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Susheel Varma
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| "modified": "2023-05-30 08:41:52.053000", | ||
| "description": "This project comprises a set of R packages to assist in epidemiological studies using electronic health records databases.\n\nCALIBER (http://caliberresearch.org/) is led from the Farr Institute @ London. CALIBER investigators represent a collaboration between epidemiologists, clinicians, statisticians, health informaticians and computer scientists with initial funding from the Wellcome Trust and the National Institute for Health Research.\n\nThe goal of CALIBER is to provide evidence across different stages of translation, from discovery, through evaluation to implementation where electronic health records provide new scientific opportunities.\n", | ||
| "license": "GNU General Public License (GPL)", | ||
| "views": 339, | ||
| "views": 340, | ||
| "category": "Package", | ||
| "relations": [], | ||
| "keywords": [ | ||
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| "modified": "2023-05-30 15:14:05.854000", | ||
| "description": "Scalable Data Anonymization Tool - supports multiple privacy models.\n\nARX is a comprehensive open source software for anonymizing sensitive personal data. It supports a wide variety of (1) privacy and risk models, (2) methods for transforming data and (3) methods for analyzing the usefulness of output data. It supports various anonymization techniques, methods for analyzing data quality and re-identification risks and it supports well-known privacy models, such as k-anonymity, l-diversity, t-closeness and differential privacy.\nARX is an open source tool for transforming structured (i.e. tabular) personal data using selected methods from the broad areas of data anonymization and statistical disclosure control. It supports transforming datasets in ways that make sure that they adhere to user-specified privacy models and risk thresholds that mitigate attacks that may lead to privacy breaches. ARX can be used to remove direct identifiers (e.g. names) from datasets and to enforce further constraints on indirect identifiers. Indirect identifiers (or quasi-identifiers, or keys) are attributes that do not directly identify an individual but may together with other indirect identifiers form an identifier that can be used for linkage attacks. It is typically assumed that information about indirect identifiers is available to the attacker (in some form of background knowledge) and that they cannot simply be removed from the dataset (e.g. because they are required later for analyses).", | ||
| "license": "Apache License 2.0", | ||
| "views": 456, | ||
| "views": 457, | ||
| "category": "Data Anonymization Tool", | ||
| "relations": [], | ||
| "keywords": [ | ||
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| "modified": "2023-05-25 09:08:34.656000", | ||
| "description": "Standardised Covid-19 questionnaires have been created that are freely available to population health researchers to address health, behaviour, social, environmental and economic questions in the context of the pandemic. \n\nThe first questionnaire asks about COVID-19 signs and symptoms, and more broadly about the effect of lockdown on physical and mental health and overall wellbeing. The second questionnaire builds on the first and includes neighbourhood issues, domestic violence, alcohol use, healthcare, thoughts on the lockdown and new connections made during the pandemic. The group continues to expand the questionnaire bank as the pandemic unfolds. \n\nThe questionnaire is freely available and to use it simply get in touch via email:\u202f [email protected].\n\nThe questionnaires have been developed with the input of many individuals and longitudinal studies, contributing time to create, collect and analyse data.\n\n\n\n### Results & Insights\n\nInsights can be explored at https://www.closer.ac.uk/covid19-longitudinal-research-hub/\n\nPublications arising will be added in due course", | ||
| "license": "", | ||
| "views": 152, | ||
| "views": 153, | ||
| "category": "Questionnaire", | ||
| "relations": [], | ||
| "keywords": [ | ||
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@@ -6391,7 +6391,7 @@ | |
| "modified": "2023-09-21 17:54:36.915000", | ||
| "description": "The Catalogue of Mental Health Measures is designed to provide easy access to information about the mental health measures included in UK cohort and longitudinal studies to maximise the uptake of existing data and facilitate mental health research.\n\nThe Catalogue provides descriptions of the studies as well as the instruments used to assess mental health and wellbeing. It also includes details on each measure such as the items, informants, response scale and reporting period. Users can explore existing data by searching for a specific study, a mental health topic, or an instrument. \n\nBy providing details of the measures and studies, the Catalogue serves as a resource for researchers to identify datasets that include mental health and wellbeing measures, plan harmonisation studies, and plan further data collection.\n\n\n### Results & Insights\n\nThe Catalogue of Mental Health Measures features over 55 cohort studies, more than 4,000 measures of mental health and wellbeing, and covers 30+ mental health topics. \n\nThe Catalogue includes a range of study types, including birth cohorts, twin studies, repeated cross-sectional studies, accelerated longitudinal and household panel designs. Some were specifically designed to focus on mental health, while others have included mental health measures within a more multi-purpose context.\n", | ||
| "license": "", | ||
| "views": 216, | ||
| "views": 217, | ||
| "category": "Dataset discovery tool", | ||
| "relations": [], | ||
| "keywords": [ | ||
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| "modified": "2023-05-25 00:03:35.482000", | ||
| "description": "Part of a set of infographics developed by Health Data Research UK that help explain some of the Gateway's key functionalities in an easy to understand format as well as some of the other programmes and services available at HRD UK.\n\nThis one relates to the Gateway Community Forum - an open space to discuss health research topics, comment on resources like datasets and share ideas about how to improve the Gateway. \n\nFree for all to download, use and share but please credit the Health Data Research Innovation Gateway when using this tool.", | ||
| "license": "CC BY NC 3.0", | ||
| "views": 19, | ||
| "views": 20, | ||
| "category": "infographic", | ||
| "relations": [], | ||
| "keywords": [ | ||
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| "modified": "2023-05-26 16:21:43.573000", | ||
| "description": "This tools provide insights into the neonatal health data contained within the National Neonatal Research Database (NNRD). The tools are aimed at parents, clinical teams, researchers, and health service managers.", | ||
| "license": "", | ||
| "views": 79, | ||
| "views": 80, | ||
| "category": "Data Modelling, Data Visualisation, Developer stack", | ||
| "relations": [], | ||
| "keywords": [ | ||
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| "modified": "2023-05-16 11:38:12.372000", | ||
| "description": "The primary purpose of the CaRROT-CDM package is to Extract input datasets and Transform them using mapping rules defined in a json file, outputting formatted datasets in tsv format that can be Loaded into a database or other destination (ETL).", | ||
| "license": "", | ||
| "views": 44, | ||
| "views": 45, | ||
| "category": "Data Integration and ETL Tool", | ||
| "relations": [], | ||
| "keywords": [], | ||
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| "modified": "2023-07-03 07:19:22.398000", | ||
| "description": "CamCOPS is an open-source application for capturing information relevant for cognitive and psychiatric assessment, on tablets, laptops, and desktops. It offers simple questionnaires and more complex tasks, and sends its data securely to your server.\n\n### Results & Insights\n\nReference paper: Cardinal RN, Burchell M (2021). The Cambridge Cognitive and Psychiatric Assessment Kit (CamCOPS): a secure open-source client-server system for mobile research and clinical data capture. Frontiers in Psychiatry 12: 578298. https://pubmed.ncbi.nlm.nih.gov/34867492/; https://doi.org/10.3389/fpsyt.2021.578298.\n\nSource code is at https://github.com/ucam-department-of-psychiatry/crate", | ||
| "license": "GNU General Public License version 3.0 (GPLv3)", | ||
| "views": 40, | ||
| "views": 41, | ||
| "category": "Mobile Application", | ||
| "relations": [], | ||
| "keywords": [ | ||
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@@ -7148,7 +7148,7 @@ | |
| "modified": "2023-09-04 14:22:05.302000", | ||
| "description": "The ACEsinEHRs library allows users to search and access tools, code lists, algorithms and scripts for validated indicators to identify adverse childhood experiences (ACEs) in electronic health records (EHRs) of parents and children before and after birth.\n\nThe platform provides information on definitions, theoretical and clinical concepts and \"how to guides\" to help users apply the developed ACE indicators to create \u201cresearch-ready\u201d datasets.\n\n### Results & Insights\n\nMission statement and aims:\n-To improve the health of families and young people by using electronic health records (EHRs) to identify and measure adverse childhood experiences (ACEs).\n-To advocate for early support and family centred services for families with ACEs.\n-To work with researchers, professionals, and policymakers to promote trauma-informed care and public health policies that support families affected by ACEs.\n-To enhance the methodological standard, accessibility, and utility of data-driven think-family approaches to study adversity using EHRs.\n-To provide a validated system to measure intervenable and clinically relevant ACEs with the potential to support trauma-informed data-driven research, public health, policy, and health care.\n-To continuously develop the www.ACEsinEHRs.com platform to improve resources for researchers, professionals, and policymakers", | ||
| "license": "Apache 2", | ||
| "views": 17, | ||
| "views": 19, | ||
| "category": "Phenomics", | ||
| "relations": [], | ||
| "keywords": [ | ||
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