Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
646
datasets available to search
ShareScore release 0.9.0
Dataset results
646 results for “clinical data”
Row data for the experiment: "Clinical, psychosocial and demographic factors affect decisions in SLE people".
<p>These datasets correspond to the article titled: “Clinical, psychosocial and demographic factors affect decisions in SLE people”, which can be found at <a href="https://www.medrxiv.org/content/10.1101/2024.03.25.24304643v1.full.pdf">https://www.medrxiv.org/content/10.1101/2024.03.25.24304643v1.full.pdf</a></p> <p>Analysis scripts, and an explanation of variables, can be found at: <a href="https://github.com/NeuroGenomicsMX/Factors_affecting_decisions_in_SLE">https://github.com/NeuroGenomicsMX/Factors_affecting_decisions_in_SLE</a></p> <p>Abstract</p> <p><span>Neurological and psychiatric manifestations affect most lupus individuals and include depression, anxiety, mood disorders, and cognitive dysfunction. Although there is evidence supporting suboptimal decision-making in lupus and its association with glucocorticoids consumption, it is not clear what variables impact such decisions. The aim of this study is to explore how social, clinical, psychological, and demographic factors impact social and temporal decision-making in people with lupus. Through a within-subjects experimental-design, our participants responded to social, clinical, psychological, and demographic electronic questionnaires. Then, they participated in two behavioral economics experiments: the third-party dictator game, and the delay discounting task. Our results show that hostility, and age are essential predictors of social decisions, whereas obsessive-compulsiveness and anxiety better predict temporal decisions. These variables behave as expected, but anxiety shows unexpected results: most anxious people act patiently and prefer delayed but bigger rewards. Finally, clinical factors are critical decision predictors for social and temporal decisions. When people are in remission, they tend to impose higher punishment on those who violate the social norm, and they also tend to prefer immediate rewards. When taking glucocorticoids, they also prefer immediate rewards, and as the dosage of glucocorticoids intake increases, they tend to impose higher punishment on norm violators. Clinicians, researchers, and practitioners must consider the side effects of glucocorticoids on decision-making.</span></p>
DATA SET: Performance Assessment of a Commercial Continuous-Wave Near-Infrared Spectroscopy Tissue Oximeter for Suitability for Use in an International, Multi-Center Clinical Trial
<p>This repository contains the data sets related to the publication:</p> <p>Cortese, L.; Zanoletti, M.; Karadeniz, U.; Pagliazzi, M.; Yaqub, M.A.; Busch, D.R.; Mesquida, J.; Durduran, T. Performance Assessment of a Commercial Continuous-Wave Near-Infrared Spectroscopy Tissue Oximeter for Suitability for Use in an International, Multi-Center Clinical Trial. <em>Sensors</em> <strong>2021</strong>, <em>21</em>, 6957. https://doi.org/10.3390/s21216957</p>
TomoBreast randomized clinical trial's lung-heart outcomes and mortality through the 2020 COVID-19 pandemic: data and software
<p>Dataset and R script to reproduce the analyses of the manuscript:</p> <p>Vinh-Hung V, Gorobets O, Adriaenssens N, Van Parijs H, Storme G, Verellen D, Nguyen NP, Magne N, De Ridder M.</p> <p><strong>Lung-heart outcomes and mortality through the 2020 COVID-19 pandemic in a prospective cohort of breast cancer radiotherapy patients.</strong></p> <p>Cancers 2022; 14(24):6241. https:// doi.org/10.3390/cancers14246241</p> <p>https://www.mdpi.com/2072-6694/14/24/6241</p> <p>PubMed: PMID: 36551726</p> <p>PMCID: PMC9777311</p> <p>Info on the variables in file "aelq6_public.R"</p> <p>reproduced in "aelq_2_3_readme.txt":</p> <p>"aelq2_base2.txt" = baseline characteristics.</p> <p>"aelq3.txt" = longitudinal maesurements.</p> <p>Variables in "aelq2_base2.txt":</p> <p>"<strong>aelq2_base2.txt</strong>" = baseline characteristics. <br># Age at randomization, years. <br># RTdose: cf TomoBreast papers. <br># 51 Gy = hypofractionated, simultaneous integrated boost<br># 42 Gy = hypofractionated, no boost, mastectomy cases only<br># 50 Gy = conventional, no boost, mastectomy cases only<br># 66 Gy = conventional, sequential boost<br># Weight kg, Height cm, <br># Detection 1=found by screening (senology follow-up/controle)<br># 2=found by symptoms (pain, palpable)<br># 9=unknown<br># Smoker 0= Not smoker<br># 1= Smoker<br># 2=ex-smoker<br># Mastectomy (and other binary coded) 1= yes<br># chemosched 0=none<br># 1= planned after RT (sequential)<br># 2= prior to RT and is finished (sequential)<br># 3= chemo is on-going or is planned to start with RT (concomitant)<br># hormonetherapy 0=no<br># 1=tamoxifen (nolvadex)<br># 2=Femara (Letrozole)<br># 3=zoladex<br># 4=tamoxifen + zoladex<br># Laterality 1,=Right, 2=Left, 3=Bilateral<br># LengthFU: length of follow-up, days from randomization</p> <p>"<strong>aelq3.txt</strong>" = longitudinal maesurements.<br># "Nr" = Case ID<br># "Time" in days from origin (origin =date of randomization), <br># if negative =before randomization<br># "KPS" "Weight" <br># "Died" "LocalRec" "Metast" "NewPrim" = binary code, 0=no, 1=yes<br># "fAEBreast" "fAEHeart" "fAELung" "fAEOther" <br># fAE = freedom from breast, heart, lung, other adverse event score<br># "LVEF2" = ejection fraction, %<br># "MacIver" = estimated cardiac strain</p> <p># the following are pulmonary function tests, untransformed units<br># "FVC", "FEV1", "PEF", "VC", "TLC", "RV", "FRC", "Raw", "sRaw", "DLCO",<br># "VA", "PF"</p> <p># "fDY", "fFA", "fPA" = freedom from dyspnea, from fatigue, from pain<br># range 0 to 100 (best)<br># see papers:</p> <p># Van Parijs, H.; Vinh-Hung, V.; Fontaine, C.; Storme, G.; Verschraegen, C.;<br># Nguyen, D.M.; Adriaenssens, N.; Nguyen, N.P.; Gorobets, O.; De Ridder, M.<br># Cardiopulmonary-related patient-reported outcomes in a randomized clinical<br># trial of radiation therapy for breast cancer. BMC Cancer 2021, 21, 1177,<br># doi:10.1186/s12885-021-08916-z.</p> <p># preprint:<br># Van Parijs, H.; Cecilia-Joseph, E.; Gorobets, O.; Storme, G.; <br># Adriaenssens, N.; Heyndrickx, B.; Verschraegen, C.; Nguyen, N.P.;<br># De Ridder, M.; Vinh-Hung, V. Lung-heart toxicity in a randomized <br># clinical trial of hypofractionated image guided radiation therapy for<br># breast cancer. Preprints 2022, 202212, 0214.<br># https://doi.org/10.20944/preprints202212.0214.v1</p> <p># <br># "Year" = year of the observation<br># example: randomized 1/1/2011, measurement done 1/31/2011, time = 30 days,<br># Year =2011<br>#<br> </p>
TCGA Lower Grade Glioma (LGG) Clinical Data
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled <a href="http://www.cell.com/cell/fulltext/S0092-8674(18)30229-0">"An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics"</a>. The paper highlights four types of carefully curated survival endpoints, and <a href="http://www.cell.com/action/showFullTableImage?isHtml=true&tableId=tbl3&pii=S0092867418302290">recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type</a>. The dataset also includes phenotypic information about LGG. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset. </p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The survival and phenotype data were merged into one file. Empty columns were removed. Columns with the same value for every sample were also removed. </p> <p><strong>Acknowledgments:</strong></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. <a href="https://doi.org/10.1016/j.cell.2018.02.052">https://doi.org/10.1016/j.cell.2018.02.052</a></p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
TCGA Head & Neck Squamous Cell Carcinoma (HNSC) Clinical Data
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled <a href="http://www.cell.com/cell/fulltext/S0092-8674(18)30229-0">"An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics"</a>. The paper highlights four types of carefully curated survival endpoints, and <a href="http://www.cell.com/action/showFullTableImage?isHtml=true&tableId=tbl3&pii=S0092867418302290">recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type</a>. The dataset also includes phenotypic information about HNSC. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset. </p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The survival and phenotype data were merged into one file. Empty columns were removed. Columns with the same value for every sample were also removed. </p> <p><strong>Acknowledgments:</strong></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. <a href="https://doi.org/10.1016/j.cell.2018.02.052">https://doi.org/10.1016/j.cell.2018.02.052</a></p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
TCGA Glioblastoma Multiforme (GBM) Clinical Data
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled <a href="http://www.cell.com/cell/fulltext/S0092-8674(18)30229-0">"An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics"</a>. The paper highlights four types of carefully curated survival endpoints, and <a href="http://www.cell.com/action/showFullTableImage?isHtml=true&tableId=tbl3&pii=S0092867418302290">recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type</a>. The dataset also includes phenotypic information about GBM. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset. </p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The survival and phenotype data were merged into one file. Empty columns were removed. Columns with the same value for every sample were also removed. </p> <p><strong>Acknowledgments:</strong></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. <a href="https://doi.org/10.1016/j.cell.2018.02.052">https://doi.org/10.1016/j.cell.2018.02.052</a></p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
CGGA mRNA seq 325 Clinical Data
<p><strong>Abstract:</strong></p> <p>The Chinese Glioma Datasets (CGGA) are comprehensive and valuable collections of data related to glioma, a type of brain tumor, originating from Chinese patients. The CGGA is a data portal for the storage and interactive exploration of cross-omics data, including nearly 2000 primary and recurrent glioma samples. This dataset contains phenotype information. The Sample IDs serve as unique identifiers for each sample. </p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Acknowledgments:</strong></p> <p>Zhao, Z., Zhang, KN., Wang, QW., et al. Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients (2021). Genomics, Proteomics & Bioinformatics 19(1):1-12.</p> <p>Fang, S., Liang, J., Qian, T., et al. (2017). Anatomic Location of Tumor Predicts the Accuracy of Motor Function Localization in Diffuse Lower-Grade Gliomas Involving the Hand Knob Area. AMERICAN JOURNAL OF NEURORADIOLOGY. 38(10): 1990-1997.</p> <p>Wang, Y., Wang, Y., Fan, X., et al. (2017). Putamen involvement and survival outcomes in patients with insular low-grade gliomas. JOURNAL OF NEUROSURGERY. 126(6): 1788-1794.</p>
TCGA Bladder Urothelial Carcinoma (BLCA) Clinical Data
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled <a href="http://www.cell.com/cell/fulltext/S0092-8674(18)30229-0">"An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics"</a>. The paper highlights four types of carefully curated survival endpoints, and <a href="http://www.cell.com/action/showFullTableImage?isHtml=true&tableId=tbl3&pii=S0092867418302290">recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type</a>. The dataset also includes phenotypic information about BLCA. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset. </p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The survival and phenotype data were merged into one file. </p> <p><strong>Acknowledgments:</strong></p> <p>Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. <a href="https://doi.org/10.1016/j.cell.2018.02.052">https://doi.org/10.1016/j.cell.2018.02.052</a></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p> <p> </p>
Formatted TCGA clinical and RNA-Seq data for colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ)
<p>COAD/READ/COADREAD_rnaseq_fpkm.txt files contain TCGA RNA-Seq data in FPKM normalisation for colorectal adenocarcinoma (COAD), rectum adenocarcinoma (READ) or combined (COADREAD).</p> <p>COAD/READ/COADREAD_rnaseq_tpm.txt files contain TCGA RNA-Seq data in TPM normalisation for colorectal adenocarcinoma (COAD), rectum adenocarcinoma (READ) or combined (COADREAD).</p> <p>COAD/READ/COADREAD_clinical_raw.xlsx files contain TCGA clinical data for patients with colorectal adenocarcinoma (COAD), rectum adenocarcinoma (READ) or combined (COADREAD).</p> <p>COAD/READ/COADREAD_rnaseq_clinical_raw.xlsx files contain corresponding information of TCGA clinical data and RNA-Seq data for patients with colorectal adenocarcinoma (COAD), rectum adenocarcinoma (READ) or combined (COADREAD).</p>
Improving The Diagnosis of Thyroid Cancer by Machine Learning and Clinical Data
<p>This repository contains the dataset used in the paper "Improving The Diagnosis of Thyroid Cancer by Machine Learning and Clinical Data" published in <strong>Scientific Reports</strong>. Please check our <a href="https://www.nature.com/articles/s41598-022-15342-z">formal publication</a> for the full details. The dataset contains 1232 nodules from 724 patients. Each row represents one nodule and each column represents one variable that describes the characteristics of the patient or nodule. The meaning of each variable is summarized below.</p> <ul> <li>id: the unique identity of the patient who carries the nodule</li> <li>age: the age of the patient</li> <li>FT3: triiodothyronine test result</li> <li>FT4: thyroxine test result</li> <li>TSH: thyroid-stimulating hormone test result</li> <li>TPO: thyroid peroxidase antibody test result</li> <li>TGAb: thyroglobulin antibodies test result</li> <li>site: the nodule location, 0: right, 1: left, 2: isthmus</li> <li>echo_pattern: thyroid echogenicity, 0: even, 1: uneven</li> <li>multifocality: if multiple nodules exist in one location, 0: no, 1: yes</li> <li>size: the nodule size in cm</li> <li>shape: the nodule shape, 0: regular, 1: irregular</li> <li>margin: the clarity of nodule margin, 0: clear; 1: unclear</li> <li>calcification: the nodule calcification, 0: absent, 1: present</li> <li>echo_strength: the nodule echogenicity, 0: none, 1: isoechoic, 2: medium-echogenic, 3: hyperechogenic, 4: hypoechogenic</li> <li>blood_flow: the nodule blood flow, 0: normal, 1: enriched</li> <li>composition: the nodule composition, 0: cystic, 1: mixed, 2: solid</li> <li>multilateral: if nodules occur in more than one location, 0: no, 1: yes</li> <li>mal: the nodule malignancy, 0: benign, 1: malignant</li> </ul>
WAW-TACE: A Hepatocellular Carcinoma Multiphase CT Dataset with Segmentations, Radiomics Features, and Clinical Data
<p>The WAW-TACE dataset contains multiphase abdominal CT images from N=233 treatment-naive patients with HCC treated with TACE in monotherapy, annotated with N=377 hand-crafted liver tumor masks, automated segmentations of multiple internal organs, extracted radiomics features, and corresponding extensive clinical data.</p> <p> </p>
Data Structure of Clinical Research
<p><em>Bro</em><em>nchial asthma is one of the most common respiratory pathologies in children, characterized by rising incidence around the world. Early disease onset, severe clinical signs of bronchial asthma, the ineffectiveness of high doses of hormone therapy reduce the quality of life in patients and lead to disability. Analysis of bronchial asthma heterogeneity is now possible in virtue of computer technology and big data processing onrush.</em></p> <p><em>The information on 70 children suffering from bronchial asthma and 20 children from the control group was analyzed in this study.</em></p> <p><em>Gender, age, duration of disease, associated diseases, family history of allergic diseases, clinical blood and urine test, spirography and blood immunoassay results, total IgE, thymic stromal lymphopoietin and results of skin allergy tests were taken into account.</em></p>
DATASET Clinical and Radiographic Evaluation Data of Autotransplantation using 3D Replicas
<p>This dataset repository contains detailed data and analysis scripts related to a research study on autotransplantation using 3D replicas. The study aims to evaluate clinical outcomes and radiographic changes over time in dental autotransplantation cases. The dataset includes CSV files containing clinical data, detailed radiographic evaluation results, and HTML files documenting analysis procedures and findings.</p>
Ivy Gap GBM Clinical Data
<p><strong>Abstract:</strong></p> <p>The Ivy Glioblastoma Atlas Project represents a fundamental tool for investigating the cellular and molecular underpinnings of glioblastoma. It offers an accessible online atlas and database containing valuable clinical and genomic information, which will undoubtedly facilitate future studies on glioblastoma pathogenesis, diagnosis, and therapeutic approaches. Glioblastoma is a highly aggressive brain tumor with a bleak prognosis, and its intricate molecular and cellular characteristics have not been fully elucidated in relation to conventional diagnostic histologic features. The dataset provided is comprised of de-identified clinical data pertaining to both patients and tumors.</p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Acknowledgments:</strong></p> <p>Puchalski RB, Shah N, Miller J, et al. An anatomic transcriptional atlas of human glioblastoma. Science. 2018;360(6389):660-663. doi:10.1126/science.aaf2666</p> <p><strong>U-BRITE last update: </strong>07/28/2023</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>
Dataset related to the article "Clinical and Molecular Data Define a Diagnosis of Arrhythmogenic Cardiomyopathy in a Carrier of a Brugada-Syndrome-Associated PKP2 Mutation"
<p>This record contains raw data related to the article “ Molecular Data Define a Diagnosis of Arrhythmogenic Cardiomyopathy in a Carrier of a Brugada-Syndrome-Associated PKP2 Mutation”. </p> <p>Plakophilin-2 (<em>PKP2</em>) is the most frequently mutated desmosomal gene in arrhythmogenic cardiomyopathy (ACM), a disease characterized by structural and electrical alterations predominantly affecting the right ventricular myocardium. Notably, ACM cases without overt structural alterations are frequently reported, mainly in the early phases of the disease. Recently, the <em>PKP2</em> p.S183N mutation was found in a patient affected by Brugada syndrome (BS), an inherited arrhythmic channelopathy most commonly caused by sodium channel gene mutations. We here describe a case of a patient carrier of the same BS-related <em>PKP2</em> p.S183N mutation but with a clear diagnosis of ACM. Specifically, we report how clinical and molecular investigations can be integrated for diagnostic purposes, distinguishing between ACM and BS, which are increasingly recognized as syndromes with clinical and genetic overlaps. This observation is fundamentally relevant in redefining the role of genetics in the approach to the arrhythmic patient, progressing beyond the concept of "one mutation, one disease", and raising concerns about the most appropriate approach to patients affected by structural/electrical cardiomyopathy. The merging of genetics, electroanatomical mapping, and tissue and cell characterization summarized in our patient seems to be the most complete diagnostic algorithm, favoring a reliable diagnosis.</p>
Nordic trial reporting project: Raw data from EU Clinical Trials Registry (EUCTR) and ClinicalTrials.gov
<p>Uploaded on behalf of the author team for the research project "<strong>Systematic evaluation of clinical trial reporting at medical universities and university hospitals in the Nordic countries</strong>".</p><p><strong>Raw data</strong> from EU Clinical Trials Registry (EUCTR) and ClinicalTrials.gov:</p><p><strong>EUCTR</strong>: We retrieved the latest dataset for the EU Trials Tracker of EUCTR trials on Nov 27, 2022, reflecting data from Nov 7, 2022 (1,2). We also used a custom web scraper that automatically extracts data from EUCTR country protocols and results sections (variables described in Appendix Table 2), developed by the EU Trials Tracker team (2).<br>References: <br>1. Goldacre B, DeVito NJ, Heneghan C, Irving F, Bacon S, Fleminger J, Curtis H. Compliance with requirement to report results on the EU Clinical Trials Register: cohort study and web resource. BMJ. 2018 Sep 12;362:k3218.<br>2. EU Trials Tracker — Who's not sharing clinical trial results? [Internet]. [cited 2022 Aug 30]. Available from: http://eu.trialstracker.net/</p><p><strong>ClinicalTrials.gov</strong>: We downloaded the complete Aggregate Analysis of ClinicalTrials.gov dataset (AACT, http://aact.ctti-clinicaltrials.org/) on Nov 27, 2022, reflecting data from Nov 9, 2022. </p><p>See our GitHub and preregistered protocol for more details:<br>https://github.com/cathrineaxfors/nordic-trial-reporting<br>https://osf.io/97qkv/</p>
Research Data for Comparative Evaluation of RT-PCR and Antigen-based Rapid Diagnostic Tests (Ag-RDTs) for SARS-CoV-2 Detection: Performance, Variant Specificity, and Clinical Implications
<p>This dataset represents laboratory findings for the comparative evaluation of the diagnostic performance of Ag-RDTs (Flourescence Immunoassay and Lateral Flow Immunoassay) with RT-PCR</p>
Determining aligner-induced tooth movements in three dimensions using clinical data of two patients: datasets
<h2>Abstract</h2> <p>The effectiveness of a series of optically transparent aligners for orthodontic treatments depends on the anchoring of each tooth. In contrast with the roots, the crowns’ positions and orientations are measurable with intraoral scans, thus avoiding any X-ray dose. Exemplified by two patients, we demonstrate that three-dimensional crown movements could be determined with micrometer precision by registering weekly intraoral scans. The data show the movement and orientation changes in the individual crowns of the upper and lower jaws as a result of the forces generated by the series of aligners. During the first weeks, the canines and incisors were more affected than the premolars and molars. We detected overall tooth movement of up to about 1 mm during a nine-week active treatment. The data on these orthodontic treatments indicate the extent to which actual tooth movement lags behind the treatment plan, as represented by the aligner shapes. The proposed procedure can not only be used to quantify the clinical outcome of the therapy, but also to improve future planning of orthodontic treatments for each specific patient. This study should be treated with caution because only two cases were investigated, and the approach should be applied to a reasonably large cohort to reach strong conclusions regarding the efficiency and efficacy of this therapeutic approach.</p> <h2>Data</h2> <p>The repository contains the data of the intraoral scans and all data where manual interactions were performed to allow reproducing the results. </p> <p>The directory and file names are as follows:</p> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Level, type<br></strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><em>p</em>_Clinical_Trial</td> <td>1st, directory</td> <td>Patient <em>p</em>: <em>p</em>=3485 stands for patient A, <em>p</em>=6457 stands for patient B</td> </tr> <tr> <td>Bottmedical</td> <td>2nd, directory</td> <td>Planning data</td> </tr> <tr> <td>Sirona</td> <td>2nd, directory</td> <td>Intraoral scan data</td> </tr> <tr> <td>T<em>n</em></td> <td>3rd, directory</td> <td>Time step <em>n</em> for <em>n</em>=0: before treatment, <em>n</em>=1-9: after 1-9 weeks of treatment; <em>n</em>=10: end of treatment</td> </tr> <tr> <td>Lower_Aligner_<em>n</em>_cut1.stl</td> <td>4th, file</td> <td>Manually cut surface mesh of planned data, lower jaw for time step <em>n</em></td> </tr> <tr> <td>Upper_Aligner_<em>n</em>_cut1.stl</td> <td>4th, file</td> <td>Manually cut surface mesh of planned data, upper jaw for time step <em>n</em></td> </tr> <tr> <td><em>p</em>_OnyxCeph3_Export_<em>j</em>_cut1.stl</td> <td>4th, file</td> <td>Manually cut surface mesh from intraoral scan, for lower (<em>j</em>=UK) or upper (<em>j</em>=OK) jaw</td> </tr> <tr> <td><em>p</em>_OnyxCeph3_Export_<em>j</em>.stl</td> <td>4th, file</td> <td>Original surface mesh from intraoral scan, for lower (<em>j</em>=UK) or upper (<em>j</em>=OK) jaw</td> </tr> <tr> <td>teethSeg</td> <td>4th, directory</td> <td>Segmented crowns via OnyxCeph3 TM</td> </tr> <tr> <td>teethSeg_noSnap_TolS</td> <td>4th, directory</td> <td>Transferred crown segmentations and occlusion plane points</td> </tr> <tr> <td><em>p</em>_Z<em>i</em>.stl</td> <td>5th, file</td> <td> <p>Surface mesh of crown segmentation for tooth number <em>i </em>of patient<em> p<br></em></p> </td> </tr> <tr> <td><em>p</em>_<em>j</em>.stl</td> <td>5th, file</td> <td> <p>Surface mesh of all crown segmentations for lower (<em>j</em>=UK) or upper (<em>j</em>=OK) jaw of patient <em>p</em></p> </td> </tr> <tr> <td><em>p</em>_<em>j</em>_occPlane.mat</td> <td>5th, file</td> <td> <p>Binary Matlab file of saved variable occPlane, which defines transferred occlusion plane points for lower (<em>j</em>=UK) or upper (<em>j</em>=OK) jaw of patient <em>p</em></p> </td> </tr> </tbody> </table> <p>File formats:</p> <p>stl files describe an unstructured triangulates surface by vertices and triangles. These files can be read by the open source software freecad or the MATLAB function stlread.</p> <p>mat files are MATLAB files and can be read via MATLAB function load.</p>
Supplementary Data for "Sequencing the Pandemic: Rapid and High-Throughput Processing and Analysis of COVID-19 Clinical Samples for 21st Century Public Health"
<p>Supplementary material for F1000 methods manuscript. Includes raw sequencing metrics for two COVID sequencing methodologies, as well as a complete cost breakdown for each methodology.</p>
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.