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17,049 results for “CHILDREN”
DICOM converted whole slide hematoxylin and eosin images of rhabdomyosarcoma from Children's Oncology Group trials
<p>Rhabdomyosarcoma (RMS) is an aggressive soft-tissue sarcoma, which primarily occurs in children and young adults. This dataset contains manifests referring to the hematoxylin and eosin (H&E) stained images in Digital Imaging and Communications in Medicine (DICOM) format available from National Cancer Institute Imaging Data Commons (IDC) [1] (also see IDC Portal at <a href="https://imaging.datacommons.cancer.gov">https://imaging.datacommons.cancer.gov</a>) as of data release v16. The original images in vendor-specific format were collected on IRB-approved clinical trials or tissue banking studies from Children’s Oncology Group (COG) patients enrolled on ARST0331, ARST0431, D9602, D9803, and D9902 trials, as described in [2]. Those images, augmented with the metadata describing their content, were provided to the IDC team for the purposes of archival, and were converted into DICOM Whole Slide Microscopy (SM) representation [3], [4] using custom open source scripts and tools available and described here [5]. The resulting converted images were released in IDC in the RMS-Mutation-Prediction collection with the data release v16.</p> <p>To conveniently explore the data available for this dataset, please use this dashboard: <a href="https://lookerstudio.google.com/reporting/7f267400-8774-42e1-b5d1-ca11863c52a9">https://lookerstudio.google.com/reporting/7f267400-8774-42e1-b5d1-ca11863c52a9</a>.</p> <p>Notebooks demonstrating how to use this data are available here: <a href="https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/collections_demos/rms_mutation_prediction">https://github.com/ImagingDataCommons/IDC-Tutorials/tree/master/notebooks/collections_demos/rms_mutation_prediction</a>.</p> <p>Clinical data accompanying the images is available via SQL interface in IDC BigQuery tables, see details on accessing IDC clinical data in the respective tutorial (<a href="https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/clinical_data_intro.ipynb">https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/clinical_data_intro.ipynb</a>).</p> <p>The images referred to by the accompanying manifests can be explored and visualized using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/">https://portal.imaging.datacommons.cancer.gov/explore/</a>. Direct link to open the collection is <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=rms_mutation_prediction">https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=rms_mutation_prediction</a>.</p> <p>The GCP and AWS manifests provided with this dataset record can be used to download the corresponding files from the IDC Google Cloud Storage (GCS) or Amazon S3 (AWS) buckets free of charge following the instructions available in IDC documentation here: <a href="https://learn.canceridc.dev/data/downloading-data">https://learn.canceridc.dev/data/downloading-data</a>. Specifically, you will need to install the s5cmd command line tool on your computer (see instructions at <a href="https://github.com/peak/s5cmd#installation">https://github.com/peak/s5cmd#installation</a>), and follow the manifest-specific download instructions accompanying the file list below.</p> <p>If you use the files referenced in the attached manifests, we ask you to please cite this dataset, as well as the publication describing the original dataset [2] and the publication acknowledging IDC [1].</p> <p>Specific files included in the record are:</p> <ol> <li> <p><strong><code>rms_mutation_prediction_gcs.s5cmd</code></strong>: GCS-based manifest (to download the files described in the manifest, execute this command: <code>s5cmd --no-sign-request --endpoint-url https://storage.googleapis.com run rms_mutation_prediction_gcs.s5cmd</code>)</p> </li> <li> <p><strong><code>rms_mutation_prediction_aws.s5cmd</code></strong>: AWS-based manifest (to download the files described in the manifest, execute this command: <code>s5cmd --no-sign-request --endpoint-url https://s3.amazonaws.com run rms_mutation_prediction_aws.s5cmd</code>)</p> </li> <li> <p><strong><code>rms_mutation_prediction_dcf.csv</code></strong>: Gen3-based manifest (see details in <a href="https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>).</p> </li> </ol> <p><strong>References</strong></p> <p>[1] A. Fedorov et al., "NCI Imaging Data Commons," Cancer Res., vol. 81, no. 16, pp. 4188–4193, Aug. 2021, doi: <a href="https://dx.doi.org/10.1158/0008-5472.CAN-21-0950">10.1158/0008-5472.CAN-21-0950</a>. </p> <p>[2] D. Milewski et al., "Predicting molecular subtype and survival of rhabdomyosarcoma patients using deep learning of H&E images: A report from the Children's Oncology Group," Clin. Cancer Res., vol. 29, no. 2, pp. 364–378, Jan. 2023, doi: <a href="https://dx.doi.org/10.1158/1078-0432.CCR-22-1663">10.1158/1078-0432.CCR-22-1663</a>.</p> <p>[3] National Electrical Manufacturers Association (NEMA), "DICOM PS3.3 - Information Object Definitions: A.32.8 VL Whole Slide Microscopy Image IOD." Accessed: Aug. 11, 2023. [Online]. Available: <a href="https://dicom.nema.org/medical/dicom/current/output/html/part03.html#sect_A.32.8">https://dicom.nema.org/medical/dicom/current/output/html/part03.html#sect_A.32.8</a></p> <p>[4] M. D. Herrmann et al., "Implementing the DICOM standard for digital pathology," J. Pathol. Inform., vol. 9, no. 1, p. 37, Jan. 2018, doi: <a href="https://dx.doi.org/10.4103/jpi.jpi_42_18">10.4103/jpi.jpi_42_18</a>. </p> <p>[5] D. Clunie, A. Fedorov, and M. D. Herrmann, ImagingDataCommons/idc-wsi-conversion: Initial release. Zenodo, 2023. doi: <a href="https://dx.doi.org/10.5281/zenodo.8240154">10.5281/zenodo.8240154</a>. </p>
Investigating the Effects of Embodiment on Emotional Categorization of Faces and Words in Children and Adults
<p>The three data files uploaded here contain the data used for the analyses in experiments 1a, 1b, and 2 as described in the article carrying the same title as this dataset, published in the journal Frontiers in Psychology. All analyses were carried out in SPSS version 22 as described in the published article.</p> <p>Article Abstract:</p> <p>The facial feedback hypothesis (FFH) indicates that besides being involved in the production of facial expressions, the musculature of the face also influences one’s perception of emotional stimuli. Recently, this effect has been the focus of increased scrutiny as efforts to replicate a key study with adult participants supporting this hypothesis, using the so-called “pen-in-the-mouth” task, have not been successful at several labs. Our series of experiments attempted to investigate whether the assumed embodiment effect can be reproduced in a simplified emotional categorization task for emotional faces and words. We also wanted to test whether the embodiment effect can be detected in children because it is assumed that their bodily processes are especially closely linked with their sensory and cognitive processes. Our experiments involved child and adult participants categorizing faces and words as positive or negative as quickly as possible, while inducing a positive or negative facial or bodily state (holding a straw in the mouth such that a smile or a frown was generated, or creating a positive or negative body posture). The positive or negative facial and bodily states could therefore be either congruent or incongruent with the valence of the target face and word stimuli. Our results did not show any significant differences between the congruent and incongruent conditions in either children or adults. This suggests that embodiment effects either do not significantly impact valence-based categorization or are not strong enough to be detected by our approach considering the sample size in the present study.</p>
Data for "Gut microbial genes are associated with neurocognition and brain development in healthy children"
<p><strong>Datasets accompanying<em> Gut microbial genes are associated with neurocognition and brain development in healthy children</em>, submitted to Nature Microbiology.</strong></p> <p><strong>Contents:</strong></p> <ul> <li> fecal_samples_master.csv <ul> <li>Metadata for all fecal samples processed by the Klepac-Ceraj Lab at Wellesley College</li> </ul> </li> <li>filemakerdb.csv <ul> <li>Initial export and parsing (long form) of deidentified patient metadata from internal filemnaker pro database</li> </ul> </li> <li>gbm.txt <ul> <li>Info about potentially neuroactive gene sets</li> <li>This was acquired as Supplementary Dataset 1 from <a href="https://doi.org/10.1038/s41564-018-0337-x">https://doi.org/10.1038/s41564-018-0337-x</a></li> </ul> </li> <li>batchXXX_analysis_noknead.tar.gz <ul> <li>Sequencing batches 001-012 (see fecal_samples_master.csv for metadata about samples contained in each batch)</li> <li>Each tarball contains: <ul> <li><strong>cluster.yaml</strong>: configuration file for snakemake pipeline (<a href="https://github.com/Klepac-Ceraj-Lab/snakemake_workflows">repo link</a>)</li> <li><strong>config.yaml</strong>: run configuration for snakemake pipeline</li> <li><strong>.snakemake/</strong>: metadata about snakemake pipeline runs on engaging cluster at MIT</li> <li><strong>output/</strong>: outputs from metaphlan2 and humann2 analysis runs. Note: kneaddata sequence files were not included, but will be uploaded to SRA (link to come)</li> </ul> </li> </ul> </li> <li>All <a href="https://www.uniprot.org/">uniprot</a> searches were performed 2019-09-19 <ul> <li>uniprot-abxr.tsv <ul> <li>search term: "keyword:\"Antibiotic resistance [KW-0046]\" AND reviewed:yes"</li> </ul> </li> <li>uniprot-carbohydrate.tsv <ul> <li>search term: "keyword:\"Carbohydrate metabolism [KW-0119]\" AND reviewed:yes"</li> </ul> </li> <li>uniprot-fa.tsv <ul> <li>search term: (keyword:\"Fatty acid biosynthesis [KW-0275]\" OR keyword:\"Fatty acid metabolism [KW-0276]\") AND reviewed:yes"</li> </ul> </li> </ul> </li> </ul>
Operation of a three terminal solar cell for children
<p>The video describes in a simple manner the operation of a three terminal solar cell. Blue arrows represent the high energy photons. Red arrows represent the low energy photons, that are absorbed deeper in the cell. Each colored square represents a semiconductor layer. White balls, represent electrons. Black ball represent holes. For a more detailed description of a particular kind of three terminal solar cell, see for example:</p> <p> A. Martí and A. Luque, “Three-terminal heterojunction bipolar transistor solar cell for high-efficiency photovoltaic conversion,” <em>Nat. Commun.</em>, vol. 6, pp. 6902–6902, Apr. 2015. (<a href="https://doi.org/10.1038/ncomms7902">https://doi.org/10.1038/ncomms7902</a>)</p> <p>The video has been created in Blender 2.82. (<a href="https://www.blender.org/">https://www.blender.org/</a>). Both the video and the source file with which we created it are attached</p>
Self-efficacy questionnaire for children - Results
<p>The dataset (csv) contains data from a self-efficacy questionnaire for children between the age of 6 and 16. This questionnaire (also attached) was specifially developed for the purposes of the DOIT project, which aimed at bringing entrepreneurial education, maker skills and social innovation together and at developing an educational programme lasting for at least 15 hours.</p> <p>Children participating in the DOIT progamme filled in the survey two times (pre and post survey). The dataset contains the questionnaire results from 633 children from 10 different European countries, some basic demographic (age, gender, country, disability) and some variables regarding the DOIT programme (attended hours, Age of facilitators, gender ratio of facilitator, etc.). For context information also a related DOIT report has been attached.</p>
Children speech recording (English, spontaneous speech + pre-defined sentences)
<p>The dataset contains audio recordings (lossless WAV) of 11 young children (age M=4.9 years old; 5 females, 6 males).</p> <p>Recordings include:</p> <ul> <li>free speech (retelling a picture book, ‘Frog, Where Are You?’ by Mercer Mayer)</li> <li>repeating 5 pre-defined short sentences (like 'the horse is in the stable')</li> <li>telling the numbers from 1 to 10</li> </ul> <p>The recordings are in English and the participants include both native and non-native speakers.</p> <p>Each sample is recorded from 3 sources:</p> <ul> <li>A studio-grade microphone (Rode NT1-A)</li> <li>A portable microphone (Zoom H1)</li> <li>The two front microphones of the Aldebaran NAO robot</li> </ul> <p>(note that, due to technical issues, a few (sample/microphone) combinations are missing).</p> <p> </p> <p>For the free-speech recording, a manual segmentation of the utterances is provided as well.</p>
Genomic investigations of unexplained acute hepatitis in children
<p>Raw genomic sequencing data, enriched for either Human Adenovirus, Adeno-associated Virus 2 or Human Herpesvirus 6, after removal of host reads. Please check the reference for detailed metadata for each sample. </p><p>Morfopoulou, S., Buddle, S., Torres Montaguth, O.E. et al. Genomic investigations of unexplained acute hepatitis in children. Nature 617, 564–573 (2023). https://doi.org/10.1038/s41586-023-06003-w</p>
Raw data of healthy children and adults in a visual learning task, a conditional learning task and a transitive inference task.
<p>Raw data of 71<strong> </strong>healthy children (31 girls; average age: 6.42 years; range: 2.95-11.64 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p><p>Raw data of 22 healthy adults (11 femaleswomen; average age: 26.05 years; range: 20.32-29.76 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p>
Data from a cross-sectional study of fifth grade children in a sample of primary schools in Belgium that differ in amount of greenness at school and landscape level
<p>The data in this deposit were collected as part of the <code>B@SEBALL</code> project (Biodiversity at School Environments - Benefits for All). </p> <p>The project investigated how biodiversity in the school environment can positively affect children’s health and mental well-being. <code>B@SEBALL</code> also investigated the opportunities for reducing health inequalities among children via biodiversity at school environments.</p> <p>The data are organized according to the <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package standard</a>. All child-level and school-level data have been anonymized. Each data package is a collection of <code>csv</code> files and a <code>json</code> file. The <code>json</code> file holds descriptive information for all variables in all <code>csv</code> files. The <code>zip</code> file contains two frictionless data packages. The data packages contain information on 37 primary schools and 513 children. </p> <p>The data package, <code>data_package_an_zenodo_cleaned_data</code>, contains the original data in a tidied and cleaned format. It consists of 46 <code>csv</code> files. The files relate to the following contents:</p> <table> <tbody> <tr> <td><strong>contents</strong></td> <td><strong>filename</strong></td> </tr> <tr> <td>metadata file</td> <td>datapackage.json</td> </tr> <tr> <td>landscape level variables</td> <td>wp1_landscape_level_data.csv</td> </tr> <tr> <td>metadata about participants</td> <td>wp2_participants_metadata.csv</td> </tr> <tr> <td>general school level data</td> <td>wp2_school_data.csv</td> </tr> <tr> <td>pollution data at school level</td> <td>wp3_ua_sirm_data.csv</td> </tr> <tr> <td>classroom data about air quality</td> <td>wp3_ucl_classroom_airquality.csv</td> </tr> <tr> <td>area of ecotopes in the school environment</td> <td>wp3_ucl_ecotope_categories.csv</td> </tr> <tr> <td>greenness indicators for the school environment derived from ecotopes</td> <td>wp3_ucl_greenness_indicators.csv</td> </tr> <tr> <td>greenness indicators for the school environment derived from ecotopes</td> <td>wp3_ucl_greenness_key.csv</td> </tr> <tr> <td>greenness indicators for the school environment derived from ecotopes</td> <td>wp3_ucl_greenpatches.csv</td> </tr> <tr> <td>playground biodiversity indicators</td> <td>wp3_ucl_playground_biodiversity.csv</td> </tr> <tr> <td>d2-test of attention data</td> <td>wp4_d2_data_by_child.csv</td> </tr> <tr> <td>d2-test of attention data</td> <td>wp4_d2_data_by_line.csv</td> </tr> <tr> <td>d2-test of attention data</td> <td>wp4_d2_data_by_linegroup.csv</td> </tr> <tr> <td>Self-reported allergy data</td> <td>wp4_isaac_data.csv</td> </tr> <tr> <td>Self-reported allergy data</td> <td>wp4_isaac_questions.csv</td> </tr> <tr> <td>Self-reported well-being data</td> <td>wp4_kidscreen_data.csv</td> </tr> <tr> <td>Self-reported well-being data</td> <td>wp4_kidscreen_questions.csv</td> </tr> <tr> <td>Self-reported attitude toward outdoor play</td> <td>wp5_atop_data.csv</td> </tr> <tr> <td>Self-reported attitude toward outdoor play</td> <td>wp5_atop_questions.csv</td> </tr> <tr> <td>Guardian-reported general questions</td> <td>wp5_guardians_general_questions_data.csv</td> </tr> <tr> <td>Guardian-reported general questions</td> <td>wp5_guardians_general_questions_key.csv</td> </tr> <tr> <td>Guardian-reported protection from risk</td> <td>wp5_guardians_risk_protection_data_part1.csv</td> </tr> <tr> <td>Guardian-reported protection from risk</td> <td>wp5_guardians_risk_protection_data_part2.csv</td> </tr> <tr> <td>Guardian-reported protection from risk</td> <td>wp5_guardians_risk_protection_key.csv</td> </tr> <tr> <td>Self-reported nature connectedness</td> <td>wp5_nc_data.csv</td> </tr> <tr> <td>Self-reported nature connectedness</td> <td>wp5_nc_key.csv</td> </tr> <tr> <td>Parent-reported allergy data</td> <td>wp5_parents_allergy_related_questions_data.csv</td> </tr> <tr> <td>Parent-reported allergy data</td> <td>wp5_parents_allergy_related_questions_key.csv</td> </tr> <tr> <td>Parent-reported cultural background</td> <td>wp5_parents_cultural_background_data.csv</td> </tr> <tr> <td>Parent-reported cultural background</td> <td>wp5_parents_cultural_background_key.csv</td> </tr> <tr> <td>Parent-reported general questions</td> <td>wp5_parents_general_questions_data.csv</td> </tr> <tr> <td>Parent-reported general questions</td> <td>wp5_parents_general_questions_key.csv</td> </tr> <tr> <td>Parent-reported independent mobility data</td> <td>wp5_parents_independent_mobility_data.csv</td> </tr> <tr> <td>Parent-reported independent mobility data</td> <td>wp5_parents_independent_mobility_key.csv</td> </tr> <tr> <td>Parent-reported living environment</td> <td>wp5_parents_living_environment_data.csv</td> </tr> <tr> <td>Parent-reported living environment</td> <td>wp5_parents_living_environment_key.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part1.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part2.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part3.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part4.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_key.csv</td> </tr> <tr> <td>Parent-reported risk protection data</td> <td>wp5_parents_risk_protection_data_part1.csv</td> </tr> <tr> <td>Parent-reported risk protection data</td> <td>wp5_parents_risk_protection_data_part2.csv</td> </tr> <tr> <td>Parent-reported risk protection data</td> <td>wp5_parents_risk_protection_key.csv</td> </tr> <tr> <td>Parent-reported data relating to socio-economic status</td> <td>wp5_parents_ses_questions_data.csv</td> </tr> <tr> <td>Parent-reported data relating to socio-economic status</td> <td>wp5_parents_ses_questions_key.csv</td> </tr> </tbody> </table> <p> </p> <p>The <code>data_package_an_zenodo_derived_data</code> data package, contains derived data that was calculated based on input from <code>data_package_an_zenodo_cleaned_data</code> at either child-level or at school-level.</p> <table> <tbody> <tr> <td><strong>contents</strong></td> <td><strong>filename</strong></td> </tr> <tr> <td>metadata file</td> <td>datapackage.json</td> </tr> <tr> <td>derived data at child level</td> <td>wp1_child_level_key_variables.csv</td> </tr> <tr> <td>derived attention score based on d2-test data, aggregated to line-level</td> <td>wp1_d2_by_line_attention_score.csv</td> </tr> <tr> <td>derived data at school level</td> <td>wp1_school_level_key_variables.csv</td> </tr> </tbody> </table> <p>These data packages only store information for participants that gave consent for a particular part of the study and that gave consent for long-term storage of the data. There may therefore be slight differences between results published as part of the project consortium, which could make use of participant data that did not give consent for long-term data storage, and reproduction of these results based on the data in this data repository. We also note that the derived variables in the derived data package were calculated with these participants included and removal of participants for which we had no long-term storage consent was done after these calculations.</p> <p>As part of the project, microbiome data were also collected (both from cheek swabs on the children and from environmental samples), but this part of the data are not a part of this deposit and will be deposited in the European Nucleotide Archive (ENA).</p>
Data associated with the article 'Intervention factors associated with efficacy, when targeting oral language comprehension of children with or at risk for (Developmental) Language Disorder: A meta-analysis'
<p>The efficacy of oral language comprehension interventions varies, but the reasons for this variation have received little attention. A meta-analysis was conducted to examine intervention factors associated with the efficacy (as expressed with effect sizes) of oral language comprehension interventions in children under the age of 18 with or at risk for (Developmental) Language Disorder, (D)LD.</p> <p>The meta-analysis article together with this additional material comprise the content needed for a thorough understanding and replication of the results.</p> <p>This dataset is based on two systematic scoping reviews on oral language comprehension interventions (Tarvainen et al., 2020, 2021). Further information from the sourced articles was extracted for this study titled ‘Intervention factors associated with efficacy, when targeting oral language comprehension of children with or at risk for (Developmental) Language Disorder: A meta-analysis’. </p> <p>In the future, we hope that this data is used with a growing body of oral language comprehension interventions to conduct further and more detailed examinations of intervention factors associated with efficacy.</p> <p>References:</p> <p>Tarvainen, S., Launonen, K., & Stolt, S. (2021). Oral language comprehension interventions in school-age children and adolescents with developmental language disorder: A systematic scoping review. <em>Autism & Developmental Language Impairments</em>, <em>6</em>, 1–24. https://doi.org/10.1177/23969415211010423</p> <p>Tarvainen, S., Stolt, S., & Launonen, K. (2020). Oral language comprehension interventions in 1–8-year-old children with language disorders or difficulties: A systematic scoping review. <em>Autism & Developmental Language Impairments</em>, <em>5</em>, 1–24. https://doi.org/10.1177/2396941520946</p> <p> </p>
Investigating the effects of COVID‑19 lockdown on Italian children and adolescents with and without neurodevelopmental disorders: a cross‑sectional study - DATASET
<p>Dataset to support the findings in the journal paper titled "Investigating the effects of COVID‑19 lockdown on Italian children and adolescents with and without neurodevelopmental disorders: a cross‑sectional study".</p> <p>Each row is a different subject.</p> <p>Each column represents an answer to the questionnaire. For single choice questions, the answer was reported as-is (Italian). For multiple choice questions, the alternatives where splitted in several columns and the answer was coded as 0/1 (one hot encoding). For the "school" column, 2=primary school, 3=middle school, 4=high school. For the "school.class" column, classes from 4 to 8 belong to primary school, from first to fifth grade; classes from 9 to 11 belong to middle school, from first to third grade; classes from 12 to 16 belong to high school, from first to fifth grade.</p>
Dataset for the identification of hypertension in school-aged children from Gqeberha, South Africa
<p>Dataset used to evaluate and compare different international references to identify hypertension among South African school-aged children from disadvantaged communities.</p> <p>It encompasses anonymized, unique, identification numbers, anthropometric and blood pressure measures, as well as blood pressure percentiles and the assigned categories derived from four different reference populations (American, German, global and the study population).</p>
A longitudinal study of the associations of children's body mass index and physical activity with blood pressure – dataset
<p>B-Proact1v is a longitudinal study examining changes in children’s physical activity and sedentary behaviours as they progress through primary school. In 2012-2013, 1299 Year 1 children (median age: 6 years) were recruited from 57 schools in greater Bristol, UK (total number of eligible children: 2600; recruitment rate: 50.0%). Following this, data were collected from 1223 Year 4 children (median age: 9 years) from 47 of the original schools between March 2015 and July 2016 (total number of eligible children: 2047; recruitment rate: 59.7%). This included 685 children from the original sample.</p> <p> </p> <p>This dataset represents a subset of the B-Proact1v data to examine the longitudinal associations of children’s body mass index and physical activity with blood pressure. Included in this repository is the dataset and a data dictionary. The dataset includes the variables that underlie the findings in a manuscript entitled ‘A longitudinal study of the associations of children’s body mass index and physical activity with blood pressure’ that has been submitted to PLOS ONE. This dataset has been made available so that future researchers can replicate the study findings using the data. If you wish to use the data for any purpose other than replicating the study findings, please contact the Principal Investigator Professor Russ Jago (russ.jago@bristol.ac.uk) to discuss this.</p>
Assessment of skin autofluorescence and its association with glycated hemoglobin, cardiovascular risk markers and concomitant chronic diseases in children with type 1 diabetes
<p>This is the dataset for the publication "Assessment of skin autofluorescence and its association with glycated hemoglobin, cardiovascular risk markers and concomitant chronic diseases in children with type 1 diabetes".</p>
Supplementary file 30 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 30: Proportion of participants on methylphenidate with asthenia and fatigue </p>
Supplementary file 37 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 37: Proportion of participants on methylphenidate with restlessness and agitation</p>
Supplementary file 66 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 66: Proportion of participants on methylphenidate with gastrointestinal adverse events</p>
Supplementary file 55 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 55: Proportion of participants on methylphenidate with upper respiratory tract infection</p>
Supplementary file 11 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 11: Proportion of participants on methylphenidate with respiratory, thoracic and mediastinal disorders</p>
Supplementary file 22 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 22: Proportion of participants on methylphenidate with difficulty falling asleep</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.