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7,582 results for “clinical study”
Dataset for the IntoValue 1 + 2 studies on results dissemination from clinical trials conducted at German university medical centers completed between 2009 and 2017
<p>The IntoValue dataset contains clinical trials conducted at one of 35 German UMCs and registered on ClinicalTrials.gov or the German Clinical Trials Registry (DRKS). All trials were reported as complete between 2009 and 2017 on the trial registry at the time of data collection. The dataset also includes a results publication found via manual searches; if multiple results publications were found, the earliest was included.</p> <p>Trials were associated with a German UMC by searching for trials with a UMC listed as responsible party or lead sponsor, or with a principle investigator (PI) from a UMC ('lead_city'). Version 1 additionally includes trials with a UMC only as a facility (`facility_city`). A lookup table of regular expressions used to identify German UMCs is available at <a href="https://github.com/quest-bih/IntoValue2/blob/master/data/1_sample_generation/city_search_terms.csv">https://github.com/quest-bih/IntoValue2/blob/master/data/1_sample_generation/city_search_terms.csv</a>.</p> <p>Trials include all interventional studies and are not limited to investigational medical product trials, as regulated by the EU's Clinical Trials Directive or Germany's Arzneimittelgesetz (AMG) or Novelle des Medizinproduktegesetzes (MPG).</p> <p>DRKS data were searched (pre-filtered for completion years and study status as well as Germany as 'Country of recruitment') and downloaded as CSVs from the DRKS website (<a href="https://www.drks.de/">https://www.drks.de/</a>). ClinicalTrials.gov data were downloaded downloaded as pipe files from Clinical Trials Transformation Initiative (CTTI) Aggregate Content of ClinicalTrials.gov (AACT) (<a href="https://aact.ctti-clinicaltrials.org/pipe_files">https://aact.ctti-clinicaltrials.org/pipe_files</a>). DRKS and ClinicalTrials.gov use different terminology for various trial aspects, such as phase and masking; these different levels are captured in the data dictionary as `levels_drks` and `levels_ctgov`. For later analyses requiring parity across registries, levels for some variables were collapsed and a lookup table is provided in `iv_data_lookup_registries.csv`.</p> <p>These data were generated and used for two publications (Wieschowski et al., 2019; Riedel et al. 2021) and therefore comprises two versions (indicated as `iv_version`).</p> <p>For version 1, registry data was collected on April 17, 2017 from ClinicalTrials.gov and on July 27, 2017 for DRKS and was limited to trials with a completion date on DRKS and primary completion date on ClinicalTrials.gov between 2009 and 2013. Version 1 manual searches for results publications were conducted from 2017-07-01 to 2017-12-01.<br> For version 2, registry data was collected on June 3, 2020 and was limited to trials with a completion date on DRKS and ClinicalTrials.gov between 2014 and 2017. Version 2 manual searches for results publications were conducted from 2020-07-01 to 2020-09-01.</p> <p>Raw registry data for versions 1 and 2 is available in `raw-registries.zip`.</p> <p>Publication identifiers (DOI, PMID, URL) were manually entered during the publication search and then further enhanced using the API of Internet Archive's open-source Fatcat catalog of research publications, to add PMIDs based on DOIs, and vice versa.</p> <p>Manual search steps differed slightly in the two versions and are indicated and described in `identification_step`.<br> Version 1 includes trials with a German UMC as either a `lead_city` or a `facility_city`, whereas version 2 is limited to trials a German UMC as a `lead_city`.</p> <p>Each row indicates a single trial registration. Due to changes in completion dates, some trials are duplicated between versions as indicated in `is_dupe`. Cross-registered trials were manually deduplicated, and some cross-registered duplicates remain (e.g., DRKS00004156 and NCT00215683) and are not indicated in the dataset.</p> <p>All dates are provided as `yyyy-mm-dd`.</p> <p>Additional documentation on each variable (type, description, levels) is provided in `iv_data_dictionary.csv`.</p> <p>Additional information on the project and methods for generating the dataset is available in associated publications and at the project's OSF page (<a href="https://osf.io/98j7u/">https://osf.io/98j7u/</a>). Code for the project is available at <a href="https://github.com/quest-bih/IntoValue2">https://github.com/quest-bih/IntoValue2</a>.</p> <p><strong>References:</strong></p> <p>Wieschowski, S., Riedel, N., Wollmann, K., Kahrass, H., Müller-Ohlraun, S., Schürmann, C., Kelley, S., Kszuk, U., Siegerink, B., Dirnagl, U., Meerpohl, J., & Strech, D. (2019). Result dissemination from clinical trials conducted at German university medical centers was delayed and incomplete. Journal of Clinical Epidemiology, 115, 37–45. <a href="https://doi.org/10.1016/j.jclinepi.2019.06.002">https://doi.org/10.1016/j.jclinepi.2019.06.002</a></p> <p>Riedel, N., Wieschowski, S., Bruckner, T., Holst, M. R., Kahrass, H., Nury, E., Meerpohl, J. J., Salholz-Hillel, M., & Strech, D. (2021). Results dissemination from completed clinical trials conducted at German university medical centers remained delayed and incomplete. The 2014-2017 cohort. Journal of Clinical Epidemiology, 0(0). <a href="http://doi.org/10.1016/j.jclinepi.2021.12.012">https://doi.org/10.1016/j.jclinepi.2021.12.012</a><br> </p>
Epidemiological and clinical characteristics predictive of ICU mortality of traumatic brain injury patients treated at a trauma reference hospital – A cohort study - Dataset
<p><strong>Dataset of a cohort whose summary is described below.</strong></p> <p><strong>ABSTRACT</strong></p> <p><strong>Background</strong>: Traumatic brain injury (TBI) has substantial physical, psychological, social and economic impacts, with high rates of morbidity and mortality. Considering its high incidence, the aim of this study was to identify epidemiological and clinical characteristics that predict mortality in patients hospitalized for TBI in intensive care units (ICUs). <strong>Methods</strong>: A retrospective cohort study was carried out with patients over 18 years old with TBI admitted to an ICU of a Brazilian trauma referral hospital between January 2012 and August 2019. TBI was compared with other traumas in terms of clinical characteristics of ICU admission and outcome. Univariate and multivariate analyses were used to estimate the odds ratio for mortality. <strong>Results</strong>: Of the 4816 patients included, 1114 had TBI, with a predominance of males (85.1%). Compared with patients with other traumas, patients with TBI had a lower mean age (45.3 ± 19.1 versus 57.1 ± 24.1 years, p < 0.001), higher median APACHE II (19 versus 15, p <0.001) and SOFA (6 versus 3, p < 0.001) scores, lower median Glasgow Coma Scale (GCS) score (10 versus 15, p < 0.001), higher median length of stay (7 days versus 4 days, p < 0.001) and higher mortality (27.6% versus 13.3%, p < 0.001). In the multivariate analysis, the predictors of mortality were older age (OR: 1.008 [1.002-1.015], p = 0.016), higher APACHE II score (OR: 1.180 [1.155-1.204], p < 0.001), lower GCS score for the first 24 hours (OR: 0.730 [0.700-0.760], p < 0.001), and greater number of brain injuries and presence of associated chest trauma (OR: 1.727 [1.192-2.501], p < 0.001). <strong>Conclusion</strong>: Patients admitted to the ICU for TBI were younger and had worse prognostic scores, longer hospital stays and higher mortality than those admitted to the ICU for other traumas. The independent predictors of mortality were advanced age, APACHE II score, first 24-hour GCS score, number of brain injuries and chest trauma.</p>
Post-trial access practice in Malaria, Tuberculosis, and NTDs Clinical Trial studies in Sub-Saharan African countries, quantitative study
<p>This is the data set used <span>to evaluate post trial access plan and implementation practice on TB, Malaria and NTD clinical trial studies conducted in the sub-Saharan African countries. </span></p>
Final Dataset for the DIssemination of REgistered COVID-19 Clinical Trials (DIRECCT) Study
<p>The DIRECCT study is a multi-phase examination of clinical trial results dissemination during the COVID-19 pandemic.</p> <p>Interim data for trials completed during the first six months of the pandemic (i.e., 1 January 2020 – 30 June 2020) was previously deposited at https://doi.org/10.5281/zenodo.4669936.<br> This data deposit comprises the results of searches for trials completed during the first 18-months of the pandemic (i.e., 1 January 2020 – 30 June 2021).<br> The data structure for the final phase of the project is not identical to the interim data as it was substantially more complex.<br> The data include datatables (CSVs) that can be treated as relational and joined on the `id` or `trn` columns. See datamodel.png for an overview of the data.</p> <p>Details on data sources and methods for the creation and analysis of this dataset are available in a detailed protocol (Version 3.1, 19 July 2023) : https://osf.io/w8t7r</p> <p>Note: This repository will be updated with additional information including a codebook and archives of raw data.</p> <p>Additional information on the project is available at the project's OSF page: https://doi.org/10.17605/osf.io/5f8j2.</p>
What is the clinical course of patients hospitalised for COVID-19 treatment Ireland: a retrospective cohort study in Dublin's North Inner City (the 'Mater 100')
<p><strong>Background: </strong>Since March 2020, Ireland has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While several cohorts from China have been described, there is little data describing the epidemiological and clinical characteristics of patients with COVID-19 in Ireland. <strong>To improve our understanding of this emerging infection we carried out </strong>a retrospective review of patient data to<strong> examine the clinical characteristics </strong>of patients admitted for COVID-19 hospital treatment.</p> <p><strong>Methods<strong>:</strong></strong> Demographic, clinical and laboratory data on the first 100 adult patients admitted to Mater Misericordiae University Hospital (MMUH) for in-patient COVID-19 treatment after onset of the outbreak in March 2020 was extracted from clinical and administrative records.</p> <p><strong>R<strong>esults:</strong></strong> Fifty-eight per cent were male, 63% were Irish nationals, and median age was 45 years (interquartile range [IQR] =34-64 years). Patients had symptoms for a median of five days before diagnosis (IQR=2.5-7 days), most commonly cough (72%), fever (65%), dyspnoea (37%), fatigue (28%), myalgia (27%) and headache (24%). Of all cases, 54 had at least one pre-existing chronic illness (most commonly hypertension, diabetes mellitus or asthma). At initial assessment, the most common abnormal findings were: C-reactive protein >7.0mg/L (74%), ferritin >247μg/L (women) or >275μg/L (men) (62%), D-dimer >0.5μg/dL (62%), chest imaging (59%), NEWS Score (modified) of ≥3 (55%) and heart rate >90/min (51%). Twenty-seven required supplemental oxygen, of which 17 were admitted to the intensive care unit - 14 requiring ventilation. Forty received antiviral treatment (most commonly hydroxychloroquine or lopinavir/ritonavir). Four died, 17 were admitted to intensive care, and 74 were discharged home, with nine days the median hospital stay (IQR=6-11).</p> <p>C<strong>onclusion:</strong> Our findings reinforce the emerging consensus of COVID-19 as an acute life-threatening disease and highlights, the importance of laboratory (ferritin, C-reactive protein, D-dimer) and radiological parameters, in addition to clinical parameters. Further cohort studies involving larger samples followed longitudinally are a priority.</p>
MetaboScope: A statistical toolbox for analyzing 1H nuclear magnetic resonance spectra from human clinical studies.
<p>MetaboScope is purposefully built as a pipeline where each module accepts the output generated by the previous one. This provides flexibility and simplicity of use, while being straightforward to maintain. The system and its libraries were developed in JavaScript and run as a web app; therefore, all the operations are performed on the local computer, circumventing the need to upload data. The code is open source (DOI: https://www.cheminfo.org/flavor/metabolomics/index.html) and can be readily installed locally. We provide module notes and video tutorials, in addition to clinical spectral datasets for modelling purposes.</p> <p>View data:</p> <p><a title="nmrium.org" href="https://www.nmrium.org/nmrium#?toc=https://zenodo.org/api/records/12916741/files/toc.json/content" target="_blank" rel="noopener">https://www.nmrium.org/nmrium#?toc=https://zenodo.org/api/records/12916741/files/toc.json/content</a></p>
Robust step detection from different waist-worn sensor positions – implications for clinical studies
<p>The dataset contains tri-axial acceleration and gyroscope data (100 Hz sampling) from walks from 19 healthy volunteers, each walking up to three times a parcours of 20 meters with self-selected speed, slow speed or with five soft turns at self-selected speed. Each participant wore 11 time-synchronized sensors during these tests: left/right foot, 5 around waist, non-dominant wrist and upper arm and collar and pocket. In addition to the sensor recordings each 20 meter walk was timed with a stop-watch. </p> <p>Also see: <a href="https://doi.org/10.1159/000511611">https://doi.org/10.1159/000511611</a></p>
Data for "Incidence, clinical course and risk factor for recurrent PCR positivity in discharged COVID-19 patients in Guangzhou, China: a prospective cohort study"
<p>Data for "Incidence, clinical course and risk factor for recurrent PCR positivity in discharged COVID-19 patients in Guangzhou, China: a prospective cohort study"</p>
Epidemiology, risk factors and clinical course of SARS-CoV-2 infected patients in a Swiss university hospital: an observational retrospective study
<p>This is the dataset of the study called "Epidemiology, risk factors and clinical course of SARS-CoV-2 infected patients in a Swiss university hospital: an observational retrospective study". <br> <br> <strong>Abstract: </strong></p> <p>Background<br> Coronavirus disease 2019 (COVID-19) is now a global pandemic with Europe and the USA at its epicenter. Little is known about risk factors for progression to severe disease in Europe. This study aims to describe the epidemiology of COVID-19 patients in a Swiss university hospital.</p> <p>Methods<br> This retrospective observational study included all adult patients hospitalized with a laboratory confirmed SARS-CoV-2 infection from March 1 to March 25, 2020. We extracted data from electronic health records. The primary outcome was the need to mechanical ventilation at day 14. We used multivariate logistic regression to identify risk factors for mechanical ventilation. Follow-up was of at least 14 days. <br> <br> Results<br> 200 patients were included, of whom 37 (18·5%) needed mechanical ventilation at 14 days. The median time from symptoms onset to mechanical ventilation was 9·5 days (IQR 7.00, 12.75). Multivariable regression showed increased odds of mechanical ventilation in males (3.26, 1.21-9.8; p=0.025), in patients who presented with a qSOFA score ≥2 (6.02, 2.09-18.82; p=0.001), with bilateral infiltrate (5.75, 1.91-21.06; p=0.004) or with a CRP of 40 mg/l or greater (4.73, 1.51-18.58; p=0.013). <br> <br> Conclusions<br> This study gives some insight in the epidemiology and clinical course of patients admitted in a European tertiary hospital with SARS-CoV-2 infection. Male sex, high qSOFA score, CRP of 40 mg/l or greater and a bilateral radiological infiltrate could help clinicians identify patients at high risk for mechanical ventilation.</p>
Selected articles from the scoping review on the study designs for clinical trials applied to personalised medicine
<p>The dataset provides the data extracted for the scoping review of the literature on the study designs for clinical trials applied to personalised medicine, as part of the EU project on “Personalised Medicine Trials” (PERMIT).</p> <p>The dataset reports the references for all articles selected as part of the scoping review, as well as information on the general study characteristics and definition, methodology, statistical considerations, and examples of each study design referred to in each included paper.</p>
Unmet Clinical Needs and Case Studies in Blood Testing - Prof Bryant Lin and Dr. Kevin Chang (Stanford University)
<p>This video is the seventh talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Unmet Clinical Needs and Case Studies in Blood Testing - Prof Bryant Lin and Dr. Kevin Chang (Stanford University)</p> <p>Bio: Bryant Lin, MD, MEng is a primary care physician, educator and researcher. The cornerstone of Dr. Lin's work is keeping medicine focused on humans - patients, providers, families and trainees - and not lost in technology and algorithms. His research and educational interests span (1) Developing and testing novel medical technologies, (2) Improving the health of Asian populations with Precision and Population Health, and (3) Increasing expression and interconnections in the Health Community with the Humanities and Arts. After receiving his undergraduate and master's degrees in Electrical Engineering and Computer Science from MIT, he completed his MD and internal Medicine training at Tufts University School of Medicine and Tufts Medical Center. He came to Stanford to serve as a Research Fellow in Cardiac Electrophysiology and Biodesign Fellow where he learned to identify unmet human-centered needs. Since completing his post-graduate training, he stayed at Stanford as clinical faculty in Primary Care and Population Health in the Department of Medicine where he has invented and researched new medical technologies addressing unmet human-centered needs and started the Consultative Medicine Clinic evaluating patients with medical mysteries. He serves as the Training Director for the Joe and Linda Chlapaty DECIDE Center which has created a novel shared decision making tool for atrial fibrillation anticoagulation and is an investigator in several active clinical trials. Three years ago, he co-founded and currently co-directs, with Dr. Latha Palaniappan, the Center for Asian Health Research and Education (CARE) which aims to improve the health of Asians everywhere. Most recently, he has worked closely with the Medicine and the Muse leadership to help start the Stuck@Home concert series, the Stanford SoundWalk and the COVID Remembrance project. Dr. Lin has an active interest in storytelling and film-making. He co-directs an undergraduate seminar, MED 53Q “Storytelling in Medicine”, with Dr. Lauren Edwards and is working with a group of students on a documentary on end-of-life care at a JapaneseAmerican Senior Home in the Bay Area.</p> <p>Kevin Chang MD, MS, is a primary care physician. His focus in on patient care, population health and quality improvement, and medical education. He received his undergraduate degree and master's degree in biomedical engineering from Duke University and Stanford University respectively, and completed his MD at New York University, followed by his medical training at Stanford University. He has since stayed on at Stanford as clinical faculty in Primary Care and Population Health in the Department of Medicine, where he also serves as the co-director of the resident physician Internal Medicine clinic.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/ozk1iJYC1yk</p>
Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial (Data for independent validation study)
<p>Digital technologies and advanced analytics have drastically improved our ability to capture and interpret health relevant data from patients. However, to date, limited data and results have been published detailing real-world patient compliance, demonstrating accuracy in target indications or examining what novel insights and clinical value can be derived. Here we present novel, digital mobility data from two studies: an independent, non-interventional validation study with elderly, naturally slow walking subjects, and a global, multi-site phase IIb clinical trial involving patients with age-related muscle loss and slow walking speed (sarcopenia). Based on these data, we validate the accuracy of a novel algorithm for capturing in-clinic and real-world gait speed in frail, slow-walking adults. We demonstrate the feasibility of continuous monitoring with a wearable inertial sensor in elderly adults in real-world settings, and propose minimum thresholds for compliance required for robust capture of gait behaviors in this population. We also show how simple, inferred contextual information, describing the length of a given walking bout, can explain some of the variation in real-world gait speed, and use this information to demonstrate for the first time a relationship between in-clinic performance and real-world gait speed behavior. This work lays a foundation for exploration of the clinical relevance and value of such measures and is a first step in building a more complete chain of evidence between standardized physical performance assessment, real-world behavior, and subjective perceptions of mobility, independence and health.</p> <p>This dataset contains data collected during the independent validation study: derived data from raw accelerometry data, and summary performance data.</p> <p>The full dataset, including raw accelerometry data, is available here: <a href="https://mueller-et-al-2019.s3.amazonaws.com/index.html">https://mueller-et-al-2019.s3.amazonaws.com/index.html</a></p>
A clinical exome study on a family segregating pontocerebellar hyploplasia
<p><span>Pontocerebellar hypoplasia type 2D (PCH2D) is caused by mutations in the SEPSECS gene (chr. 4p15.2), encoding O-Phosphoseryl-tRNA:selenocysteinyl-tRNA synthase. This is a key enzyme in the biosynthesis of selenoproteins, which act in maintaining antioxidant systems. . We describe a novel patient with compound heterozygosity in the SEPSECS gene including a novel missense variant. </span><span> This study broadens the genetic background and associated PCH2D phenotype, supporting the causal link with mitochondrial disorders in selenoproteins biosynthesis deficiency</span></p> <p> </p> <p>22M1764 vcf files are related to the proband</p> <p>22M1764M vcf files are related to his mother</p> <p>22M1764P files are related to his father</p> <p> </p> <p>The proband suffers with pontocerebellar hyplosia while his parents are healthy</p>
Dataset from: Leveraging open tools to realize the potential of self-archiving: A cohort study in clinical trials
<p>This record includes the data associated with the study "Leveraging open tools to increase the potential of self-archiving to increase discoverability: A cohort study in clinical trials". The code used to generate these data is available under an open license in GitHub (<a href="https://github.com/delwen/oa-archiving-permissions">https://github.com/delwen/oa-archiving-permissions</a>). The deposit includes:</p> <p>- `intovalue.csv`: download of the IntoValue dataset (IntoValue 1 and IntoValue 2), which is actively maintained in GitHub (<a href="https://github.com/maia-sh/intovalue-data">https://github.com/maia-sh/intovalue-data</a>). The data was downloaded on 17 December 2022. More information on the generation of this dataset can be found at: <a href="https://doi.org/10.5281/zenodo.5141343">https://doi.org/10.5281/zenodo.5141343</a>. This data corresponds to the start of the trial screening flow diagram in the manuscript (n = 3,788).</p> <p>- `oa-unpaywall.csv`: dataset containing the results of the Unpaywall API query (query date: 17 December 2022). The dataset queried includes the following adaptations from `intovalue.csv`:</p> <ul> <li>As updated registry data had been downloaded on 1 November 2022, the IntoValue inclusion criteria were re-applied: <ul> <li>Interventional</li> <li>Study completion date between 2009 and 2017</li> <li>Complete based on study status</li> <li>Conducted by a German university medical center.</li> </ul> </li> <li>The dataset was further limited to: <ul> <li>Unique trials (trials from IntoValue 2 were preserved)</li> <li>Unique publications with a DOI</li> </ul> </li> </ul> <p>- `oa-syp-permissions.csv`: dataset containing the results of the Shareyourpaper API query (query date: 17 December 2022). The dataset queried is the same as in `oa-unpaywall.csv`.</p> <p>- `oa-merged-data.csv`: dataset containing the merged Unpaywall and Shareyourpaper data for all clinical trial results publications considered in this study. The dataset was further limited to journal articles that resolved in Unpaywall and were published between 2010 - 2020 (based on the publication date in Unpaywall). This is the main dataset underlying the analyses in the manuscript.</p>
Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group
<p>Example datasets and code for the recommended implementation of Quantitative Susceptibility Mapping (QSM) in "Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group".</p>
Consensus recommendations for opioid agonist treatment following the introduction of emergency clinical guidelines in Ireland during the COVID-19 pandemic: A national Delphi study
<p>Anonymous Delphi survey likert scale responses (round 1 (S1-S32) and 2 (R2S1-R2S15)) <a href="https://zenodo.org/api/files/92371fe3-6af6-4044-ba3f-83a26b2fe471/Delphi_likert_responses_ano.csv?versionId=7c3aaa7b-8b43-43da-bfeb-fe644f33d3e2">Delphi_likert_responses_ano.csv</a> and corresponding statements in <a href="https://zenodo.org/api/files/92371fe3-6af6-4044-ba3f-83a26b2fe471/STATEMENTS_REPO.csv?versionId=ada85e7c-ba48-4f61-8e1a-d90dab9b4f05">STATEMENTS_REPO.csv</a>.</p>
Study to Investigate the Clinical and Parasiticidal Activity and Pharmacokinetics of Different Doses of Artefenomel and Ferroquine in Patients With Uncomplicated Plasmodium Falciparum Malaria
ClinicalTrials.gov study NCT03660839. IPD Sharing: YES. Countries: 5. Publications: 1.
A Clinical Study to Assess the Efficacy and Safety of Gene Therapy for the Treatment of Cerebral Adrenoleukodystrophy (CALD)
ClinicalTrials.gov study NCT03852498. IPD Sharing: YES. Countries: 6. Publications: 1.
Multinational Clinical Study Comparing Isatuximab, Pomalidomide, and Dexamethasone to Pomalidomide and Dexamethasone in Refractory or Relapsed and Refractory Multiple Myeloma Patients
ClinicalTrials.gov study NCT02990338. IPD Sharing: YES. Countries: 24. Publications: 8.
An Extension Study of HGT-HIT-045 Evaluating Long-Term Safety and Clinical Outcomes of Idursulfase-IT in Conjunction With Elaprase in Pediatric Participants With Hunter Syndrome and Cognitive Impairme
ClinicalTrials.gov study NCT01506141. IPD Sharing: YES. Countries: 3. Publications: 3.
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.