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351 results for “Case control studies”
Inter-Chemical Correlation results for the study: HHEARx2016-1534 (A Nested Case-Control Study of Prenatal Exposure to Phthalates and Psychosocial Stress: Adverse Pregnancy Outcomes and the Mediating Role of Placental Function)
Title: A Nested Case-Control Study of Prenatal Exposure to Phthalates and Psychosocial Stress: Adverse Pregnancy Outcomes and the Mediating Role of Placental Function <br>Species: Homo sapiens <br>Number of samples: 5789 <br>Number of named analytes: 17 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=14 <br>
A Case-Control Study to Measure Behavioral Risks of Malware Encounters in Organizations
<p>The behavior of enterprise users (e.g. browsing at night or visiting gambling sites) is a potential factor that might increase the chances of malware encounters (e.g. coinminers vs ransomware) on the field. This dataset report the aggregated results of a case-control study on telemetry data collected by Trend Micro, a global cybersecurity vendor, to identify users’ behavioral characteristics that can be used to differentiate cybersecurity risks profiles.</p>
Supplementary material for "Learning Boolean controls in regulated metabolic networks: a case-study"
<p>This record contains notebooks and Docker image for reproducing the learning of Boolean controls in regulated metabolic networks, and the case study presented in the CMSB 2021 conference proceeding article "Learning Boolean controls in regulated metabolic networks: a case-study".</p> <p>Visualize notebooks online:</p> <ul> <li><a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/5060985/files/CaseStudy-Simulations.ipynb">CaseStudy-Simulations.ipynb</a></li> <li><a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/5060985/files/CaseStudy-SearchSpace.ipynb">CaseStudy-SearchSpace.ipynb</a></li> <li><a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/5060985/files/CaseStudy-Inference.ipynb">CaseStudy-Inference.ipynb</a></li> </ul> <p>Notebooks can be executed interactively within the Docker image <code>bioasp/boolean-caspo-flux:cmsb2021 </code>which extends the <a href="http://colomoto.org/notebook">CoLoMoTo Docker</a> version <code>2021-02-01.</code></p> <p>Alternatively, they can be executed without any installation at <a href="https://mybinder.org/v2/zenodo/10.5281/zenodo.5070151/">https://mybinder.org/v2/zenodo/10.5281/zenodo.5070151/</a>.</p> <p>The Docker image can be executed as follows:</p> <pre><code class="language-bash">docker pull bioasp/boolean-caspo-flux:cmsb2021 docker run -it --rm -p 8888:8888 bioasp/boolean-caspo-flux:cmsb2021 </code></pre> <p>then point your browser to <a href="http://127.0.0.1:8888">http://127.0.0.1:8888</a>.</p> <p>The image can be imported using the command <code>docker load</code> with the image file provided in this record:</p> <pre><code>docker load -i image.tar.gz</code></pre> <p>or with the <code>donodo</code> command available at <a href="https://github.com/pauleve/donodo">https://github.com/pauleve/donodo</a>:</p> <pre><code>pip install -U donodo donodo pull 10.5281/zenodo.5070151</code></pre>
Prospective with historical control, case-matched cohort study of the tertiary survey beneficial in critically severe trauma patients.
<p>Raw data, Tertiary survey record form, and STROBE checklist of "Prospective with historical control, case-matched cohort study of the tertiary survey beneficial in critically severe trauma patients" study.</p>
Data in support of "An exploration of linkage fine-mapping on sequences from case-control studies"
<p>These data were simulated for an exploration of linkage fine-mapping on sequences from case-control studies. The code to generate and analyze the data is available on GitHub in the scripts at <a href="https://github.com/SFUStatgen/PBJ0">https://github.com/SFUStatgen/PBJ0</a>. Queries may be directed to Payman Nickchi at <a href="mailto:pnickchi@sfu.ca">pnickchi@sfu.ca</a> or Charith (Bhagya) Karunarathna at <a href="mailto:ch757276@dal.ca">ch757276@dal.ca</a>.</p>
Hyperglycemia and steroid use increase the risk of rhino-orbito-cerebral mucormycosis regardless of COVID-19 hospitalization: Case-control study, India
<p><strong>Abstract</strong></p> <p><strong><em>BACKGROUND</em></strong></p> <p>In the context of the ongoing COVID-19 pandemic increased incidence of ROCM was noted in India, among those infected with COVID. We determined risk factors for rhino-orbito-cerebral mucormycosis (ROCM) post Coronavirus disease 2019 (COVID-19) among those never and ever hospitalized for COVID-19 separately through a multi-centric, hospital-based, unmatched case-control study across India.</p> <p><strong><em>METHODS</em></strong></p> <p>We defined cases and controls as those with and without post-COVID ROCM, respectively. We compared their socio-demographics, comorbidities, steroid use, glycaemic status, and practices. We calculated crude and adjusted odds ratio (AOR) with 95% confidence intervals (CI) through logistic regression. The covariates with p-value for crude OR of less 0·20 were considered for the regression model.</p> <p><strong><em>RESULTS</em></strong></p> <p>Among hospitalised, we recruited 267 cases and 256 controls and 116 cases and 231 controls among never hospitalised. Risk factors (AOR; 95% CI) for post-COVID ROCM among the hospitalised were age 45-59 years (2·1; 1·4 to 3·1), having diabetes mellitus (4·9; 3·4 to 7·1), elevated plasma glucose (6·4; 2·4 to 17·2), steroid use (3·2; 2 to 5·2) and frequent nasal washing (4·8; 1·4 to 17). Among those never hospitalised, age ≥ 60 years (6·6; 3·3 to 13·3), having diabetes mellitus (6·7; 3·8 to 11·6), elevated plasma glucose (13·7; 2·2 to 84), steroid use (9·8; 5·8 to 16·6), and cloth facemask use (2·6; 1·5 to 4·5) were associated with increased risk of post-COVID ROCM.</p> <p><strong><em>CONCLUSIONS</em></strong></p> <p>Hyperglycemia irrespective of having diabetes mellitus and steroid use was associated with increased risk of ROCM independent of COVID-19 hospitalisation. Rational steroid usage and glucose monitoring may reduce the risk of post-COVID.</p>
Data in support of "An exploration of linkage fine-mapping on sequences from case-control studies"
<p>These data were simulated for an exploration of linkage fine-mapping on sequences from case-control studies. The scripts to generate and analyze the data are available at <a href="https://github.com/SFUStatgen/PBJ0">https://github.com/SFUStatgen/PBJ0</a>. Queries may be directed to Payman Nickchi at <a href="mailto:pnickchi@sfu.ca">pnickchi@sfu.ca</a> or Charith (Bhagya) Karunarathna at <a href="mailto:ch757276@dal.ca">ch757276@dal.ca</a>.</p> <p><strong>README file for All_data directory</strong></p> <p><strong>Directory structure</strong></p> <p>The All_data directory consists of this README file and 500 sub-directories named DatasetX, for X=1 to 500. Within each DatasetX sub-directory are further sub-directories named alt and null containing files named pop_data.RData and sample_data.RData.</p> <p><strong>alt <em>versus</em> null directories</strong></p> <p>The files in the alt and null directories contain the same variant data but different phenotype data. In particular, under the null hypothesis, disease status is simulated at random according to a 5% prevalence in the population, whereas under the alternative hypothesis disease status is simulated according to a penetrance model that depends on causal SNVs. The R script to simulate data<br> under the alternative hypothesis is in the file 1_SimulateData.R in the Github repository <a href="https://github.com/SFUStatgen/PBJ0">https://github.com/SFUStatgen/PBJ0</a>.</p> <p><strong>pop_data.RData and sample_data.RData files</strong></p> <p>The data structures contained in the pop_data.RData and sample_data.RData files are described below. The structure is the same under both the null and alternative hypothesis.</p> <p><strong>pop_data.RData</strong></p> <p>From R, load("pop_data.RData") loads a list named pop_data whose elements describe the population’s haplotype and phenotype data. The list elements are as follows.</p> <ul> <li>Variants: a matrix of variants for the population of 6200 haplotypes <ul> <li>rows are SNVs,</li> <li>columns are sequences</li> </ul> </li> <li>Positions: a data frame of SNV positions <ul> <li>rows are SNVs,</li> <li>column 1 is the SNV name and column 2 is the SNV position in base pairs</li> </ul> </li> <li>Population.Mapping: a data frame telling us how the sequences are paired into individuals <ul> <li>rows are individuals</li> <li>First column 1 is an individual ID from 1,…,3100; columns 2 and 3 are the sequence IDs of the first and second sequence for that individual where the sequence IDs are the column names of the Variants matrix.</li> </ul> </li> <li>Genotype.Matrix: a matrix of genotypes (i.e. variant counts) for the 3100 individuals <ul> <li>rows are SNVs</li> <li>columns are the individuals</li> </ul> </li> <li>causal_region: a vector containing the lower- and upper-limit of the causal region in base pairs.</li> <li>cSNV: a vector containing the IDs of the causal SNVs, where the SNV IDs are the row names of the Variants matrix.</li> <li>DISCRETE: a list with the following elements. <ul> <li>CaseIndividuals: vector of IDs of the affected individuals in the population.</li> <li>ControlIndividuals: vector of IDs of the unaffected in the population.</li> <li>BinaryTrait: a vector of trait status (0=unaffected, 1=affected) for each individual.</li> </ul> </li> </ul> <p><strong>Note:</strong> Within the same DatasetX directory, the only difference between the pop_data data structures under the null and alternative hypothesis is the phenotype information contained in their respective DISCRETE list elements. Both the null and alternative pop_data data structure share list elements: Variants, Positions, Population.Mapping, Genotype.Matrix, causal_region and cSNV.</p> <p><strong>sample_data.RData</strong></p> <p>From R, load("sample_data.RData") loads a list whose elements describe the sequences and phenotypes of the sample of 50 affected individuals (cases) and 50 unaffected individuals (controls) from the population.</p> <ul> <li>Haps: a list with two elements. <ul> <li>sample_haps: a matrix of 200 sequences for the 50 cases and 50 controls. Rows are SNVs and columns are sequences, with the sequences of sampled cases appearing first (i.e. first 100 columns), followed by the sequences of sampled controls (i.e. last 100 columns). Sequences include only those SNVs that are polymorphic in the sample.</li> <li>ccStatus: a vector indicating the case/control status of the individual to which the sequence belongs, with case=1 and control=0.</li> </ul> </li> <li>Genos: a list with two elements. <ul> <li>sample_genos: a matrix of 100 genotypes for the 50 cases and 50 controls. Rows are SNVs and columns are genotypes, with genotypes of cases appearing first, followed by genotypes of controls.</li> <li>ccStatus: a vector indicating the case/control status of each individual, with case=1 and control=0.</li> </ul> </li> <li>Posn: a data frame of SNV positions for each SNV that is polymorphic in the sample. The first column is the SNV name and the second is the SNV position in base pairs. Posn is a subset of pop_data$Positions.</li> <li>poly_cSNV: a vector of IDs for causal SNVs that are polymorphic in the sample.</li> <li>CaseIND: a vector of individual IDs for the case individuals (see pop_data$Population.Mapping).</li> <li>ControlIND: a vector of individual IDs for the control individuals (see pop_data$Population.Mapping).</li> <li>CaseHapID: a vector of IDs for the sequences that belong to cases (see the sequence IDs in the column names of the matrix pop_data$Variants).</li> <li>ControlHapID: a vector of IDs for the sequences that belong to controls (see the sequence IDs in the column names of the matrix pop_data$Variants).</li> </ul> <p> </p>
Dataset from: "Assessment of depression, anxiety, and psychological symptoms in parents of pediatric palliative care patients: A single-center case-control study"
<p>The dataset comprises raw data from a case-control study that compared levels of depression, anxiety, and general psychological symptoms between parents of pediatric palliative care (PPC) patients and parents of healthy controls.</p> <p>Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), and Symptom Checklist-90 Revised (SCL-90-R) were employed to provide a comprehensive and nuanced understanding of the mental health challenges faced by parents.</p> <p>The designation "case" in the first column (A) represents the parents of PPC patients, while the designation "control" represents the control group. The columns labeled B to S present demographic characteristics data, while columns T to AR present inventory scores. Columns BB to GC, in turn, present the responses to the inventory items.</p> <p> </p> <p> </p>
Relative role of border restrictions, case finding and contact tracing in controlling SARS-CoV-2 in the presence of undetected transmission: a mathematical modelling study
<p>Data and code for publication on <em>Relative role of border restrictions, case finding and contact tracing in controlling SARS-CoV-2 in the presence of undetected transmission: a mathematical modelling study</em></p>
Contrasting association of Leptin receptor polymorphisms and haplotypes with polycystic ovary syndrome in Bahraini and Tunisian women: a case–control study
<p><span><b>Background</b>. This study examined the contribution of ethnicity to the association of leptin receptor gene (<i>LEPR)</i> genetic variants with polycystic ovary syndrome (PCOS) in Tunisian and Bahraini Arabic-speaking women.<b> </b></span></p> <p><span><b>Methods. </b>Subjects consisted of 320 women with PCOS, and 446 eumenorrhic women from Tunisia, and 242 women with PCOS and 238 controls from Bahrain. Genotyping of (exonic) rs1137100 and rs1137101 and (intronic) rs2025804 <i>LEPR</i> variants was done by allelic exclusion.<b> </b></span></p> <p><span><b>Results. </b>The minor allele frequencies of rs1137100 and rs1137101 were significantly different between PCOS cases and control women from Bahrain but not Tunisia, and <i>LEPR</i> rs1137101 was associated with increased PCOS susceptibility only in Bahraini subjects. Furthermore, rs1137100 was associated with decreased PCOS risk among Bahrainis under codominant and recessive models; rs1137100 was negatively associated with PCOS in Tunisians after controlling for testosterone. In addition, rs2025804 was associated with increased PCOS risk among Tunisian but not Bahraini women, after adjusting for key covariates. Negative correlation was seen between rs1137101 and triglycerides in Tunisians, while HOMA-IR and insulin correlated with rs2025804 and rs1137101 among Bahraini subjects, and rs1137101 correlated with estradiol and prolactin. Taking TAG haplotype as common, positive association of TAA and negative association of TGG haplotype with PCOS was seen among Bahraini women; no three-locus PCOS-associated haplotypes were found in Tunisians.<b> </b></span></p> <p><span><b>Conclusions. </b>T<span>his study is the first to demonstrate the contribution of ethnicity to the association of <i>LEPR</i> gene variants with PCOS</span>, thereby highlighting the significance of controlling for ethnicity in gene association investigations.</span></p>
Dataset Case-control study: Risk factors for sporadic non-pregnancy related listeriosis in Germany, 2012-2013
<p>The dataset contains data of a nationwide age-frequency matched case-control study in Germany, 2012-2013, which was performed to identify underlying conditions and foods asscociated with sporadic listeriosis. It covers anonymous sociodemografic information, information on underlying conditions, and >60 food items. Data for control subjects were obtained from a population-based random telephone sample, which was generated according to the method by Gabler and Häder and considered telephone numbers not registered in telephone books.</p>
Software Evolution and Quality Data from Controlled, Multiple, Industrial Case Studies
<p>This data was obtained from a controlled, multiple case study involving six professional developers and four real-life, industrial systems. The study was designed to control for the moderator factors: programmer skill, maintenance task and learning effect. The primary data set contains multiple sets of defects, in the form of reports (excel files) extracted from six issue tracking systems. The secondary data consists of a series of attributes extracted from the software systems (i.e., code smells) and their evolution (i.e., code churn), and a log specifying the dates on which developers worked on each of the systems/tasks, in the form of excel files. Details on the controlled, multiple case study can be found in the doctoral dissertation by Yamashita titled: "Assessing the Capability of Code Smells to Support Software Maintainability Assessments: Empirical Inquiry and Methodological Approach" (online) Available at: https://www.duo.uio.no/handle/10852/34525</p>
Comparison of the Birth Weights of Infants with Esophageal Atresia and Tracheoesophageal Fistula and Infants with Inguinal Hernia: A Retrospective Case–Control Study
<p>Esophageal atresia with tracheoesophageal fistula is the most common tracheoesophageal malformation. Amniotic fluid swallowing into the upper blind sack and aspiration were observed during fetal life. Tracheoesophageal fistula serves as a sideway passage of amniotic fluid into the gastrointestinal tract. Compared with inguinal hernia, gestational age adjusted birth weights were lower for infants with esophageal atresia. In esophageal atresia with tracheoesophageal fistula nutrition by aspirated amniotic fluid did not fully compensate the intrauterine growth deficit.</p>
Headache in workers. A matched case-control study
<p>Data utilized for the study to be published</p>
Effectiveness of seasonal malaria chemoprevention administered in a mass campaign in the Kedougou region of Senegal in 2016: a Case-control study
<p><strong><span>Context</span></strong></p> <p><span>Seasonal malaria chemoprevention (SMC) with Sulfadoxine-Pyrimethamine plus Amodiaquine (SPAQ) is a malaria prevention strategy recommended since 2012 by the World Health Organization (WHO) for children under 5 years of age. In Senegal, the scaling up of the SMC has started since 2013 in the south-eastern regions of the country with an extension of the target to 10 years old children. The scaling up of SMC requires a regular evaluation of the strategy as recommended by the WHO. This study was conducted to evaluate the effectiveness of SMC. </span></p> <p><strong><span>Methodology </span></strong></p> <p><span>A case-control study was conducted in some villages of the health districts of Saraya and Kedougou in the Kedougou region from July to December 2016. A "case" was a sick child, aged 3 months to 10 years, seen in consultation and with a positive RDT. The "control" was a child of the same age group with a negative RDT and living in the same compound as the case or in a neighbouring compound. Each case was matched with two controls. Exposure to SMC was assessed by interviewing the mothers/caretakers and by checking the SMC administration card.</span></p> <p><strong><span>Results</span></strong></p> <p><span>A total of 492 children, including 164 cases and 328 controls, were recruited for our study. Their mean ages were 5.32 (+/- 2.15) and 4.44 (+/-2.25) years for the cases and the controls respectively. Male children predominated in both cases (55.49%) and controls (51.22%) (p=</span><span>0.18)</span><span>. Net ownership was 85.80% among cases and 90.85% among controls (p=0.053). The proportion of controls who received SMC was higher than that of cases (98.17% vs 85.98%; (p=1.10<sup>-7</sup>)). The protective effectiveness of SMC was 89% </span><span>(IC 95% = 84–93%)</span><span> </span><span>(OR=0.11).</span></p> <p><strong><span>Conclusion </span></strong></p> <p><span>SMC is therefore an effective strategy in the control of malaria in children. Case-control studies are a good approach for monitoring the efficacy of drugs administered during SMC.</span></p>
Genome-wide association study Summary statistics of Invasive melanoma vs controls, In situ Melanoma vs controls and In situ vs invasive melanoma (case-case)
<p>Genome-wide association study Summary statistics of Invasive melanoma vs controls, In situ Melanoma vs controls and In situ vs invasive melanoma (case-case). The first GWAS meta-analysis combines GWAS summary statistics of invasive melanoma from UK Biobank (as of August 2022), FinnGen release 9, QSkin Sun and Health Study and The Queensland Study of Melanoma: environmental and genetic associations (Q-MEGA) study.</p> <p>The second GWAS meta-analysis combines GWAS summary statistics of in situ melanoma from UK Biobank (as of August 2022), FinnGen release 9, QSkin Sun and Health Study and The Queensland Study of Melanoma: environmental and genetic associations (Q-MEGA) study.</p> <p>The third GWAS meta-analysis combines GWAS summary statistics of in situ vs invasive (case-case; in situ code 0, invasive code 1) melanoma from UK Biobank (as of August 2022), QSkin Sun and Health Study and The Queensland Study of Melanoma: environmental and genetic associations (Q-MEGA) study.</p> <p>Columns</p> <p>CHR Chromosome</p> <p>SNP rsid</p> <p>POS Base position HG Build 37</p> <p>A1 effect allele</p> <p>A2 Non-effect allele</p> <p>A1FREQ Allele frequency of effect allele</p> <p>BETA effect estimate of effect allele</p> <p>SE standard error of effect estimate</p> <p>PVAL two-tailed p value</p> <p>DIRECTION the Direction of effect of the SNP in each cohort ( in the order UKBB, FINNGEN, QSKIN, QMEGA 610k, QMEGA OMNI)</p> <p>N sample size</p> <p>See </p>
Data from: Non-inheritable risk factors during pregnancy for congenital heart defects in offspring: a matched case-control study
<p>Data analyzed in "Non-inheritable risk factors during pregnancy for congenital heart defects in offspring: a matched case-control study". The data provided by the authors to benefit other researchers. The posted materials are not copyedited and are the sole responsibility of the authors, so questions should be addressed to the corresponding author.</p>
Supplemental Files to "Mining biodiversity databases establishes a global baseline of cosmopolitan Insecta mOTUs: a case study on Platygastroidea (Hymenoptera) with consequences for biological control programs"
<p>These are supplemental files to the manuscript, "Mining biodiversity databases establishes a global baseline of cosmopolitan Insecta mOTUs: a case study on Platygastroidea (Hymenoptera) with consequences for biological control programs". Supplements contain excel spreadsheets, DNA alignments, Newick tree files, FigTree files, and csv files.</p>
Scripts and Data for "Disentangling the Hydrological and Hydraulic Controls on Streamflow Variability in E3SM V2 – A Case Study in the Pantanal Region"
<p>Matlab scripts for processing and showing the coupled ELM-MOSART coupled simulations for Pantanal region.</p> <p>domain_lnd_Pantanal_default.nc, MOSART_Pantanal_default_c211116.nc, and surfdata_Pantanal_default_c220520.nc are the domain file, MOSART input file, and ELM surface dataset, respectively. </p> <p><a href="https://zenodo.org/api/files/6be468b6-cbfa-4ef4-bb10-0f15812be9bd/Pantanal_half_calibration_CLMCRUNCEPv7.sh">Pantanal_half_calibration_CLMCRUNCEPv7.sh</a> is the bash script to run E3SM with coupled ELM-MOSART configuration. ANd detailed instruction of running E3SMV2 can be found at: https://e3sm.org/model/running-e3sm/e3sm-quick-start/ (last access: Aug 2023).</p> <p>Pantanal_GSIM.zip contains the observed streamflow that used in this study, which is downloaded from <a href="https://doi.pangaea.de/10.1594/PANGAEA.887470">https://doi.pangaea.de/10.1594/PANGAEA.887470</a> (last access: Aug 2023). The reference is Gudmundsson, Lukas; Do, Hong Xuan; Leonard, Michael; Westra, Seth (2018): The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment. Earth System Science Data, 10(2), 787-804, https://doi.org/10.5194/essd-10-787-2018.</p> <p>BFI3.mat is the baseflow index from GSCD and processed to the study domain. The GSCD dataset was download from <a href="http://www.gloh2o.org/gscd/">http://www.gloh2o.org/gscd/</a> (last access: Aug 2023). The reference is Beck, H. E., van Dijk, A. I. J. M., Miralles, D. G., de Jeu, R. A. M., Bruijnzeel, L. A., McVicar, T. R., and Schellekens, J.: Global patterns in base flow index and recession based on streamflow observations from 3394 catchments, Water Resour Res, 49, 7843-7863, <a href="https://doi.org/10.1002/2013WR013918">https://doi.org/10.1002/2013WR013918</a>, 2013.</p> <p>runoff_uncertianty.mat contains the annual runoff time series from GRUN, LORA, and GFRF that processed to the study domain. The GRUN runoff dataset was downloaded from <a href="https://doi.org/10.6084/m9.figshare.9228176">https://doi.org/10.6084/m9.figshare.9228176</a> (last access: Aug 2023). The LORA runoff dataset was downloaded from <a href="https://dap.nci.org.au/thredds/remoteCatalogService?catalog=http://dapds00.nci.org.au/thredds/catalog/ks32/ARCCSS_Data/LORA/v1-0/catalog.xml">https://dap.nci.org.au/thredds/remoteCatalogService?catalog=http://dapds00.nci.org.au/thredds/catalog/ks32/ARCCSS_Data/LORA/v1-0/catalog.xml</a> (last access: Aug 2023). The reference is Hobeichi, S., Abramowitz, G., Evans, J., and Beck, H. E.: Linear Optimal Runoff Aggregate (LORA): a global gridded synthesis runoff product, Hydrol. Earth Syst. Sci., 23, 851-870, 10.5194/hess-23-851-2019, 2019. The GRFR runoff was downloaded from <a href="http://hydrology.princeton.edu/data/mpan/GRFR/runoff/monthly_1deg/">http://hydrology.princeton.edu/data/mpan/GRFR/runoff/monthly_1deg/</a> (last access: Aug 2023). The reference is Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C. d. H., Lu, H., Yang, K., Hong, Y., and Wood, E. F.: Global Reach-level 3-hourly River Flood Reanalysis (1980-2019), B Am Meteorol Soc, 1-49, 10.1175/BAMS-D-20-0057.1, 2021.</p> <p>GLAD_Pantanal_half.mat, GLAD_Pantanal_8th.mat are the processed surface water fraction from GLAD at half and 8th spatial resoution. Specifically, The GLAD surface water dynamics was downloaded from <a href="https://console.cloud.google.com/storage/browser/earthenginepartners-hansen/water;tab=objects">https://console.cloud.google.com/storage/browser/earthenginepartners-hansen/water;tab=objects</a> (last access: Aug 2023). The reference is Pickens, A. H., Hansen, M. C., Hancher, M., Stehman, S. V., Tyukavina, A., Potapov, P., Marroquin, B., and Sherani, Z.: Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series, Remote Sens Environ, 243, 111792, <a href="https://doi.org/10.1016/j.rse.2020.111792">https://doi.org/10.1016/j.rse.2020.111792</a>, 2020.</p>
Case-Control-Study on the Breast Cancer Risk of Mirena® Compared With Copper IUDs
ClinicalTrials.gov study NCT00461253. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.