Data challenges of biomedical researchers in the age of omics
Background High-throughput technologies are rapidly generating large amounts of diverse omics data. Although this offers a great opportunity, it also poses great challenges as data analysis becomes more complex. The purpose of this study was to identify the main challenges researchers face in analyz...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2018-09-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/5553.pdf |
| _version_ | 1797425527834804224 |
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| author | Rolando Garcia-Milian Denise Hersey Milica Vukmirovic Fanny Duprilot |
| author_facet | Rolando Garcia-Milian Denise Hersey Milica Vukmirovic Fanny Duprilot |
| author_sort | Rolando Garcia-Milian |
| collection | DOAJ |
| description | Background High-throughput technologies are rapidly generating large amounts of diverse omics data. Although this offers a great opportunity, it also poses great challenges as data analysis becomes more complex. The purpose of this study was to identify the main challenges researchers face in analyzing data, and how academic libraries can support them in this endeavor. Methods A multimodal needs assessment analysis combined an online survey sent to 860 Yale-affiliated researchers (176 responded) and 15 in-depth one-on-one semi-structured interviews. Interviews were recorded, transcribed, and analyzed using NVivo 10 software according to the thematic analysis approach. Results The survey response rate was 20%. Most respondents (78%) identified lack of adequate data analysis training (e.g., R, Python) as a main challenge, in addition to not having the proper database or software (54%) to expedite analysis. Two main themes emerged from the interviews: personnel and training needs. Researchers feel they could improve data analyses practices by having better access to the appropriate bioinformatics expertise, and/or training in data analyses tools. They also reported lack of time to acquire expertise in using bioinformatics tools and poor understanding of the resources available to facilitate analysis. Conclusions The main challenges identified by our study are: lack of adequate training for data analysis (including need to learn scripting language), need for more personnel at the University to provide data analysis and training, and inadequate communication between bioinformaticians and researchers. The authors identified the positive impact of medical and/or science libraries by establishing bioinformatics support to researchers. |
| first_indexed | 2024-03-09T08:17:25Z |
| format | Article |
| id | doaj.art-2e8c2977c3964d36ade2b825ff81ca04 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T08:17:25Z |
| publishDate | 2018-09-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-2e8c2977c3964d36ade2b825ff81ca042023-12-02T22:00:14ZengPeerJ Inc.PeerJ2167-83592018-09-016e555310.7717/peerj.5553Data challenges of biomedical researchers in the age of omicsRolando Garcia-Milian0Denise Hersey1Milica Vukmirovic2Fanny Duprilot3Bioinformatics Support Program, Research and Education Services, Cushing/Whitney Medical Library, Yale University, New Haven, CT, United States of AmericaScience Libraries, Lewis Science Library, Princeton University, Princeton, NJ, United States of AmericaPulmonary Critical Care & Sleep Medicine, Yale School of Medicine, Yale University, New Haven, CT, United States of AmericaService commun de la documentation, Université Denis Diderot (Paris VII), Paris, FranceBackground High-throughput technologies are rapidly generating large amounts of diverse omics data. Although this offers a great opportunity, it also poses great challenges as data analysis becomes more complex. The purpose of this study was to identify the main challenges researchers face in analyzing data, and how academic libraries can support them in this endeavor. Methods A multimodal needs assessment analysis combined an online survey sent to 860 Yale-affiliated researchers (176 responded) and 15 in-depth one-on-one semi-structured interviews. Interviews were recorded, transcribed, and analyzed using NVivo 10 software according to the thematic analysis approach. Results The survey response rate was 20%. Most respondents (78%) identified lack of adequate data analysis training (e.g., R, Python) as a main challenge, in addition to not having the proper database or software (54%) to expedite analysis. Two main themes emerged from the interviews: personnel and training needs. Researchers feel they could improve data analyses practices by having better access to the appropriate bioinformatics expertise, and/or training in data analyses tools. They also reported lack of time to acquire expertise in using bioinformatics tools and poor understanding of the resources available to facilitate analysis. Conclusions The main challenges identified by our study are: lack of adequate training for data analysis (including need to learn scripting language), need for more personnel at the University to provide data analysis and training, and inadequate communication between bioinformaticians and researchers. The authors identified the positive impact of medical and/or science libraries by establishing bioinformatics support to researchers.https://peerj.com/articles/5553.pdfInformation seeking behaviorData interpretationStatisticalGenomicsComputational biologySoftware |
| spellingShingle | Rolando Garcia-Milian Denise Hersey Milica Vukmirovic Fanny Duprilot Data challenges of biomedical researchers in the age of omics PeerJ Information seeking behavior Data interpretation Statistical Genomics Computational biology Software |
| title | Data challenges of biomedical researchers in the age of omics |
| title_full | Data challenges of biomedical researchers in the age of omics |
| title_fullStr | Data challenges of biomedical researchers in the age of omics |
| title_full_unstemmed | Data challenges of biomedical researchers in the age of omics |
| title_short | Data challenges of biomedical researchers in the age of omics |
| title_sort | data challenges of biomedical researchers in the age of omics |
| topic | Information seeking behavior Data interpretation Statistical Genomics Computational biology Software |
| url | https://peerj.com/articles/5553.pdf |
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