Dataset Information

IncluSet: A Data Surfacing Repository for Accessibility Datasets.

ABSTRACT: Datasets and data sharing play an important role for innovation, benchmarking, mitigating bias, and understanding the complexity of real world AI-infused applications. However, there is a scarcity of available data generated by people with disabilities with the potential for training or evaluating machine learning models. This is partially due to smaller populations, disparate characteristics, lack of expertise for data annotation, as well as privacy concerns. Even when data are collected and are publicly available, it is often difficult to locate them. We present a novel data surfacing repository, called IncluSet, that allows researchers and the disability community to discover and link accessibility datasets. The repository is pre-populated with information about 139 existing datasets: 65 made publicly available, 25 available upon request, and 49 not shared by the authors but described in their manuscripts. More importantly, IncluSet is designed to expose existing and new dataset contributions so they may be discoverable through Google Dataset Search.

SUBMITTER: Kacorri H

PROVIDER: S-EPMC8375514 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundTissue microarray (TMA) technology has been developed to facilitate large, genome-scale molecular pathology studies. This technique provides a high-throughput method for analyzing a large cohort of clinical specimens in a single experiment thereby permitting the parallel analysis of molecular alterations (at the DNA, RNA, or protein level) in thousands of tissue specimens. As a vast quantity of data can be generated in a single TMA experiment a systematic approach is required for the storage and analysis of such data.DescriptionTo analyse TMA output a relational database (known as TmaDB) has been developed to collate all aspects of information relating to TMAs. These data include the TMA construction protocol, experimental protocol and results from the various immunocytological and histochemical staining experiments including the scanned images for each of the TMA cores. Furthermore the database contains pathological information associated with each of the specimens on the TMA slide, the location of the various TMAs and the individual specimen blocks (from which cores were taken) in the laboratory and their current status i.e. if they can be sectioned into further slides or if they are exhausted. TmaDB has been designed to incorporate and extend many of the published common data elements and the XML format for TMA experiments and is therefore compatible with the TMA data exchange specifications developed by the Association for Pathology Informatics community. Finally the design of the database is made flexible such that TMA experiments from several types of cancer can be stored in a single database, which incorporates the national minimum data set required for pathology reports supported by the Royal College of Pathologists (UK).ConclusionTmaDB will provide a comprehensive repository for TMA data such that a large number of results from the numerous immunostaining experiments can be efficiently compared for each of the TMA cores. This will allow a systematic, large-scale comparison of tumour samples to facilitate the identification of gene products of clinical importance such as therapeutic or prognostic markers. In addition this work will contribute to the establishment of a standard for reporting TMA data analogous to MIAME in the description of microarray data.

Dataset Information

IncluSet: A Data Surfacing Repository for Accessibility Datasets.

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