Resources

Data Cleaning and Standardization

• Wilkinson, Mark D., Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg et al. “FAIR Guiding Principles for scientific data management and stewardship.” Scientific data 3, no. 1 (2016): 1-9.
• File formats
  • https://researchdataservice.illinois.edu/file-format/
  • https://guides.osu.edu/rdm-best-practice/file-formats
• Lossless/Lossy reference
  • https://www.adobe.com/uk/creativecloud/photography/discover/lossy-vs-lossless.html
  • https://en.wikipedia.org/wiki/Audio_file_format
  • https://en.wikipedia.org/wiki/List_of_file_formats

Data Documentation

• Data Management
  • Becker, Carina, Carolin Hundt, Claudia Engelhardt, Johannes Sperling, Moritz Kurzweil, and Ralph Müller-Pfefferkorn. “Data management plan tools: Overview and evaluation.” In Proceedings of the Conference on Research Data Infrastructure, vol. 1. 2023.
  • Hudson-Vitale, Cynthia, and Heather Moulaison-Sandy. “Data Management Plans: A Review.” DESIDOC Journal of Library & Information Technology 39, no. 6 (2019).
  • Smale, Nicholas, Gareth Denyer, Kathryn Unsworth, Elise Magatova, and Daniel Barr. “A review of the history, advocacy and efficacy of data management plans.” International Journal of Digital Curation (2020).
• Software
  • Barker, Michelle, Neil P. Chue Hong, Daniel S. Katz, Anna-Lena Lamprecht, Carlos Martinez-Ortiz, Fotis Psomopoulos, Jennifer Harrow et al. “Introducing the FAIR Principles for research software.” Scientific Data 9, no. 1 (2022): 622.
  • Smith, Arfon M., Kyle E. Niemeyer, Daniel S. Katz, Lorena A. Barba, George Githinji, Melissa Gymrek, Kathryn D. Huff et al. “Journal of Open Source Software (JOSS): design and first-year review.” PeerJ Computer Science 4 (2018): e147.
  • The Open Source Initiative Approved Licenses (software).
• Clarence the Tortoise
  • https://www.mpacorn.com/articles/tortoise-turning-100/
  • https://www.discover805.com/clarence-the-95-year-old-galapagos-tortoise-living-at-moorpark-zoo-helped-save-his-species/
  • https://www.latimes.com/archives/la-xpm-2001-feb-09-me-23253-story.html
  • https://econlife.com/2017/10/metric-system-switch/
• Ontologies
  • Oltjen, William C, Fan, Yangxin, Liu, Jiqi, Huang, Liangyi, Huang, Liangyi, Yu, Xuanji, Li, Mengjie, Seigneur, Hubert, Xiao, Xusheng, Davis, Kristopher O., Bruckman, Laura S, Wu, Yinghui, & French, Roger H. FAIRification, Quality Assessment, and Missingness Pattern Discovery for Spatiotemporal Photovoltaic Data. United States. https://doi.org/10.2172/1959042
  • Priyan Rajamohan, Alexander Harding Bradley, Hayden Caldwell, Erika I. Barcelos, and Roger H. French, “FAIRmaterials: Find the docs.” SDLE Res. Cntr., Case Western Reserve University, 2023. Available: https://cwrusdle.bitbucket.io/. [Accessed: Mar. 14, 2023]
  • A. Nihar et al., “Toward Findable, Accessible, Interoperable and Reusable (FAIR) Photovoltaic System Time Series Data,” 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC), Fort Lauderdale, FL, USA, 2021, pp. 1701-1706, doi: 10.1109/PVSC43889.2021.9518782.
  • Cambridge Semantics OWL 101: https://cambridgesemantics.com/blog/semantic-university/learn-owl-rdfs/owl-101/
  • CSIRO’s Intro to RDF and OWL Tutorial: https://csiro-enviro-informatics.github.io/info-engineering/tutorials/tutorial-intro-to-rdf-and-owl.html
  • FAIR Materials FindTheDocs: https://cwrusdle.bitbucket.io/
  • WebVOWL Ontology visualizer: https://webvowl-w4rydgnccq-ul.a.run.app/
  • JSON-LD Playground: https://json-ld.org/playground/

Data Publication

• Licensing
  • D.B. Deutz, M.C.H. Buss, J. S. Hansen, K. K. Hansen, K.G. Kjelmann, A.V. Larsen, E. Vlachos, K.F. Holmstrand (2020). How to FAIR: a Danish website to guide researchers on making research data more FAIR. https://doi.org/10.5281/zenodo.3712065
  • The Open Knowledge Foundation’s Conformant Licenses Page (data and content)
  • The Open Source Initiative Approved Licenses (software)
  • GitHub’s “Choose a License” Help Guide.
• Data Repositories
  • Registry of Research Data Repositories (re3data.org)
  • FAIRsharing.org
• Data-specific conferences:
  • Neural Information Processing Systems (NeurIPS)
  • Special Interest Group on Knowledge Discovery and Data Mining (KDD)
  • IEEE International Conference on Image Processing (ICIP)
• Pure data journals (General):
  • Data in Brief
  • Scientific Data
• Pure data journals (Domain-specific):
  • Earth System Science Data
  • Geoscience Data Journal
• Mixed Journals
  • Springer Discover series

Equitable Accessibility

• Why Equitable Accessibility Matters – Statistics
  • Population: https://www.who.int/health-topics/disability#tab=tab_1
  • Hearing: https://www.who.int/health-topics/hearing-loss#tab=tab_2
  • Visual: https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment
• Why Equitable Accessibility Matters – Perspective
  • Abdolrahmani, A., Storer, K. M., Roy, A. R. M., Kuber, R., & Branham, S. M. (2020). Blind leading the sighted: drawing design insights from blind users towards more productivity-oriented voice interfaces. ACM Transactions on Accessible Computing (TACCESS), 12(4), 1-35.
  • Dobel, C., Nestler-Collatz, B., Guntinas-Lichius, O., Schweinberger, S. R., & Zäske, R. (2020). Deaf signers outperform hearing non-signers in recognizing happy facial expressions. Psychological research, 84, 1485-1494.
  • Han, C., Mitra, P., & Billah, S. M. (2024, May). Uncovering Human Traits in Determining Real and Spoofed Audio: Insights from Blind and Sighted Individuals. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-14).
  • Pang, W., Xing, H., Zhang, L., Shu, H., & Zhang, Y. (2020). Superiority of blind over sighted listeners in voice recognition. The Journal of the Acoustical Society of America, 148(2), EL208-EL213.
  • Grant, A., & Kara, H. (2021). Considering the Autistic advantage in qualitative research: the strengths of Autistic researchers. Contemporary Social Science, 16(5), 589-603.
  • Taylor, H., & Vestergaard, M. D. (2022). Developmental dyslexia: disorder or specialization in exploration?. Frontiers in psychology, 13, 889245.
  • Schippers, L. M., Horstman, L. I., Velde, H. V. D., Pereira, R. R., Zinkstok, J., Mostert, J. C., … & Hoogman, M. (2022). A qualitative and quantitative study of self-reported positive characteristics of individuals with ADHD. Frontiers in Psychiatry, 13, 922788.
  • Bury, S. M., Hedley, D., Uljarević, M., & Gal, E. (2020). The autism advantage at work: A critical and systematic review of current evidence. Research in Developmental Disabilities, 105, 103750.
  • Hatak, I., Chang, M., Harms, R., & Wiklund, J. (2021). ADHD symptoms, entrepreneurial passion, and entrepreneurial performance. Small business economics, 57, 1693-1713.
• Language
  • Eglash, Ron. “Broken metaphor: The master-slave analogy in technical literature.” Technology and culture 48, no. 2 (2007): 360-369.
  • IT Inclusive Language Guide: https://itconnect.uw.edu/guides-by-topic/identity-diversity-inclusion/inclusive-language-guide/
• Generated Graphs and Plots
  • Color vision deficiency types: https://www.allaboutvision.com/conditions/color-blindness/types-of-color-blindness/
  • Alivia Eng, M. R. (2023). Mastcam Image Data (Sols 2303-3672) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8339229
  • Crameri, F., Shephard, G. E., & Heron, P. J. (2020). The misuse of colour in science communication. Nature communications, 11(1), 5444.
  • Vilkin, Aleksey and Safonov, Ilia. (2014). Newspaper and magazine images segmentation dataset. UCI Machine Learning Repository. https://doi.org/10.24432/C5N60V
  • matplotlib colormaps: https://matplotlib.org/stable/users/explain/colors/colormaps.html

Equitable Data Challenges

• Big Data
  • Li, J., Li, D., Savarese, S., & Hoi, S. (2023, July). Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning (pp. 19730-19742). PMLR.
  • Leotta, M., Mori, F., & Ribaudo, M. (2023). Evaluating the effectiveness of automatic image captioning for web accessibility. Universal access in the information society, 22(4), 1293-1313.
  • Mei, X., Liu, X., Plumbley, M. D., & Wang, W. (2022). Automated audio captioning: An overview of recent progress and new challenges. EURASIP journal on audio, speech, and music processing, 2022(1), 26.
• Audio visualization
  • Truskinger, A., Brereton, M., & Roe, P. (2018, October). Visualizing five decades of environmental acoustic data. In 2018 IEEE 14th International Conference on e-Science (e-Science) (pp. 1-10). IEEE.
  • Cottingham, M. D., & Erickson, R. J. (2020). Capturing emotion with audio diaries. Qualitative Research, 20(5), 549-564.
• Visual sonification
  • Ali, S., Muralidharan, L., Alfieri, F., Agrawal, M., & Jorgensen, J. (2020). Sonify: making visual graphs accessible.” In Human Interaction and Emerging Technologies: Proceedings of the 1st International Conference on Human Interaction and Emerging Technologies (IHIET 2019), August 22-24, 2019, Nice, France (pp. 454-459). Springer International Publishing.
  • Sawe, N., Chafe, C., & Treviño, J. (2020). Using data sonification to overcome science literacy, numeracy, and visualization barriers in science communication. Frontiers in Communication, 5, 46.
• Tactile vibration responses
  • Yoshioka, T., Bensmaia, S. J., Craig, J. C., & Hsiao, S. S. (2007). Texture perception through direct and indirect touch: An analysis of perceptual space for tactile textures in two modes of exploration. Somatosensory & motor research, 24(1-2), 53-70.
  • Otake, K., Okamoto, S., Akiyama, Y., & Yamada, Y. (2022). Tactile texture display combining vibrotactile and electrostatic-friction stimuli: Substantial effects on realism and moderate effects on behavioral responses. ACM Transactions on Applied Perception, 19(4), 1-18.
• Raw/Original Data Components
  • Van Horn, G., Branson, S., Farrell, R., Haber, S., Barry, J., Ipeirotis, P., … & Belongie, S. (2015). Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 595-604).
  • Gemmeke, J. F., Ellis, D. P., Freedman, D., Jansen, A., Lawrence, W., Moore, R. C., … & Ritter, M. (2017, March). Audio set: An ontology and human-labeled dataset for audio events. In 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 776-780). IEEE.
  • “[HSD] Practise Toniic, ChrizZ, Feybi (Melbourne Shuffle Hamburg).” YouTube, uploaded by Feybi11 16 July, 2010 https://www.youtube.com/watch?v=4X2aUZFZlzc&t=31s
  • Diment, A., Mesaros, A., Heittola, T., & Virtanen, T. (2017). TUT Rare sound events, Development dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.401395
  • Gaur, M., Alambo, A., Sain, J. P., Kursuncu, U., Thirunarayan, K., Kavuluru, R., Sheth, A., Welton, R., & Pathak, J. (2019, May 4). Reddit C-SSRS Suicide Dataset. The World Wide Web Conference. https://doi.org/10.5281/zenodo.2667859
  • Michal Ptaszynski, Agata Pieciukiewicz, Pawel Dybala, Pawel Skrzek, Kamil Soliwoda, Marcin Fortuna, Gniewosz Leliwa, & Michal Wroczynski. (2022). Expert-annotated dataset to study cyberbullying in Polish language [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7188178