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https://github.com/aametwally/Metabolic_Subphenotype_Predictor

Software repository contains code for Inference of T2D metabolic subphenotypes (MuscleIR, Beta-cell Function, Incretin Effect, Hepatic IR), Identification of dominant metabolic subphenotype, Feature extraction from glucose tiemseries, Extraction of reduced representation of glucose tiemseries,Visualization of metabolic phenotypes based on various glucose-related metrics,Concordance between CGM and Venous glucose values from at home and at clinical setting, Classification of metabolic subphenotypes.

Proper citation: Metabolic Subphenotype Predictor (RRID:SCR_027192) Copy   

•   Source: SciCrunch Registry

https://github.com/SciCrunch/Antibody-Watch

Text mining antibody specificity from literature. Helps researchers identify potential problems with antibody specificity. By mining the scientific literature and linking findings to Research Resource Identifiers (RRIDs), it provides alerts on antibodies that may yield unreliable results, supporting reproducibility in biomedical research.

Proper citation: Antibody Watch (RRID:SCR_027424) Copy   

•   Source: SciCrunch Registry

https://spin.niddk.nih.gov/bax/software/TALOS-N/

Software package for prediction of protein backbone and sidechain torsion angles from NMR chemical shifts.

Proper citation: TALOS-N (RRID:SCR_022800) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/Maaslin2.html

SoftwareR package that identifies microbial taxa correlated with factors of interest using generalized linear models and mixed models.Used for efficiently determining multivariable association between clinical metadata and microbial meta'omic features.

Proper citation: MaAsLin2 (RRID:SCR_023241) Copy   

•   Source: SciCrunch Registry

https://github.com/ParkerLab/ataqv

Software package for QC and visualization of ATAC-seq results. Used to examine aligned reads and report basic metrics, including reads mapped in proper pairs, optical or PCR duplicates, reads mapping to autosomal or mitochondrial references, ratio of short to mononucleosomal fragment counts, mapping quality, various kinds of problematic alignments.

Proper citation: ataqv (RRID:SCR_023112) Copy   

•   Source: SciCrunch Registry

https://github.com/qianli10000/mtradeR

Software R package implements Joint model with Matching and Regularization and simulation pipeline. Used to test association between taxa and disease risk, and adjusted for correlated taxa screened by pre-selection procedure in abundance and prevalence, individually.

Proper citation: mtradeR (RRID:SCR_022977) Copy   

•   Source: SciCrunch Registry

https://sdrc.stanford.edu/

University-affiliated center that promotes research in diabetes and related metabolic and endocrine disorders at Stanford University.

Proper citation: Stanford Diabetes Research Center (RRID:SCR_015856) Copy   

•   Source: SciCrunch Registry

Ratings or validation data are available for this resource

https://github.com/BodenmillerGroup/imctools

Software Python package that implements preprocessing pipeline for imaging mass cytometry data. Can convert IMC raw files to tiff files that are used as inputs into CellProfiller, Ilastik, Fiji etc.

Proper citation: imctools (RRID:SCR_017132) Copy   

•   Source: SciCrunch Registry

https://github.com/caleblareau/mgatk

Software python-based command line interface for processing .bam files with mitochondrial reads and generating high-quality heteroplasmy estimation from sequencing data. This package places a special emphasis on mitochondrial genotypes generated from single-cell genomics data, primarily mtscATAC-seq, but is generally applicable across other assays.

Proper citation: mgatk (RRID:SCR_021159) Copy   

•   Source: SciCrunch Registry

https://pancreatlas.org/

Collection of human pancreas data and images. Platform to share data from human pancreas samples. Houses reference datasets from human pancreas samples, achieved through generosity of organ donors and their families.

Proper citation: Pancreatlas (RRID:SCR_018567) Copy   

•   Source: SciCrunch Registry

https://gitlab.com/rosen-lab/white-adipose-atlas

Single cell atlas of human and mouse white adipose tissue.

Proper citation: White Adipose Atlas (RRID:SCR_023625) Copy   

•   Source: SciCrunch Registry

http://www.diacomp.org

Consortium serving the diabetic complications community that sponsors annual meetings in complications-relevant scientific areas, solicits and funds pilot projects in high impact areas of complications research, and provides resources and data including animal models, protocols and methods, validation criteria, reagents and resources, histology, publications and bioinformatics for researchers conducting diabetic complications research.

Proper citation: Diabetic Complications Consortium (RRID:SCR_001415) Copy   

•   Source: SciCrunch Registry

http://www.cure.med.ucla.edu/

Center whose interests and activities encompass several facets of gastrointestinal regulatory physiology and cell biology. It provides an infrastructure to support basic, translational and clinical research and to facilitate interdisciplinary research and training activities in digestive diseases.

Proper citation: CURE - Digestive Diseases Research Center (RRID:SCR_004238) Copy   

•   Source: SciCrunch Registry

http://dkcoin.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented October 13, 2014. The resource has moved to the NIDDKInformation Network (dkNET) project. Contact them at info_at_dknet.org with any questions. Database of large pools of data relevant to the mission of NIDDKwith the goal of developing a community-based network for integration across disciplines to include the larger DKuniverse of diseases, investigators, and potential users. The focus is on greater use of this data with the objective of adding value by breaking down barriers between sites to facilitate linking of different datasets. To date (2013/06/10), a total of 1,195 resources have been associated with one or more genes. Of 11,580 total genes associated with resources, the ten most represented are associated with 359 distinct resources. The main method by which they currently interconnect resources between the providers is via EntrezGene identifiers. A total of 780 unique genes provide the connectivity between 3,159 resource pairs across consortia. To further increase interconnectivity, the groups have been further annotating their data with additional gene identifiers, publications, and ontology terms from selected Open Biological and Biomedical Ontologies (OBO).

Proper citation: dkCOIN (RRID:SCR_004438) Copy   

•   Source: SciCrunch Registry

https://repository.niddk.nih.gov/study/21

Data and biological samples were collected by this consortium organizing international efforts to identify genes that determine an individual risk of type 1 diabetes. It originally focused on recruiting families with at least two siblings (brothers and/or sisters) who have type 1 diabetes (affected sibling pair or ASP families). The T1DGC completed enrollment for these families in August 2009. They completed enrollment of trios (father, mother, and a child with type 1 diabetes), as well as cases (people with type 1 diabetes) and controls (people with no history of type 1 diabetes) from populations with a low prevalence of this disease in January 2010. T1DGC Data and Samples: Phenotypic and genotypic data as well as biological samples (DNA, serum and plasma) for T1DGC participants have been deposited in the NIDDKCentral Repositories for future research.

Proper citation: Type 1 Diabetes Genetics Consortium (RRID:SCR_001557) Copy   

•   Source: SciCrunch Registry

https://iscconsortium.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Consortium to advance the understanding of intestinal epithelial stem cell biology during development, homeostasis, regeneration and disease. Its immediate goals are to isolate, characterize, culture and validate populations of intestinal stem cells; answer major questions in stem cell biology of the intestinal epithelium; and accelerate research by making information and resources available to the research community. Resources include data sets, protocols, and a resource catalog. Long-term goals include: 1) laying the ground work for therapeutic manipulation of the intestinal epithelium 2) contributing to the greater understanding of stem cell biology through knowledge of the intestine as a model stem cell-driven system. Research Projects are housed at 8 institutions across the nation: Oregon Health & Science University, Stanford University, Stowers Institute for Medical Research, University of California, Los Angeles School of Medicine (UCLA) (partnered with the VA Greater Los Angeles), University of North Carolina, Chapel Hill (UNC), University of Oklahoma, University of Pennsylvania, and University of Pittsburgh.

Proper citation: Intestinal Stem Cell Consortium (RRID:SCR_001555) Copy   

•   Source: SciCrunch Registry

http://type1diabetes.jax.org/

International repository for importation, curation, genotypic and phenotypic validation, cryopreservation, and distribution of mouse stocks of value to the type 1 diabetes scientific community holding over 250 genetically modified or congenic mouse stocks that are being used to dissect genetic and biologic features of T1D. They provide extensive genotypic and phenotypic quality control and genetic stabilization for these strains, as well as incidence studies when available. An added value of T1DR stocks is their ability to propel advances in related areas of science, including research in non-T1D autoimmunity and infectious diseases. The staff provides information and technical assistance regarding selection and use of existing T1DR models, and will provide limited support for development of new models considered to be of high-value for the T1D community. The resource includes strains generated at the Jackson Laboratory as well as strains donated by external scientists. Investigators are highly encouraged to donate a strain to ensure its preservation and availability to other researchers.

Proper citation: Type 1 Diabetes Resource (RRID:SCR_001475) Copy   

•   Source: SciCrunch Registry

http://www.diabetestrialnet.org/

International network of researchers who are exploring ways to prevent, delay and reverse the progression of type 1 diabetes. It is conducting clinical trials with researchers from 18 Clinical Centers in the United States, Canada, Finland, United Kingdom, Italy, Germany, Australia and New Zealand. In addition, more than 150 medical centers and physician offices are participating in the TrialNet network. Studies are available for people newly diagnosed with type 1 diabetes, as well as for relatives of people with type 1 diabetes who are at greater risk of developing the disease. This NIH-sponsored clinical trials network conducts studies designed to evaluate new approaches to prevent or ameliorate type 1 diabetes specifically by interdicting the type 1 diabetes disease process. These include interventions designed to decrease beta-cell destruction and/or enhance beta-cell survival. Studies are conducted in non-diabetic persons at risk of type 1 diabetes in an effort to delay the development of type 1 diabetes as a clinical disease; or (if initiated prior to appearance of autoimmunity) in an effort to delay the appearance of autoimmunity; or in individuals with type 1 diabetes who are either newly diagnosed or have evidence of sustained beta cell function. Studies include long-term follow-up of subjects developing type 1 diabetes. The TrialNet network also supports natural history and genetics studies in populations screened for or enrolled in studies conducted by the TrialNet study group. In addition, TrialNet will evaluate methodologies that enhance the conduct of clinical trials interdicting the type 1 diabetes disease process.

Proper citation: Type 1 Diabetes TrialNet (RRID:SCR_001508) Copy   

•   Source: SciCrunch Registry

http://www.statepi.jhsph.edu/ckid/

Prospective, observational cohort study of children with mild to moderate chronic kidney disease (CKD) to: (1) determine risk factors for progression of pediatric chronic kidney disease (CKD); (2) examine the impact of CKD on neurocognitive development; (3) examine the impact of CKD on risk factors for cardiovascular disease, and; (4) examine the impact of CKD on growth. The CKiD study population will include a cohort of 540 children, age 1 16 years, expected to be enrolled over a 24-month period.

Proper citation: CKID A Prospective Cohort Study of Kidney Disease in Children (RRID:SCR_001500) Copy   

•   Source: SciCrunch Registry

http://www.citregistry.org/

Collect, analyze, and communicate on comprehensive and current data on all islet/beta cell transplants in human recipients performed in North America, as well as some European and Australian centers to expedite progress and promote safety in islet/beta cell transplantation. This site serves as a repository for general information concerning protocols, clinical transplantation sites, publications, and other information of interest to the general community. Annual Reports are available. Islet/beta cell transplantation is a complex procedure with many factors contributing to the outcome. Compiling and analyzing data from all transplant centers in the US, Canada, as well as some European and Australian centers will accelerate the identification of both critical risk factors and key determinants of success and thereby guide transplant centers in developing and refining islet/beta cell transplant protocols. The inclusion of the term collaborative in the name of the Registry emphasizes the importance of collaboration in fulfilling the CITR mission and goals. Close collaboration with the transplant centers will ensure that relevant questions are addressed, that data submitted are accurate and complete, and that the needs of the transplant community are served. Information on how to participate as a CITR Transplant Center and to receive a transplant center application is available through the website. Progress in islet transplantation depends entirely on complete, high-quality medical data, including the information patients consented to report to the Collaborative Islet Transplant Registry. To make it as easy as possible to provide updated information about patient's health, an on-line questionnaire is available or patients can mail it to their transplant center. This information is very important in the continuing search for a cure for Type 1 diabetes.

Proper citation: Collaborative Islet Transplant Registry (RRID:SCR_001466) Copy   

•   Source: SciCrunch Registry