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1,659 results for “Patient Data”
Experiment data in support of "Segmentation analysis and the recovery of queuing parameters via the Wasserstein distance: a study of administrative data for patients with chronic obstructive pulmonary disease"
<p>This archive contains a ZIP archive, `data.zip`, that itself contains the data used in the final sections of the paper. The remainder of the paper's supporting files are available at <a href="https://github.com/daffidwilde/copd-paper/">github.com/daffidwilde/copd-paper/</a></p> <p>The ZIP archive is structured as follows:</p> <ul> <li>There is a directory, `wasserstein`, for the parameter sweep described in the model construction section of the paper. Its contents are: (i) a file, `main.csv`, describing each parameter and their maximal Wasserstein distance to the observed data, and (ii) three directories, `best`, `median` and `worst`, each containing the simulated queuing results (in `main.csv`) from that sweep with the best, median and worst found parameter sets, respectively (in `params.txt`).</li> <li>The remaining three directories correspond to the experiments conducted in the final section of the paper. Each directory contains two files: (i) `system_times.csv` which holds trial parameters and system time records for every patient to pass through the model in that experiment, and (ii) `utilisations.csv` which holds trial parameters and utilisations for each server in the model for that experiment.</li> </ul>
Single-cell mouse and PC9 data for "TP53 loss with whole genome doubling mediates heterogeneous intra-patient therapy response through Chromosomal Instability"
<p>This repository includes the processed data (including copy number profiles and related analysis) for the E/EP mouse tumors and for the PC9 resistance cell lines for all the analyses of the manuscript "TP53 loss with whole genome doubling mediates heterogeneous intra-patient therapy response through Chromosomal Instability".</p><p>The code for the related analyses is available in GitHub at https://github.com/zaccaria-lab/TP53loss_WGD</p>
The supplemental data for the paper: "Methodology of generation of CFD meshes and 4D shape reconstruction of coronary arteries from patient-specific dynamic CT"
<p>The supplemental data for the paper: "Methodology of generation of CFD meshes and 4D shape reconstruction of coronary arteries from patient-specific dynamic CT"</p><p>A video file (minimum play resolution is HD to see the mesh) showing the movement of the LCA throughout the heart cycle and .STL files for 10--100% (increment of 10\%) of the heart cycle phase.</p>
16S rRNA Sequencing Data of Fecal Microbiota in an Italian Cohort of Patients with CDKL5 Deficiency Disorder
<h3>Summary of the study </h3> <p>CDKL5 deficiency disorder (CDD) is a neurodevelopmental condition characterized by global developmental delay, early-onset seizures, intellectual disability, visual and motor impairments, distinct from Rett Syndrome (RTT) due to the absence of a clear regression period. Gastrointestinal (GI) disturbances and signs of subclinical immune dysregulation are common in CDD patients, yet the underlying causes are unknown. Recent studies hint at a possible link between neurological disorders and gut microbiota, an unexplored area in CDD. In this groundbreaking study, we examined fecal microbiota in CDD patients and their healthy relatives, revealing differences in bacterial diversity and composition. We further investigated microbiota changes based on various factors, including the severity of GI issues, seizure frequency, sleep disorders, food intake type, neuro-behavioral features (assessed through the RTT Behaviour Questionnaire – RSBQ), and ambulation capacity. </p> <p>Our findings suggest a potential connection between CDD, microbiota, and symptom severity. This study represents the first exploration of the gut-microbiota-brain axis in CDD patients, contributing to the growing body of research on the role of gut microbiota in neurodevelopmental disorders. It opens doors to potential interventions targeting intestinal microbes to enhance the well-being of individuals with CDD.</p> <h3>Mehods</h3> <p>The Dataset represent the raw data (.fastq) obtained from the sequencing of the fecal samples from 17 Italian Patients with CDD, and 17 Healthy Relatives (i.e. siblings or mother), collected at a single time-point.</p> <p>Samples from Patients affected by CDD are called CDD, samples from Healthy Relatives are called HC-CDD (i.e. healthy controls of patients affected by CDD). For details about the sample names see the “Explanation Table”.</p> <p>Bacterial DNA was extracted from fecal samples using the QIAmp Powerfexal DNA Kit (Qiagen, Germany) following the manufacturer's protocol. The 16S rRNA sequencing and analysis was performed by a service offered by Zymo Research (Germany).</p> <p><em>Targeted Library Preparation</em>: The DNA samples were prepared for targeted sequencing with the Quick-16S™ NGS Library Prep Kit (Zymo Research). The primer sets used were Quick-16S™ Primer Set V3-V4 (Zymo Research). The sequencing library was prepared using an innovative library preparation process in which PCR reactions were performed in real-time PCR machines to control cycles and therefore limit PCR chimera formation. The final PCR products were quantified with qPCR fluorescence readings and pooled together based on equal molarity. The final pooled library was cleaned up with the Select-a-Size DNA Clean & Concentrator™, then quantified with TapeStation® (Agilent Technologies, Santa Clara, CA) and Qubit® (Thermo Fisher Scientific, Waltham, WA). </p> <p><em>Sequencing:</em> The final library was sequenced on Illumina® MiSeq™ with a v3 reagent kit (600 cycles). </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>
Data for "Impact of early cleft lip and palate surgery on maxillary growth in 5- and 10-Year-old patients with unilateral cleft lip and palate: a cross-sectional study"
<p>Relative frequency in % (absolute frequency is shown above each bar). Frequency of 5YO indices in cleft patients and frequency of GOSLON indices in cleft patients.</p>
Patient-specific processed data, code and visualisations for "Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions"
<p>Processed data and code for reproducing the main results and figures of the paper "<strong>Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions</strong>".</p> <p>We analysed publicly available data from subjects with drug-resistant focal epilepsy. A total of 2656 hours of long-term intracranial electroencephalography (iEEG) from 18 subjects was obtained using the "The SWEC-ETHZ iEEG Database and Algorithms" (available at <a href="http://ieeg-swez.ethz.ch">http://ieeg-swez.ethz.ch</a>) (Burrello et al., 2019).</p> <p>Reference<br> A. Burrello, L. Cavigelli, K. Schindler, L. Benini, A. Rahimi, <strong>‘‘</strong>Laelaps: An Energy-Efficient Seizure Detection Algorithm from Long-term Human iEEG Recordings without False Alarms<strong>’’</strong> <em>in proceedings of the</em> <em>ACM/IEEE Design, Automation, and Test in Europe Conference (DATE)</em>, Florence, Italy, March 25-29, 2019. </p>
CSV files and R script: writing process data of typed picture description by 15 cognitively impaired patients and 15 healthy controls
<p>Writing process data of 15 cognitively impaired patients and 15 age- and gender-matched healthy controls were obtained. Each of them completed two typed picture description tasks that were logged with Inputlog, a keystroke logging tool. Variables included time on task; number of characters, pauses and Pause-bursts per minute; proportion of pause time; duration of Pause-bursts; and pause time between words. For pause time between words, also the effect of pauses preceeding specific word categories was analyzed.</p> <p>The data were used to explore if the observation of writing behavior can assist in the screening and follow-up of mild cognitive impairment (MCI) and mild dementia due to Alzheimer’s disease (AD). This data set contains the CSV files that were used for the analyses and the corresponding R script.</p>
Treating anti-vax patients, a new stressor for COVID-19 center doctors. Data from a 2-year prospective study.
<p>Dataset of the study "Treating anti-vax patients, a new stressor for COVID-19 center doctors. Data from a 2-year prospective study."</p>
Source data to publication "Benchmarking of Analysis Strategies for Data-Independent Acquisition Proteomics Using a Large-Scale Dataset Comprising Inter-Patient Heterogeneity"
<p>Source data to publication "Benchmarking of Analysis Strategies for Data-Independent Acquisition Proteomics Using a Large-Scale Dataset Comprising Inter-Patient Heterogeneity".</p> <p>Data and further information at GitHub repository https://github.com/kreutz-lab/dia-benchmarking (DOI: 10.5281/zenodo.6371925)</p>
Metabolic and lipidomic data of patients with idiopathic pulmonary fibrosis and healthy volunteers
<p>The metabolomic / lipidomic datasets used in the manuscript draft entiteld: </p> <p>"Changes in Serum Metabolomics in Idiopathic Pulmonary Fibrosis and effect of approved antifibrotic medication"</p> <p>by</p> <p>Benjamin Seeliger, Alfonso Carleo, Pedro David Wendel-Garcia, Jan Fuge, Ana Montes Worboys, Sven Schuchardt, Maria Molina-Molina and Antje Prasse</p> <p>Data is untransformed and missing data were imputated. All values are in µmol/L.</p>
The input data set includes 729 objects (patients) and 39 variables (clinical qualitative and quantitative descriptors).
<p>For reliable data treatment and interpretation qualitative descriptors were omitted and only numerical clinical indicators were included in the data matrix. Finally, the data set dimension was [729 x 18].</p> <p> The data were treated by hierarchical cluster analysis and factor analysis. The major goal of the data mining was to reach statistically significant partitioning of the objects and variables into similarity patterns (clusters) which helps to better understand the data structure, to assess the meaning of the partitioning achieved, thus promoting the evaluation of the health status of the patients and the role of specific descriptors for the formation of the partitioning patterns.</p> <p>3D classification Python tool.</p>
A dataset of anonymised hospitalised COVID-19 patient data: outcomes, demographics and biomarker measurements for two New York hospitals
<p>These datasets are for a cohort of n=1540 anonymised hospitalised COVID-19 patients, and the data provide information on outcomes (i.e. patient death or discharge), demographics and biomarker measurements for two New York hospitals: State<br> University of New York (SUNY) Downstate Health Sciences University and Maimonides<br> Medical Center.</p> <p>The file "demographics_both_hospitals.csv" contains the ultimate outcomes of hospitalisation (whether a patient was discharged or died), demographic information and known comorbidities for each of the patients.</p> <p>The file "dynamics_clean_both_hospitals.csv" contains cleaned dynamic biomarker measurements for the n=1233 patients where this information was available and the data passed our various checks (see https://doi.org/10.1101/2021.11.12.21266248 for information of these checks and the cleaning process). Patients can be matched to demographic data via the "id" column.</p> <p><strong>Study approval and data collection</strong></p> <p>Study approval was obtained from the State University of New York (SUNY) Downstate Health Sciences University Institutional Review Board (IRB\#1595271-1) and Maimonides Medical Center Institutional Review Board/Research Committee (IRB\#2020-05-07). A retrospective query was performed among the patients who were admitted to SUNY Downstate Medical Center and Maimonides Medical Center with COVID-19-related symptoms, which was subsequently confirmed by RT PCR, from the beginning of February 2020 until the end of May 2020. Stratified randomization was used to select at least 500 patients who were discharged and 500 patients who died due to the complications of COVID-19. Patient outcome was recorded as a binary choice of “discharged” versus “COVID-19 related mortality”. Patients whose outcome was unknown were excluded. Demographic, clinical history and laboratory data was extracted from the hospital’s electronic health records.</p>
Brain functional connectivity data in anesthetized participants and patients with neuropathological or psychiatric diagnoses
<p>Five fMRI datasets were collected from independent research sites including: propofol deep sedation (PDS; drug effect site concentration= ~2.4 μg/ml) in Dataset-1, propofol general anesthesia (PGA; drug effect site concentration= 4.0 μg/ml) in Dataset-2, ketamine anesthesia (KA) in Dataset-3, unresponsive wakefulness syndrome (UWS) in Dataset-4, and schizophrenia (SCHZ), bipolar disorder (BD), and attentional deficit hyperactivity disorder (ADHD) in Dataset-5. Following fMRI data preprocessing, the fMRI time courses were extracted from 400 cortical areas according to a well-established brain parcellation scheme (Schaefer's 400 ROIs). A connectivity matrix was then calculated using Pearson correlation resulting in a 400x400 connectivity matrix for each participant and each condition.</p>
Data set - What Defines Quality of Life for Older Patients Diagnosed with Cancer? A Qualitative Study
<p><strong>Data set from- What Defines Quality of Life for Older Patients Diagnosed with Cancer? A Qualitative Study</strong></p> <p><strong>Abstract of the study: </strong>The treatment of cancer can have a significant impact on quality of life in older patients and this needs to be taken into account in decision making. However, quality of life can consist of many different components with varying importance between individuals. We set out to assess how older patients with cancer define quality of life and the components that are most significant to them. This was a single-centre, qualitative interview study. Patients aged 70 years or older with cancer were asked to answer open-ended questions: What makes life worthwhile? What does quality of life mean to you? What could affect your quality of life? Subsequently, they were asked to choose the five most important determinants of quality of life from a predefined list: cognition, contact with family or with community, independence, staying in your own home, helping others, having enough energy, emotional well-being, life satisfaction, religion and leisure activities. Afterwards, answers to the open-ended questions were independently categorized by two authors. The proportion of patients mentioning each category in the open-ended questions were compared to the predefined questions. Overall, 63 patients (median age 76 years) were included. When asked, “What makes life worthwhile?”, patients identified social functioning (86%) most frequently. Moreover, to define quality of life, patients most frequently mentioned categories in the domains of physical functioning (70%) and physical health (48%). Maintaining cognition was mentioned in 17% of the open-ended questions and it was the most commonly chosen option from the list of determinants (72% of respondents). In conclusion, physical functioning, social functioning, physical health and cognition are important components in quality of life. When discussing treatment options, the impact of treatment on these aspects should be taken into consideration.</p> <p><strong>Reference of research paper: </strong>Seghers PAL, Kregting JA, van Huis-Tanja LH, Soubeyran P, O'Hanlon S, Rostoft S, Hamaker ME, Portielje JEA. What Defines Quality of Life for Older Patients Diagnosed with Cancer? A Qualitative Study. <em>Cancers</em>. 2022; 14(5):1123. https://doi.org/10.3390/cancers14051123</p> <p><strong>Content of the data set: </strong>The first Tab describes what questions were asked, the second tab shows all individual anonymised answers to the open questions, the fourth shows the definitions that were used to classify all answers. Q1-Q4 show how the answers were categorised. </p>
Processed data for FOXA2 analysis in TCGA KIRP and KIRC patients
<pre># Data and code to test whether FOXA2 is changed in KIRP patients with low FH ## Code https://github.com/ArianeMora/foxa2_kirp_kirc ## Datasets RNA count data were downloaded from TCGA (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) using scidat (https://github.com/ArianeMora/scidat) for patients with kidney cancers (rna_df.csv). ## Processing The kidney cancer patient count data were split into KIRC and KIRP, and only the tumour data was used for this analysis, see notebook FOXA2.ipynb in the code folder. Samples were split by their expression of FH in their tumour samples, with several annotations used to separate patients for completeness: 1. Low-High: Comparing the bottom 25% (< Q1) of patients by FH vs “high” FH (i.e. top 25%, > Q3): p.adj 0.00004 2. Low-Normal: Comparing the bottom 25% of patients by FH to the patients with “normal” range FH (between Q1 and Q3): p.adj 0.053 3. Outlier-High: Comparing outlier FH to “high” (i.e. top 25%): p.adj 0.067 4. Outlier-Normal: Comparing the outlier FH (Q1 – 1.5*IQR) to all “normal” FH patients: p.adj 0.169 We did the same for KIRC patients – we don’t see FOXA2 as expected 1. Comparing the bottom 25% of patients by FH vs the top 25% of patients with FH: 0.14 2. Comparing the bottom 25% of patients by FH to the patients with “normal” range FH: 0.25 3. Comparing the outlier FH to all “normal” FH patients: 0.31 4. Comparing outlier FH to “high” (i.e. top 25%): 0.32 Each of these groups were used to also perform DE analysis between the two groups, see respective RMD files in code for details. ### References If you use this work please cite TCGA: ``` Creighton, C. J., Morgan, M., Gunaratne, P. H., Wheeler, D. A., Gibbs, R. A., Gordon Robertson, A., Chu, A., Beroukhim, R., Cibulskis, K., Signoretti, S., Vandin Hsin-Ta Wu, F., Raphael, B. J., Verhaak, R. G. W., Tamboli, P., Torres-Garcia, W., Akbani, R., Weinstein, J. N., Reuter, V., Hsieh, J. J., … University of North Carolina at Chapel Hill. (2013). Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature, 499(7456), Article 7456. https://doi.org/10.1038/nature12222 ``` </pre>
MOTU data. FHIR-standardized data collection on the clinical rehabilitation pathway of trans-femoral amputation patients.
<h3>Dataset presented in the article "MOTU on FHIR: A 10-year data collection on the clinical rehabilitation pathway of 1006 trans-femoral amputees".</h3> <p>Data has been anonymised prior the publication. The data are in Comma-separated-values (CSV) format. </p> <p>This work has been conducted within the framework of the MOTU++ project (PR19-PAI-P2).</p> <p><span>This research was co-funded by the Complementary National Plan PNC-I.1 "Research initiatives for innovative technologies and pathways in the health and welfare sector” D.D. 931 of 06/06/2022, DARE - DigitAl lifelong pRevEntion initiative, code PNC0000002, CUP: (B53C22006450001) and by the Italian National Institute for Insurance against Accidents at Work (INAIL) within the MOTU++ project (PR19-PAI-P2). </span></p> <p><span>Authors express their gratitude to all the AlmaHealthDB Team.</span></p>
Supplementary data to: Detection of Neoantigen-specific T Cells Following a Personalized Vaccine in a Patient with Glioblastoma
<p>Supplemental Data for the patient described in the manuscript: "Detection of Neoantigen-specific T Cells Following a Personalized Vaccine in a Patient with Glioblastoma". Summary of somatic variant calls from DNA whole exome, gene FPKM from RNA sequencing, and neoantigen predictions for high-affinity (ic<sub>50</sub> <500 nM) candidates.</p>
Patients data for meta-analysis of genotype-phenotype associations in Bardet-Biedl Syndrome
<p>Data used for metaanalysis of the genotype-phenotype relationship in Bardet Biedl Syndrome.</p> <p>File "EV table 1 literature.xlsx" describes studies that were included in the metaanalysis. File "EV table 2 dataset.xlsx" contains individual patient data. Each row corresponds to a patient. If the same patient was reported in more than 1 study, their data were merged into one row. The columns are as follows:</p> <p>* source - a citation to the study the patient originated in</p> <p>* FamilyID - randomly generated ID of a family (unique over the dataset), two persons with the same FamilyID are related.</p> <p>* source case n. - A unique identifier of the patient within the study</p> <p>* gene - A gene carrying the principal BBSome related mutation</p> <p>* nucleotide change (allele 1,2) - description of the mutations in DNA individual alleles of the gene, in HGVS nomenclature</p> <p>* protein change (allele 1,2) - description of how the mutations in DNA change the resulting protein, in HGVS nomenclature</p> <p>* type of mut allele 1,2 - whether the given mutation is considered missense (MS) or large truncation (trunc)</p> <p>* mut/mut - combination of mutations for both alleles</p> <p>* additional mutations - mutations in other BBSome-related genes. Format is "gene: DNA mutation, protein mutation"</p> <p>* sex - "F" or "M" (where reported)</p> <p>* age group - age group (where reported)</p> <p>* age - age in years. Contains fractions, decimal values and "5 month"</p> <p>* RD, OBE, PD, CI, REP, REN, HEART, LIV, DD - presense or absence of phenotypes, if reported. RD – retinal dystrophy, OBE – obesity, PD – polydactyly, CI – cognitive impairment , REP – reproductive system anomalies, REN – renal anomalies, HRT – heart disease, LIV – liver anomalies, DD - Developmental delay. Values are "" (not reported), "0" (no phenotype), "1" (phenotype present), "1!" conflicting reports of phenotype in multiple studies (some patients were involved in multiple studies)</p> <p>* ethnicity - ethnicity of the patient, if reported</p> <p>* ethinc group - grouping of the ethnicities into 8 larger groups (see paper for details)</p> <p>* note - miscellanous text, in particular contains notes on patients merged from multiple studies</p> <p>====</p> <p>The protocol for this meta-analysis was pre-registered with PROSPERO (CRD42018096099).</p> <p>PubMed and Google Scholar databases were searched in May 2018 for the following keywords: [bardet-biedl syndrome AND (genotype phenotype OR cohort)]. Other suitable records were identified by snowball searching, in particular, by retrieving relevant articles from the references of the studied full-texts. In addition, all the references included in the publicly available Euro-Wabb database (<a href="https://lovd.euro-wabb.org/home.php">https://lovd.euro-wabb.org/home.php</a>) were covered. Our search was limited to the literature published in English language and covered the period from the inception of each database to the 21st of May 2018.</p>
SPSS data of malaria patient in East Nile locality, Khartoum, Sudan
<p>Column 1= Age</p> <p>Column 2= Sex (male=1, female=2)</p> <p>Column 3= Residence (E.N= East Nile Locality)</p> <p>Column 4= Occupation (Government=1, Private=2, home worker=3, Student=4, Child=5)</p> <p>Column 5= Chronic disease (Diabetes mellitus =1, Blood pressure=2, Heart disease=3, Asthma=4, </p> <p> Healthy=5, Renal disease=6)</p> <p>Column 6 = Recurrent infection (yes=1, No = 2)</p> <p>Column 7 = Test sensitivity (yes=1, No=2)</p> <p>Column 8 = Treatment (Quinine=1, Quartum=2, Artemether=3, Not take any treatment= 4)</p> <p>Column 9= Changing stored water (yes=1, No=2)</p> <p>Column 10: Do you use bed nets? (yes=1, No=2)</p> <p>Column 11= Spraying insecticides (yes=1, No=2)</p> <p>Column 12= Blood film (Positive=1, Negative =2)</p> <p>Column 13= Plasmodium species (P.falciparum= 1, P. vivax= 2, Mixed infection=3)</p> <p>Column 14= Plasmodium stage (Ring stage=1, Late trophozoite =2, Schizont=3)</p> <p>Column 15= Density of parasite (+=1, ++=2,+++=3,++++=4)</p> <p>Column 16= Interferon</p> <p>Column 17= Interleukin 10</p> <p>Column 18= Tumer necrosis factor</p> <p>Column 19= Control sample Interferon</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.