoust Soc Am. 1975;57(3):705–23. doi: 10.1121/1.380472 1123489

25. Muller M. Frequency representation in the rat cochlea. Hear Res. 1991;51(2):247–54. doi: 10.1016/0378-5955(91)90041-7 2032960

26. Muller M, Smolders JW. Shift in the cochlear place-frequency map after noise damage in the mouse. Neuroreport. 2005;16(11):1183–7. doi: 10.1097/00001756-200508010-00010 16012345

27. Pujol R, Lenoir M, Ladrech S, Tribillac F, Rebillard G. Correlation between the length of outer hair cells and the frequency coding of the cochlea. Advances in Bioscience. 1992;83.

28. Corbitt C, Farinelli F, Brownell WE, Farrell B. Tonotopic relationships reveal the charge density varies along the lateral wall of outer hair cells. Biophys J. 2012;102(12):2715–24. doi: 10.1016/j.bpj.2012.04.054 22735521

29. Mammano F, Ashmore JF. Differential expression of outer hair cell potassium currents in the isolated cochlea of the guinea-pig. J Physiol. 1996;496 (Pt 3):639–46.

30. Raybould NP, Housley GD. Variation in expression of the outer hair cell P2X receptor conductance along the guinea-pig cochlea. J Physiol. 1997;498 (Pt 3):717–27.

31. Santos-Sacchi J, Kakehata S, Kikuchi T, Katori Y, Takasaka T. Density of motility-related charge in the outer hair cell of the guinea pig is inversely related to best frequency. Neurosci Lett. 1998;256(3):155–8. doi: 10.1016/s0304-3940(98)00788-5 9855363

32. Beisel KW, Rocha-Sanchez SM, Morris KA, Nie L, Feng F, Kachar B, et al. Differential expression of KCNQ4 in inner hair cells and sensory neurons is the basis of progressive high-frequency hearing loss. J Neurosci. 2005;25(40):9285–93. doi: 10.1523/JNEUROSCI.2110-05.2005 16207888

33. Engel J, Braig C, Ruttiger L, Kuhn S, Zimmermann U, Blin N, et al. Two classes of outer hair cells along the tonotopic axis of the cochlea. Neuroscience. 2006;143(3):837–49. doi: 10.1016/j.neuroscience.2006.08.060 17074442

34. Eglen SJ, Weeks M, Jessop M, Simonotto J, Jackson T, Sernagor E. A data repository and analysis framework for spontaneous neural activity recordings in developing retina. Gigascience. 2014;3(1):3. doi: 10.1186/2047-217X-3-3 24666584

35. Landis SC, Amara SG, Asadullah K, Austin CP, Blumenstein R, Bradley EW, et al. A call for transparent reporting to optimize the predictive value of preclinical research. Nature. 2012;490(7419):187–91. doi: 10.1038/nature11556 23060188

36. National Temporal Bone Database | NIDCD National Temporal Bone, Hearing and Balance Pathology Resource Registry [Internet]. Available from: https://national-tb-database.meei.harvard.edu.

37. Children's Hospital of Philadelphia Research Institute. Audiological and Genetic Database: medical data for researchers studying pediatric hearing health [Available from: https://audgendb.chop.edu/.

38. University of Maryland. gEAR (gene Expression Analysis Resource) portal [Available from: http://umgear.org/.

39. The International Mouse Phenotyping Consortium, IMPC, https://www.mousephenotype.org/ [Internet]. 2019.

40. Bowl MR, Simon MM, Ingham NJ, Greenaway S, Santos L, Cater H, et al. A large scale hearing loss screen reveals an extensive unexplored genetic landscape for auditory dysfunction. Nat Commun. 2017;8(1):886. doi: 10.1038/s41467-017-00595-4 29026089

41. Jessop M, Weeks M, Austin J. CARMEN: a practical approach to metadata management. Philos Trans A Math Phys Eng Sci. 2010;368(1926):4147–59. doi: 10.1098/rsta.2010.0147 20679128

42. Altman M, King G. A Proposed Standard for the Scholarly Citation of Quantitative Data. D-Lib Magazine. 2007;13(3/4):1–.

43. Future of Research Communication e-Scholarship FORCE. The FAIR data principles 2017 [updated 2017. Available from: https://www.force11.org/group/fairgroup/fairprinciples.

44. Cousijn H, Kenall A, Ganley E, Harrison M, Kernohan D, Lemberger T, et al. A data citation roadmap for scientific publishers. Sci Data. 2018;5:180259. doi: 10.1038/sdata.2018.259 30457573

45. Bandrowski A, Brinkman R, Brochhausen M, Brush MH, Bug B, Chibucos MC, et al. The Ontology for Biomedical Investigations. PloS One. 2016;11(4):e0154556. doi: 10.1371/journal.pone.0154556 27128319

46. MATLAB—MathWorks [Available from: https://www.mathworks.com/products/matlab.html.

47. The HDF Group. Hierarchical Data Format, version 5 1997–2019 [Available from: https://www.hdfgroup.org/HDF5/.

48. Farrell B, Bengtson J. Ontology based data architecture to promote data sharing in electrophysiology. Proceedings of the 9th International Conference on Biological Ontology (ICBO); August 7th to 10th Corvallis, Oregon http://ceur-ws.org/Vol-2285/ICBO_2018_paper_3.pdf: http://ceur-ws.org/Vol-2285/ICBO_2018_paper_3.pdf; 2018.

49. Wu L, Wang D, Evans JA. Large teams develop and small teams disrupt science and technology. Nature. 2019;566(7744):378–82. doi: 10.1038/s41586-019-0941-9 30760923

50. Hamill OP, Marty A, Neher E, Sakmann B, Sigworth FJ. Improved patch-clamp techniques for high-resolution current recording from cells and cell-free membrane patches. Pflugers Arch. 1981;391(2):85–100. doi: 10.1007/bf00656997 6270629

51. Farrell B, Do Shope C, Brownell WE. Voltage-dependent capacitance of human embryonic kidney cells. Phys Rev E Stat Nonlin Soft Matter Phys. 2006;73(4 Pt 1):041930.

52. Santos-Sacchi J, Kakehata S, Takahashi S. Effects of membrane potential on the voltage dependence of motility-related charge in outer hair cells of the guinea-pig. J Physiol. 1998;510 (Pt 1):225–35.

53. Curry A. Rescue of Old Data Offers Lesson for Particle Physicists. Science. 2011;331(6018):694–5. doi: 10.1126/science.331.6018.694 21311003

54. Sun G, Khoo CSG. Social science research data curation: issues of reuse. Libellarium: journal for the research of writing, books, and cultural heritage institutions. 2017;9(2).

55. Doan A, Halevy A, Ives Z. Principles of Data Integration. Saint Louis, UNITED STATES: Elsevier Science; 2012 2012.

56. Berman JJ. Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information. San Francisco, UNITED STATES: Elsevier Science; 2013 2013.

57. da Silva JR, Ribeiro C, Lopes JC, editors. Ontology-based Multi-domain Metadata for Research Data Management Using Triple Stores2014 2014. New York, NY, USA: ACM.

58. Lacroix Z, Critchlow T. Bioinformatics: Managing Scientific Data. San Francisco, UNITED STATES: Elsevier Science; 2003 2003.

59. Kansa EC, Kansa SW, Arbuckle B. Publishing and Pushing: Mixing Models for Communicating Research Data in Archaeology. International Journal of Digital Curation. 2014;9(1):57–70.

60. Vita R, Overton JA, Greenbaum JA, Sette A, Peters B. Query enhancement through the practical application of ontology: the IEDB and OBI. J Biomed Semantics. 2013;4(Suppl 1):S6.

61. Gelernter J, Lesk M. Use of Ontologies for Data Integration and Curation. International Journal of Digital Curation. 2011;6(1):70–8.

62. Payne PRO, Borlawsky TB, Kwok A, Dhaval R, Greaves AW. Ontology-anchored Approaches to Conceptual Knowledge Discovery in a Multi-dimensional Research Data Repository. Summit on Translat Bioinforma. 2008;2008:85–9. 21347129

63. Code Analysis, Repository & Modelling For E-Neuroscience, CARMEN [

64. International Neuroinformatics Coordinating Facility, INCF [Available from: https://www.incf.org.

65. Neuroscience Information Framework, NIF [Available from: https://neuinfo.org/.

66. Neurodata Without Borders—The Kavli Foundation [Available from: https://neuinfo.org/.

67. XNAT [Available from: https://www.xnat.org.

68. Gibson F, Overton PG, Smulders TV, Schultz SR, Eglen SJ, Ingram CD, et al. Minimum Information about a Neuroscience Investigation (MINI) Electrophysiology. Nature Precedings. 2008(713).

69. CRCNS—Collaborative Research in Computational Neuroscience—Data sharing [Internet]. Available from: http://crcns.org/.

70. Teeters JL, Godfrey K, Young R, Dang C, Friedsam C, Wark B, et al. Neurodata Without Borders: Creating a Common Data Format for Neurophysiology. Neuron. 2015;88(4):629–34. doi: 10.1016/j.neuron.2015.10.025 26590340

71. Teeters JL, Harris KD, Millman KJ, Olshausen BA, Sommer FT. Data sharing for computational neuroscience. Neuroinformatics. 2008;6(1):47–55. doi: 10.1007/s12021-008-9009-y 18259695

72. Quinn TA, Granite S, Allessie MA, Antzelevitch C, Bollensdorff C, Bub G, et al. Minimum Information about a Cardiac Electrophysiology Experiment (MICEE): standardised reporting for model reproducibility, interoperability, and data sharing. Prog Biophys Mol Biol. 2011;107(1):4–10. doi: 10.1016/j.pbiomolbio.2011.07.001 21745496

73. Aras K, Good W, Tate J, Burton B, Brooks D, Coll-Font J, et al. Experimental Data and Geometric Analysis Repository-EDGAR. J Electrocardiol. 2015;48(6):975–81. doi: 10.1016/j.jelectrocard.2015.08.008 26320369

74. Consortium for ECG Imaging (CEI) [Available from: http://www.ecg-imaging.org/home.

75. IEEG.org [Internet]. Available from: https://www.ieeg.org/.

76. Kini LG, Davis KA, Wagenaar JB. Data integration: Combined imaging and electrophysiology data in the cloud. Neuroimage. 2016;124(Pt B):1175–81. doi: 10.1016/j.neuroimage.2015.05.075 26044858

77. Wagenaar JB, Worrell GA, Ives Z, Dumpelmann M, Litt B, Schulze-Bonhage A. Collaborating and sharing data in epilepsy research. J Clin Neurophysiol. 2015;32(3):235–9. doi: 10.1097/WNP.0000000000000159 26035676

78. Electrophysiology Task Force of the International Neuroinformatics Coordinating Facility (INCF) Program on Standards for Data Sharing. Requirements for storing electrophysiology data. 2014.

79. Courtot M, Gibson F, Lister AL, Malone J, Schober D, Brinkman RR, et al. MIREOT: the Minimum Information to Reference an External Ontology Term. Nature Precedings. 2009(713).

80. NCBO Bioportal [Internet]. Available from: https://bioportal.bioontology.org/.

81. Jupp Simon TBJMCLMPJ, Parkinson M, H. A new Ontology Lookup Service at EMBL-EBI. In: Malone J, et al., editors. Proceedings of SWAT4LS International Conference; 20152015.

82. Ong E, Xiang Z, Zhao B, Liu Y, Lin Y, Zheng J, et al. Ontobee: A linked ontology data server to support ontology term dereferencing, linkage, query and integration. Nucleic Acids Res. 2017;45(D1):D347–D52. doi: 10.1093/nar/gkw918 27733503

83. Ontobee: A linked data server designed for ontologies [Available from: http://www.ontobee.org.

84. Maguire E, Gonzalez-Beltran A, Whetzel PL, Sansone SA, Rocca-Serra P. OntoMaton: a bioportal powered ontology widget for Google Spreadsheets. Bioinformatics. 2013;29(4):525–7. doi: 10.1093/bioinformatics/bts718 23267176

85. Beta Cell Genomics Ontology, OBI_BCGO 2015 [updated 2015. Available from: http://purl.obolibrary.org/obo/bcgo.owl.

86. protégé [Available from: http://protege.stanford.edu/about.php.

87. WebProtege—Protege Wiki [Available from: https://protegewiki.stanford.edu/wiki/WebProtege.

88. Mungall C. O-SD, Diehl A., Haendel M., Vasilevsky N., van Slyke C., Balhoff C. Meehan T., Bradford Y. Cell Ontology, http://purl.obolibrary.org/obo/cl.owl 2018 [updated 2018.

89. Mungall C. Ontology of Biological Attributes, http://purl.obolibrary.org/obo/oba.owl. 2018.

90. Phenotype And Trait Ontology, PATO [Available from: http://www.obofoundry.org/ontology/pato.html.

91. Foundational Model of Anatomy, FMA 2018 [updated 2018. Available from: http://purl.obolibrary.org/obo/fma.owl.

92. Mungall CJ, Torniai C, Gkoutos GV, Lewis SE, Haendel MA. Uberon, an integrative multi-species anatomy ontology. Genome Biol. 2012;13(1):R5. doi: 10.1186/gb-2012-13-1-r5 22293552

93. Uber Anatomy Ontology, UBERON, http://purl.obolibrary.org/obo/uberon.owl [Internet]. 2018.

94. Chemical Entities of Biological Interest, CHEBI 2018 [updated 2018. Available from: http://purl.obolibrary.org/obo/chebi.owl.

95. Computational Neuroscience Ontology, CNO, 2015 [updated 2015. Available from: https://bioportal.bioontology.org/ontologies/CNO.owl.

96. Ménager HIJ, Kalaš M. EMBRACE Data and Methods, EDAM, http://edamontology.org/EDAM.owl 2017 [updated 2017.

97. Gene Ontology, GO—Summary | NCBO BioPortal [Available from: https://bioportal.bioontology.org/ontologies/GO.

98. Mammalian Phenotype Ontology, MP—Summary | NCBO BioPortal [Available from: https://bioportal.bioontology.org/ontologies/MP.

99. National Center for Biotechnology Information (NCBI) Organismal Classification—Summary | NCBO BioPortal [Available from: https://bioportal.bioontology.org/ontologies/NCBITAXON.

100. National Cancer Institute Thesaurus, NCIT—Summary | NCBO BioPortal [Available from: https://bioportal.bioontology.org/ontologies/NCIT.

101. Mungall C. Ontology of Biological Attributes, OBA, http://purl.obolibrary.org/obo/oba.owl. 2018.

102. Ontology of Physics for Biology, OPB—Summary | NCBO BioPortal [Available from: https://bioportal.bioontology.org/ontologies/OPB.

103. Systems Biology Ontology, SBO—Summary | NCBO BioPortal [Available from: https://bioportal.biolontology.org/ontologies/SBO.

104. Semantic science Integrated Ontology, SIO—Summary | NCBO BioPortal [Available from: https://bioportal.bioontology.org/ontologies/SIO.

105. Santos-Sacchi J. Reversible inhibition of voltage-dependent outer hair cell motility and capacitance. J Neurosci. 1991;11(10):3096–110. 1941076

106. Gale JE, Ashmore JF. An intrinsic frequency limit to the cochlear amplifier. Nature. 1997;389(6646):63–6. doi: 10.1038/37968 9288966

107. Barnett DW, Misler S. An optimized approach to membrane capacitance estimation using dual-frequency excitation. Biophys J. 1997;72(4):1641–58. doi: 10.1016/S0006-3495(97)78810-6 9083668

108. Chen P, Gillis KD. The noise of membrane capacitance measurements in the whole-cell recording configuration. Biophys J. 2000;79(4):2162–70. doi: 10.1016/S0006-3495(00)76464-2 11023920

109. Farrell B. Ugrinov R. Brownell W. E. Frequency dependence of admittance and conductance of the outer hair cell. In: Nuttall PG A., Ren T., Grosh K., deBoer E., editor. Auditory Mechanisms: processes and models; 2006. New Jersey: World Scientific; 2006. p. 230–1.

110. Zenodo—Research [Internet]. Available from: https://zenodo.org.

111. Digital Commons at the Texas Medical Center Library [Available from: https://digitalcommons.library.tmc.edu/.

112. Rubel O, Dougherty M, Prabhat, Denes P, Conant D, Chang EF, et al. Methods for Specifying Scientific Data Standards and Modeling Relationships with Applications to Neuroscience. Front Neuroinform. 2016;10:48. doi: 10.3389/fninf.2016.00048 27867355

113. Allotrope Foundation developed and implemented by OSTHUS. Allotrope Data Format [updated 20190428. Available from: http://www.allotrope-framework-architect.com/.

114. Zehl L, Jaillet F, Stoewer A, Grewe J, Sobolev A, Wachtler T, et al. Handling Metadata in a Neurophysiology Laboratory. Front Neuroinform. 2016;10:26. doi: 10.3389/fninf.2016.00026 27486397

115. Hinard V, Britan A, Rougier JS, Bairoch A, Abriel H, Gaudet P. ICEPO: the ion channel electrophysiology ontology. Database (Oxford). 2016;2016.

116. Zheng J, Shen W, He DZ, Long KB, Madison LD, Dallos P. Prestin is the motor protein of cochlear outer hair cells. Nature. 2000;405(6783):149–55. doi: 10.1038/35012009 10821263

117. McQuilton P. S. Hodson R. Lawrence S-A Sansone. FAIRsharing, a registry interlinking standards, databases, repositories and policies 2019 [updated 2019. Available from: https://rd-alliance.org/group/fairsharing-registry-connecting-data-policies-standards-databases-wg/outcomes/fairsharing.

118. Registry of Research Data Repositories, https://doi.org/10.17616/R3D 2019 [updated 2019/01/24/. Available from: https://www.re3data.org/.

119. FAIRsharing [Available from: https://fairsharing.org/.

120. PhysioNet the research resource for complex physiologic signals [Internet]. Available from: https://physionet,org/.

121. Electrophysiology Data Discovery Index | The CardioVascular Research Grid [Available from: http://www.cvrgrid.org/tools/eddi.

122. The Organization—Dryad [Internet]. Available from: https://datadryad.org/pages/organization.

123. Duret G PFA. Data from: Diflunisal inhibits prestin by chloride-dependent mechanism. http://datadryad.org.

124. Duret G, Pereira FA, Raphael RM. Diflunisal inhibits prestin by chloride-dependent mechanism. PLoS One. 2017;12(8):e0183046. doi: 10.1371/journal.pone.0183046 28817613

125. The Dataverse Project [Internet]. Available from: https://dataverse.org/.

126. OpenRefine [Available from: http://openrefine.org/.

127. Bengtson J. The Semantic Revolution. Journal of Electronic Resources in Medical Libraries. 2015;12(1):72–82.

128. Sansone SA, Rocca-Serra P, Field D, Maguire E, Taylor C, Hofmann O, et al. Toward interoperable bioscience data. Nat Genet. 2012;44(2):121–6. doi: 10.1038/ng.1054 22281772

129. Fairdom. FAIRDOM.org [Available from: https://fair-dom.org/.

130. Wolstencroft K, Owen S, Horridge M, Krebs O, Mueller W, Snoep JL, et al. RightField: embedding ontology annotation in spreadsheets. Bioinformatics. 2011;27(14):2021–2. doi: 10.1093/bioinformatics/btr312 21622664

131. Just Enough Results Model Ontology, JERM, 2017 [updated 2017. Available from: https://bioportal.bioontology.org/ontologies/JERM.

132. DataMed prototype(v3.0) [Internet]. Available from: https://datamed.org.

133. biomedical and healthCare Data Discovery Index Ecosystem (bioCADDIE) [Available from: https://biocaddie.org/.


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