Tools   Select Another Resource Report Type

http://www.tue.nl/en/

Proper citation: Eindhoven University of Technology; North Brabant; Netherlands (RRID:SCR_003354) Copy   

•   Source: SciCrunch Registry
  

https://neuinfo.org/mynif/search.php?q=nlx_149462&t=indexable&list=cover&nif=nlx_144509-1

A virtual database that indexes both BioNOT for negation data, and the Resource Discovery Pipeline: an automated resource discovery and semi-automated type characterization with text-mining scripts that facilitate curation team efforts to discover, integrate and display new content. This virtual database currently indexes the following resources: * BioNOT, http://snake.ims.uwm.edu/bionot/index.php?searchterm=mecp2+autism&submit=Search * Resource Discovery Pipeline, http://lucene1.neuinfo.org/nif_resource/current/

Proper citation: Integrated Auto-Extracted Annotation (RRID:SCR_005892) Copy   

•   Source: SciCrunch Registry
  

http://www.t1diabetes.nih.gov/T1D-PTP/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. Investigator access is provided to the established facilities and expertise needed to extend, enhance and validate preclinical studies of promising new therapeutics in cases where additional preclinical testing is needed to validate potential therapies under disease-specific conditions and in multiple animal models before therapeutics can enter the Type 1 Diabetes Rapid Access to Intervention Development (T1D-RAID) development pipeline. The T1D-RAID program provides resources for pre-clinical development of drugs, natural products, and biologics that will be tested as new therapeutics in type 1 diabetes clinical trials. The T1D-RAID program is not currently accepting applications. The T1D-PTP program currently supports two contracts, which are separate from each other and from the T1D-RAID NCI contract resources, to assist in preclinical development of therapeutics for T1D: * Agents to be tested for Preclinical Efficacy in Prevention or Reversal of Type 1 Diabetes in Rodent Models. Type 1 Diabetes Preclinical Testing Program (T1D-PTP) (NOT-DK-09-006) * Needs for Preclinical Efficacy Testing of Promising Agents to Prevent or Reverse Diabetic Complications (NOT-DK-09-009) The T1D-RAID and T1D-PTP are programs intended to remove the most common barriers to progress in identification and development of new therapies for Type 1 Diabetes. The common goal of these programs is to support and provide for the preclinical work necessary to obtain proof of principle establishing that a new molecule or novel approach will be a viable candidate for expanded clinical evaluation.

Proper citation: Type 1 Diabetes Preclinical Testing Program (RRID:SCR_006861) Copy   

•   Source: SciCrunch Registry
  

http://rsync.samba.org/

Software that provides rapid incremental file transfer.

Proper citation: rsync (RRID:SCR_003113) Copy   

•   Source: SciCrunch Registry
  

http://www.cs.tau.ac.il/~shlomito/tissue-net/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. Network visualizations in which the expression and predicted flux data are projected over the global human network. These network visualizations are accessible through the supplemental website using the publicly available Cytoscape software (Cline, Smoot et al. 2007). Since many high degree nodes exist in the network, special layouts are required to produce network visualizations that are readily interpretable. To this end we produced network visualizations in which hub nodes are repeated multiple times and hence layouts with a small number of edge crossings can be generated. Contains entries for brain compartments and brain pathways.

Proper citation: Network-based Prediction of Human Tissue-specific Metabolism (RRID:SCR_007392) Copy   

•   Source: SciCrunch Registry
  

http://harvard.eagle-i.net/i/0000012e-3517-ac53-550e-f59280000000

Core facility that provides metals analytical capabilities to biomedical and non-biomedical researchers and serves as a source for study design consultation and sample QA/QC requirements. The transport, fate, exposure, and toxic effects of heavy metals is a primary focus of research at the Center. It operates as a modified fee-for-service laboratory. Researchers have the option of having the samples run by the Service staff, or of receiving instruction (for themselves or a doctoral or post doctoral trainee) on how to operate the analytical equipment and analyze their own samples. Both options have associated fees and, as with other services, facility access funds can be requested internal or external services when individual grant support is not yet available.

Proper citation: HSPH Trace Metals Laboratory (RRID:SCR_002819) Copy   

•   Source: SciCrunch Registry
  

http://www.sci.unisannio.it/docenti/rampone/

Data set of Homo Sapiens Exons, Introns and Splice regions extracted from GenBank Rel.123 with an aim of giving standardized material to train and to assess the prediction accuracy of computational approaches for gene identification and characterization. From the complete GenBank (Primate Sequences Division) Rel.123 (162,557 entries), entries of Human Nuclear DNA including Complete CDS and more than one Exon have been selected, and 4523 exons and 3802 introns have been extracted from these entries. Details about extracted exons and introns are reported (Locus, number, Start and End position in the entry, sequence, length, G+C content, presence of not AGCT data (nucleotide scan check)). Statistics are also reported (overall nucleotides, average G+C content, nucleotide scan check results, number of not GT starting / AG ending introns, minimum / maximum / average length, length standard deviation). 3799+3799 donor and acceptor sites, as windows of 140 nucleotides around each splice site have been extracted. After discarding sequences not including canonical GTAG junctions (65+74), including insufficient data (not enough material for a 140 nucleotide window) (686+589), including not AGCT bases (29+30), and redundant (218+226) there are 2796+ 2880 windows. Finally, there are 271,937 + 332,296 windows of false splice sites, selected by searching canonical GTAG pairs in not splicing positions. The false sites in a range of +/- 60 from a true splice site are marked as proximal.

Proper citation: HS3D - Homo Sapiens Splice Sites Dataset (RRID:SCR_002939) Copy   

•   Source: SciCrunch Registry
  

http://www.unipi.it/english/

Proper citation: University of Pisa; Pisa; Italy (RRID:SCR_006616) Copy   

•   Source: SciCrunch Registry
  

http://depts.washington.edu/yeastrc/

Biomedical technology research center that (1) exploits the budding yeast Saccharomyces cerevisiae to develop novel technologies for investigating and characterizing protein function and protein structure (2) facilitates research and extension of new technologies through collaboration, and (3) actively disseminates data and technology to the research community. Through collaboration, the YRC freely provides resources and expertise in six core technology areas: Protein Tandem Mass Spectrometry, Protein Sequence-Function Relationships, Quantitative Phenotyping, Protein Structure Prediction and Design, Fluorescence Microscopy, Computational Biology.

Proper citation: Yeast Resource Center (RRID:SCR_007942) Copy   

•   Source: SciCrunch Registry
  

http://noble.gs.washington.edu/proj/charge/

Charge Czar is a software tool that uses a support vector machine to discriminate between +2- and +3-charged tandem mass spectra, with the goal of reducing database search time by eliminating the need to search twice with each spectrum. Charge Czar is written in Python and ANSI C. Source code for the latest version, as well as some pre-compiled versions for popular platforms (Linux, Cygwin) can be downloaded after you have agreed to the license agreement. Mass spectrometry is a particularly useful technology for the rapid and robust identification of peptides and proteins in complex mixtures. Peptide sequences can be identified by correlating their observed tandem mass spectra (MS/MS) with theoretical spectra of peptides from a sequence database. Unfortunately, to perform this search the charge of the peptide must be known, and current charge-state-determination algorithms only discriminate singly- from multiply-charged spectra: distinguishing +2 from +3, for example, is unreliable. Thus, search software is forced to search multiply-charged spectra multiple times. To minimize this inefficiency, we present a support vector machine (SVM) that quickly and reliably classifies multiply-charged spectra as having either a +2 or +3 precursor peptide ion. By classifying multiply-charged spectra, we obtain a 40% reduction in search time while maintaining an average of 99% of peptide and 99% of protein identifications originally obtained from these spectra.

Proper citation: Charge Czar: Peptide charge state determination for low-resolution tandem mass spectra (RRID:SCR_004315) Copy   

•   Source: SciCrunch Registry
  

http://www.physionet.org/physiobank/database/sleep-edf/

Sleep EEG dataset from 8 subjects in European Data Format (EDF) including original recordings and their hypnograms as described in B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Obery��. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000). The recordings were obtained from Caucasian males and females (21 - 35 years old) without any medication; they contain horizontal EOG, FpzCz and PzOz EEG, each sampled at 100 Hz. The sc* recordings also contain the submental-EMG envelope, oro-nasal airflow, rectal body temperature and an event marker, all sampled at 1 Hz. The st* recordings contain submental EMG sampled at 100 Hz and an event marker sampled at 1 Hz. The 4 sc* recordings were obtained in 1989 from ambulatory healthy volunteers during 24 hours in their normal daily life, using a modified cassette tape recorder. The 4 st* recordings were obtained in 1994 from subjects who had mild difficulty falling asleep but were otherwise healthy, during a night in the hospital, using a miniature telemetry system with very good signal quality.

Proper citation: Sleep-EDF Database (RRID:SCR_006976) Copy   

•   Source: SciCrunch Registry
  

http://ftp://ftp.informatics.jax.org/pub/reports/MGI_PhenotypicAllele.rpt

Data set of collected and annotated expression and activity data for recombinase-containing transgenes and knock-in alleles. As the authoritative source of official names for mouse genes, alleles, and strains, MGI makes this list of transgenes available as a service and includes all known transgenes and synonyms. NIF provides a database interface so that researchers may have a better idea whether the trangene or transgenic animal that they are searching for is available.
Nomenclature follows the rules and guidelines established by the International Committee on Standardized Genetic Nomenclature for Mice.

Proper citation: Mouse Genome Informatics Transgenes (RRID:SCR_003468) Copy   

•   Source: SciCrunch Registry
  

http://www.gesis.org/allbus/

Data set of up-to-date data on attitudes, behavior, and social structure in Germany. Every two years since 1980 a representative cross section of the population is surveyed using both constant and variable questions. The ALLBUS data become available to interested parties for research and teaching as soon as they are processed and documented.

Proper citation: ALLBUS - German General Social Survey (RRID:SCR_003588) Copy   

•   Source: SciCrunch Registry
  

http://alchemy.sourceforge.net/

ALCHEMY is a genotype calling algorithm for Affymetrix and Illumina products which is not based on clustering methods. Features include explicit handling of reduced heterozygosity due to inbreeding and accurate results with small sample sizes. ALCHEMY is a method for automated calling of diploid genotypes from raw intensity data produced by various high-throughput multiplexed SNP genotyping methods. It has been developed for and tested on Affymetrix GeneChip Arrays, Illumina GoldenGate, and Illumina Infinium based assays. Primary motivations for ALCHEMY''s development was the lack of available genotype calling methods which can perform well in the absence of heterozygous samples (due to panels of inbred lines being genotyped) or provide accurate calls with small sample batches. ALCHEMY differs from other genotype calling methods in that genotype inference is based on a parametric Bayesian model of the raw intensity data rather than a generalized clustering approach and the model incorporates population genetic principles such as Hardy-Weinberg equilibrium adjusted for inbreeding levels. ALCHEMY can simultaneously estimate individual sample inbreeding coefficients from the data and use them to improve statistical inference of diploid genotypes at individual SNPs. The main documentation for ALCHEMY is maintained on the sourceforge-hosted MediaWiki system. Features * Population genetic model based SNP genotype calling * Simultaneous estimation of per-sample inbreeding coefficients, allele frequencies, and genotypes * Bayesian model provides posterior probabilities of genotype correctness as quality measures * Growing number of scripts and supporting programs for validation of genotypes against control data and output reformating needs * Multithreaded program for parallel execution on multi-CPU/core systems * Non-clustering based methods can handle small sample sets for empirical optimization of sample preparation techniques and accurate calling of SNPs missing genotype classes ALCHEMY is written in C and developed on the GNU/Linux platform. It should compile on any current GNU/Linux distribution with the development packages for the GNU Scientific Library (gsl) and other development packages for standard system libraries. It may also compile and run on Mac OS X if gsl is installed.

Proper citation: ALCHEMY (RRID:SCR_005761) Copy   

•   Source: SciCrunch Registry
  

http://www.ifs.org.uk/ELSA

An interdisciplinary data resource on health, economic position and quality of life as people age. Longitudinal multidisciplinary data from a representative sample of the English population aged 50 and older have been collected. Both objective and subjective data are collected relating to health and disability, biological markers of disease, economic circumstance, social participation, networks and well-being. Participants are surveyed every two years to see how people''s health, economic and social circumstances may change over time. One of the study''s aims is to determine the relationships between functioning and health, social networks, resources and economic position as people plan for, move into and progress beyond retirement. It is patterned after the Health and Retirement Study, a similar study based in the United States. ELSA''s method of data collection includes face-to-face interview with respondents aged 50+; self-completion; and clinical, physical, and performance measurements (e.g., timed walk). Wave 2 added questions about quality of health care, literacy, and household consumption, and a visit by a nurse to obtain anthropometric, blood pressure, and lung function measurements, as well as saliva and blood samples, and to record results from tests of balance and muscle strength. Another new aspect of Wave 2 is the ''Exit Interview'' carried out with proxy informants to collect data about respondents who have died since Wave 1. This interview includes questions about the respondents'' physical and psychological health, the care and support they received, their memory and mood in the last year of their life, and details of what has happened to their finances after their death. Wave 3 data added questions related to mortgages and pensions. The intention is to conduct interviews every 2 years, and to have a nurse visit every 4 years. It also is envisioned that the ELSA data will ultimately be linked to available administrative data, such as death registry data, a cancer register, NHS hospital episodes data, National Insurance contributions, benefits, and tax credit records. The survey data are designed to be used for the investigation of a broad set of topics relevant to understanding the aging process. These include: * health trajectories, disability and healthy life expectancy; * the determinants of economic position in older age; * the links between economic position, physical health, cognition and mental health; * the nature and timing of retirement and post-retirement labour market activity; * household and family structure, social networks and social supports; * patterns, determinants and consequences of social, civic and cultural participation; * predictors of well-being. Current funding for ELSA will extend the panel to 12 years of study, giving significant potential for longitudinal analyses to examine causal processes. * Dates of Study: 2002-2007 * Study Features: Longitudinal, International, Anthropometric Measures * Sample Size: ** 2000-2003 (Wave 1): 12,100 ** 2004-2005 (Wave 2): 9,433 ** 2006-2007 (Wave 3): 9,771 ** 2008-2009 (Wave 4): underway Links * Economic and Social Data Service (ESDS): http://www.esds.ac.uk/longitudinal/about/overview.asp * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00139#scope-of-study

Proper citation: English Longitudinal Study of Ageing (RRID:SCR_006727) Copy   

•   Source: SciCrunch Registry
  

http://sv.gersteinlab.org/breakdb/

Data set developed to store, annotate and dsplay structural variant (SV) breakpoint events identified by PEMer and from other sources.

Proper citation: BreakDB (RRID:SCR_003134) Copy   

•   Source: SciCrunch Registry
  

http://ewww.kumamoto-u.ac.jp/

Proper citation: Kumamoto University; Kumamoto; Japan (RRID:SCR_004102) Copy   

•   Source: SciCrunch Registry
  

http://www.unisa.it/english/index

Proper citation: University of Salerno; Salerno; Italy (RRID:SCR_007853) Copy   

•   Source: SciCrunch Registry
  

http://www.usciences.edu/default.aspx

Proper citation: University of Sciences in Philadelphia; Pennsylvania; USA (RRID:SCR_008023) Copy   

•   Source: SciCrunch Registry
  

http://memory.psych.upenn.edu/Electrophysiological_Data

Multiple data sets, and associated publications, of electrophysiological data from the Computational Memory Lab, University of Pennsylvania. Separate requests must be made for each dataset. A collection of behavioral testing data is also available.

Proper citation: Cognitive Electrophysiology Data Portal (RRID:SCR_003129) Copy   

•   Source: SciCrunch Registry