The Data Integration & Imaging Informatics (DI-Cubed) project explored the issue of lack of standardized data capture at the point of data creation, as reflected in the non-image data accompanying 4 TCIA breast cancer collections (Multi-center breast DCE-MRI data and segmentations from patients in the I-SPY 1/ACRIN 6657 trials (ISPY1), BREAST-DIAGNOSIS, Single site breast DCE-MRI data and segmentations from patients undergoing neoadjuvant chemotherapy (Breast-MRI-NACT-Pilot), The Cancer Genome Atlas Breast Invasive Carcinoma Collection (TCGA-BRCA)) and the Ivy Glioblastoma Atlas Project (IvyGAP) brain cancer collection. The work addressed the desire for semantic interoperability between various NCI initiatives by aligning on common clinical metadata elements and supporting use cases that connect clinical, imaging, and genomics data. Accordingly, clinical and measurement data imported into I2B2 were cross-mapped to industry standard concepts for names and values including those derived from BRIDG, CDISC SDTM, DICOM Structured Reporting models and using NCI Thesaurus, SNOMED CT and LOINC controlled terminology.

A subset of the standardized data was then exported from I2B2 in SDTM compliant SAS transport files. The SDTM data was derived from data taken from both the curated TCIA spreadsheets as well as tumor measurements and dates from the TCIA Restful API. Due to the nature of the available data not all SDTM conformance rules were applicable or adhered to.

These Study Data Tabulation Model format (SDTM) datasets were validated using Pinnacle 21 CDISC validation software. The validation software reviews datasets according to their degree of conformance to rules developed for the purposes of FDA submissions of electronic data. Iterative refinements were made to the datasets based upon group discussions and feedback from the validation tool.

Export datasets for the following SDTM domains were generated:

• DM (Demographics)
• DS (Disposition)
• MI (Microscopic Findings)
• PR (Procedures)
• SS (Subject Status)
• TU (Tumor/Lesion Identification)
• TR (Tumor/Lesion Results)

A series of tiled models of superficial thickness covering the UK. The models are derived by direct modelling (natural neighbour interpolation) of BGS Borehole records and BGS Digmap. For the purposes of modelling, superficial deposits include sediments deposited during the Quaternary, subsequent Holocene rivers and coastal systems and also modern anthropogenic material. i.e. deposits that are less than 2.6 million years old. Grids are overprinted with a minimum value so that areas where no bore data is present, but drift is known to occur are given a minimum 1.5m thickness. The superficial thickness models have been created as baseline datasets for the BGS Geohazard programme. They represent the first attempt by BGS to create nationwide models of such data and the models provide only a simple, mathematical interpretation of reality. The complexity of Superficial deposits in Great Britain is such that it is only possible to model indicative values of thickness and elevation. The models should never be used as a substitute for thorough site investigation.

The Critical Path for Alzheimer's Disease (CPAD: http://c-path.org/programs/cpad/) CODR data base contains patient-level control arm data (6,500 patients; 24 clinical trials; MCI and AD), fully anonymized and remapped using CDISC SDTM v3.1.2 Standard. The database includes, but is not limited to, demographic information, APOE4 genotype, concomitant medications and cognitive scales (MMSE, ADAS-Cog, CDR-SB). Currently no AD fluid biomarker or imaging data are included.

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This Web service provides layers which are part of the BGS Superficial Deposits Thickness Model (SDTM) series of datasets. It currently includes two layers from the STDM 1 km hex grid dataset, which is available under the Open Government Licence. These two layers are symbolised on the mean and maximum values of the BSTM thickness model within the area of each hexagon. In these two layers, note that the data include a generic value of 1 m thickness for any area where superficial material is present, but is unproven by boreholes (shown on the map by a grey colour).

A series of tiled models of superficial thickness covering the UK. The models are derived by direct modelling (natural neighbour interpolation) of BGS borehole records and BGS DiGMapGB-50. For the purposes of modelling, superficial deposits include sediments deposited during the Quaternary, subsequent Holocene rivers and coastal systems and also modern anthropogenic material, i.e. deposits that are less than 2.6 million years old. The 50 m x 50 m grids are overprinted with a minimum value so that areas where no bore data is present, but drift is known to occur, are given a minimum 1.5 m thickness. The superficial thickness models have been created as baseline datasets for the BGS Geohazard programme. They represent the first attempt by BGS to create nationwide models of such data and the models provide only a simple, mathematical interpretation of reality. The complexity of superficial deposits in Great Britain is such that it is only possible to model indicative values of thickness and elevation. The models should never be used as a substitute for thorough site investigation. The SDTM comprises three individual datasets; two datasets describe thickness variation and a third dataset details 'proximity' of the modelled data to the original source information. 1. The ASTM (Advanced Superficial Thickness Model) is a model of thickness variation indirectly derived from archive borehole records and map data. 2. The BSTM (Basic Superficial Thickness Model) is a model of thickness variation directly derived from archive borehole records. 3. The DBUFF (distance buffer) dataset is a calculation of spatial distance to the location of any data point used in the model. This provides the user with an indication as to how far the computer has had to interpolate and extrapolate the data from a measured observation point. For more information, refer to the user guide: The National Superficial Deposit Thickness Model (version 5). BGS Open Report OR/09/49. https://webapps.bgs.ac.uk/data/publications/publication.html?id=19867430

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Demographic and clinical characteristics of glaucoma and control subjects included in the study evaluating the relationship between fear of falling and postural reactivity.*.

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Morbidity and mortality attributable to COVID-19 is devastating global health systems and economies. Bacillus Calmette Guérin (BCG) vaccination has been in use for many decades to prevent severe forms of tuberculosis in children. Studies have also shown a combination of improved long-term innate or trained immunity (through epigenetic reprogramming of myeloid cells) and adaptive responses after BCG vaccination, which leads to non-specific protective effects in adults. Observational studies have shown that countries with routine BCG vaccination programs have significantly less reported cases and deaths of COVID-19, but such studies are prone to significant bias and need confirmation. To date, in the absence of direct evidence, WHO does not recommend BCG for the prevention of COVID-19. This project aims to investigate in a timely manner whether and why BCG-revaccination can reduce infection rate and/or disease severity in health care workers during the SARS-CoV-2 outbreak in South Africa...., This dataset was collected in a clinical randomised control trial under the TASK008-BCG CORONA protocol. The trial was conducted in South Africa. This trial was registered with ClinicalTrials.gov, NCT04379336., , # Data from: Safety and efficacy of BCG re-vaccination in relation to COVID-19 morbidity in healthcare workers: A double-blind, randomised, controlled, phase 3 trial

The TASK008-BCG CORONA SDTM datasets contains all the study data collected under the TASK008-BCG CORONA protocol. The data is in the raw format of information captured onto the electronic Case Report Forms from the source documentation.

Description of the data and file structure

The TASK008-BCG CORONA SDTM datasets contain the study data in the CDISC SDTM format. The following CDISC SDTM domains were reported in the datasets:

AE - Adverse Events

CM - Concomitant Medication

DM - Demographics

DS - Disposition

EX - Exposure

IE - Inclusion and Exclusion Criteria

LB - Laboratory Findings

MH - Medical History

SV - Subject Visits

VS - Vital Signs

File Formats: The datasets are in both .CSV and .sas7bdat (include 1 SAS formats. catalogue) Below is the structure of each domain

| AE (Adverse Events) Domain ...

A sample of the data that is used as features for the Siamese network.

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Overall accuracy and macro F1 scores using 3 methods of evaluating predicted labels with the siamese network along with the two comparison methods that were implemented in prior attempts.

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This dataset contains data files for clinical data in SDTM format for three studies conducted during the BioVacSafe project at the University of Surrey Clinical research centre. Data has been processed at the Data Science Institute for preparation to loading them into the demo instance of PlatformTM ( a data custodianship platform for translational research data) Users can use this dataset to load into local instances of PlatformTM for demo purposes.

The zipped file contains datasets from the FAMOVID clinical trial (Thai Clinical Trials Registry ID: TCTR20230111009; published paper: 10.1016/j.ijid.2024.107021) in CDISC SDTM format.
Data de-identification was performed in compliance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule. In particular, participant and site identifiers were recoded as new, randomly-generated unique identifiers, and all dates were redacted.

This dataset contains sample annotation files and data files for three microarray studies conducted during the BioVacSafe project, CRC305A, CRC305B and CRC305C Sample files are formatted against the SDTM format and the data files are 2d matrix each study (samples in columns and probsets in rows)

This is a dataset contributed by the Kenya Medical Research Institute KEN to IDDO’s Malaria research platform in 2013. It has been curated to the CDISC SDTM data standard at the individual participant-level for 385 participants. Further information about the data available in this dataset can be found on the IDDO website.