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Unravelling the physiological and psychosocial signatures of pain by machine learning
<p>These datasets include information from 118 subjects, with 81 chronic pain patients across three different cohorts, Complex Regional Pain Syndrome (CRPS), Low Back Pain (LBP) and Spinal Cord Injury with Neuropathic Pain (SCI NP) and 37 healthy subjects. Each participant underwent 40 repetitions of experimentally induced pain, resulting in a total of 4,697 pain trials. Physiological signals (EDA and EEG) and psychosocial information have been recorded and collected. Age, gender, height, weight, BMI, medications, fatigue, sleep quality, perceived health, quality of life, sleep quality, and sick leave and validated questionnaires: Hospital Anxiety and Depression Scale (HADS), Pain Catastrophizing Score (PCS), and Pain Self-Efficacy Questionnaire (PSEQ) and Multidimensional Assessment of Interoceptive Awareness (MAIA).</p> <p>This information has been used for the publication "Unravelling the physiological and psychosocial signatures of pain by machine learning".</p> <p><span><em><strong>Cite this dataset as:</strong></em></span><br>N. Gozzi, G. Preatoni, F. Ciotti, M. Hubli, P. Schweinhardt, A. Curt, S. Raspopovic, Unraveling the physiological and psychosocial signatures of pain by machine learning. Med 0 (2024). <a href="https://doi.org/10.1016/j.medj.2024.07.016" target="_blank" rel="noopener">10.1016/j.medj.2024.07.016</a></p> <p> </p> <p> </p>
Genome-wide association summary statistics for back pain
<p>The dataset contains results of a genome-wide association study of back pain. Two files contain association summary statistics for discovery GWAS based on the analysis of 350,000 white British individuals from the UK Biobank and meta-analysis GWAS based on the meta-analysis of the same 350,000 individuals and additional 103,862 individuals of European Ancestry from the UK biobank (total N = 453,862). The phenotype of back pain was defined by the answer provided by the UK biobank participants to the following question: "Pain type(s) experienced in last month". Those who reported “Back pain”, were considered as cases, all the rest were considered as controls. Individuals who did not reply or replied: "Prefer not to answer" or "Pain all over the body" were excluded. This dataset is also available for graphical exploration in the genomic context at <a href="http://gwasarchive.org/">http://gwasarchive.org</a>. </p> <p>The data are provided on an "AS-IS" basis, without warranty of any type, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose. If investigators use these data, any and all consequences are entirely their responsibility. By downloading and using these data, you agree that you will cite the appropriate publication in any communications or publications arising directly or indirectly from these data; for utilisation of data available prior to publication, you agree to respect the requested responsibilities of resource users under 2003 Fort Lauderdale principles; you agree that you will never attempt to identify any participant. This research has been conducted using the UK Biobank Resource and the use of the data is guided by the principles formulated by the UK Biobank.</p> <p><strong>When using downloaded data, please cite corresponding paper and this repository:</strong></p> <ol> <li>Insight into the genetic architecture of back pain and its risk factors from a study of 509,000 individuals. Freidin, Maxim; Tsepilov, Yakov; Palmer, Melody; Karssen, Lennart; Suri, Pradeep; Aulchenko, Yurii; Williams, Frances MK,# CHARGE Musculoskeletal Working Group. PAIN: February 06, 2019 - Volume Articles in Press - Issue - p<br> doi: 10.1097/j.pain.0000000000001514</li> <li>Maxim B Freidin, Yakov A Tsepilov, Melody Palmer, Lennart Karssen, CHARGE Musculoskeletal Working Group, Pradeep Suri, … Frances MK Williams. (2018). Genome-wide association summary statistics for back pain (Version 1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1319332</li> </ol> <p><strong>Funding:</strong></p> <p>This study was supported by the European Community’s Seventh Framework Programme funded project PainOmics (Grant agreement # 602736). <br> The research has been conducted using the UK Biobank Resource (project # 18219).</p> <p>The development of software implementing SMR/HEIDI test and database for GWAS results was supported by the Russian Ministry of Science and Education under the 5-100 Excellence Program”.</p> <p>Dr. Suri’s time for this work was supported by VA Career Development Award # 1IK2RX001515 from the United States (U.S.) Department of Veterans Affairs Rehabilitation Research and Development Service. The contents of this work do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.</p> <p>Dr. Tsepilov’s time for this work was supported in part by the Russian Ministry of Science and Education under the 5-100 Excellence Program.</p> <p><strong>Column headers - discovery (350K)</strong></p> <ol> <li>CHR: chromosome</li> <li>POS: position (GRCh37 build) </li> <li>ID: SNP rsID</li> <li>REF: reference allele (coded as "0")</li> <li>ALT: effect allele (coded as "1")</li> <li>CASE_ALLELE_CT: allele observation count in cases</li> <li>CTRL_ALLELE_CT: allele observation count in controls</li> <li>ALT_FREQ: effect allele frequency </li> <li>MACH_R2: imputation quality</li> <li>TEST: model of association test (additive)</li> <li>OBS_CT: sample size</li> <li>BETA: effect size of effect allele</li> <li>SE: standard error of effect size</li> <li>T_STAT: Z-value of effect allele</li> <li>P: P-value of association (without GC correction)</li> <li>MAF: minor allele frequency</li> </ol> <p><strong>Column headers - meta-analysis (450K)</strong></p> <ol> <li>MarkerName: SNP rsID</li> <li>Allele1: effect allele (coded as "1")</li> <li>Allele2: reference allele (coded as "0")</li> <li>Freq1: effect allele frequency</li> <li>FreqSE: standard error of effect allele frequency</li> <li>Effect: effect size of effect allele</li> <li>StdErr: standard error of effect size</li> <li>P-value: P-value of association (without GC correction)</li> <li>Direction: sign of effect in discovery and replication samples</li> <li>n_total: Total sample size</li> <li>CHR: chromosome</li> <li>POS: position (GRCh37 build) </li> <li>MACH_R2_discovery: imputation quality in discovery sample</li> </ol>
Cognitive control of sensory pain encoding in the pregenual anterior cingulate cortex. d1 - decoder construction in day 1, d2 - adaptive control in day 2.
Open the record for dataset details and reuse information.
PsPM-SCRV6: Skin conductance responses to pain by electric stimulation
<p>This dataset includes skin conductance response (SCR) measurements for each of 20 healthy unmedicated participants (10 males and 10 females aged 21.8+/-3.3 years) in response to 10 discomforting electric shocks. Stimuli are 0.5ms wide square current pulse repeated at 500Hz for 100ms. Amplitude is varied (mean +/- SD: 0.78mA +/- 0.43mA). ITI is selected randomly on each trial from 29s, 34s or 39s.</p>
Dataset Musculoskeletal pain in health professionals at the end of their studies and 1 year after entry into the profession: A multi-center longitudinal study.
<p>Dataset related to the paper "Musculoskeletal pain in health professionals at the end of their studies and 1 year after entry into the profession: A multi-center longitudinal study."</p> <p><strong>Abstract</strong></p> <p>Background: Musculoskeletal pain, especially back pain, is common among health professionals (HPs). They reduce work productivity and cause high costs. This follow up study investigates the prevalence and individual course of Musculoskeletal pain among HP students at the end of their studies and one year after entering the health care workforce. Participants were asked whether their Musculoskeletal pain was related to study or work conditions.</p> <p>Method: Self reported one year prevalence for lower back pain, neck and shoulder pain, pain in arms or hands, and pain in legs or feet was collected by an online questionnaire at two timepoints from 1046 participating HPs. Generalized estimating equation (GEE) models of the binomial family with log link were employed to estimate adjusted prevalence and corresponding normal based 95% confidence intervals were derived using the bootstrap method with 1,000 replications.</p> <p>Results: Prevalence of lower back pain as well as neck and shoulder pain was very high at baseline and follow up in all students and later HPs. Prevalence for pain in arms or hands, legs or feet was lower and there were significant differences between the professions. HP associated their lower back pain and neck and shoulder pain clearly with study and work conditions; HPs linked pain in arms or hands, legs or feet strongly with work conditions only.</p> <p>Conclusion: The prevention of lower back pain and neck and shoulder pain must be included in the curricula of all health professions at universities. As best practice example, they should incorporate ergonomic measures and exercises as a daily routine of the formation of health professionals. The impact of physically demanding professional tasks on upper and lower extremities needs to be investigated in further studies in order to take preventive measures.</p>
Data and analysis for Association of meeting 24-hour movement guidelines with low back pain among adults
<p>Introduction</p> <p>This data and code forms the analytical process of a study examining associations between meeting different combinations of 24-h movement guidelines (that integrates a recommendations on physical activity, sedentary behaviour, and sleep) with prevalence, frequency and intensity of low back pain in a sample of adults aged 18 years and over. </p> <p>Notes: </p> <p>* the raw data is provided alongside this upload, but the processing is not addressed here. <br> * the authors of this document are a subset of the authors of the related paper.<br> * this document and the related data files were uploaded at the time of submission for review. An update providing the doi of the related paper will be provided when it is available.</p>
Glycomics measurements of the retrospective study of the Pain-Omics project
<p>This deposit contains the QCed glycomics data for the patients in the retrospective study of the Pain-Omics FP7 project.</p>
Genome-wide association summary statistics of chronic musculoskeletal pain at four anatomic sites and their genetically independent components
<p>The dataset contains results of a genome-wide association study of distinct chronic musculoskeletal pain conditions: back pain, knee pain, neck pain, and hip pain. Additionally, there are genome-wide association summary statistics for four genetically independent components of pain conditions, listed above. For more details, please, read the paper XXX.</p> <p>All files contain association summary statistics for genome-wide association meta-analysis of the 265,000 white British individuals from the UK Biobank and additional 191,580 individuals of European Ancestry from the UK biobank (total N = 456,580). Cases and controls were defined based on questionnaire responses. First, participants responded to “Pain type(s) experienced in the last months” followed by questions inquiring if the specific pain had been present for more than 3 months. Those who reported back, neck or shoulder, hip, or knee pain lasting more than 3 months were considered chronic back, neck/shoulder, hip, and knee pain cases, respectively. Participants reporting no such pain lasting longer than 3 months were considered controls (regardless of whether they had another regional chronic pain, such as abdominal pain, or not). Individuals who preferred not to answer were excluded from the study. Besides this, we excluded individuals who reported more than 3 months of pain all over the body.</p> <p>The data are provided on an "AS-IS" basis, without warranty of any type, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose. If investigators use these data, any and all consequences are entirely their responsibility. By downloading and using these data, you agree that you will cite the appropriate publication in any communications or publications arising directly or indirectly from these data; for utilization of data available prior to publication, you agree to respect the requested responsibilities of resource users under 2003 Fort Lauderdale principles; you agree that you will never attempt to identify any participant. This research has been conducted using the UK Biobank Resource and the use of the data is guided by the principles formulated by the UK Biobank.</p> <p><strong>When using downloaded data, please cite the corresponding paper and this repository:</strong></p> <ol> <li>Tsepilov et al 2020</li> </ol> <p><strong>Funding:</strong></p> <p>The work of YSA and SZS was supported by the Russian Ministry of Education and Science under the 5-100 Excellence Programme and by the Federal Agency of Scientific Organizations via the Institute of Cytology and Genetics (project 0324-2019-0040). The work of YAT, ASSh, and EEE was supported by the Russian Foundation for Basic Research (project 19-015-00151). The contribution of LСK was funded by PolyOmica. Dr. Suri was supported by VA Career Development Award # 1IK2RX001515 from the United States (U.S.) Department of Veterans Affairs Rehabilitation Research and Development (RR&D) Service. Dr. Suri is a Staff Physician at the VA Puget Sound Health Care System. The contents of this work do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.</p> <p><strong>List of files:</strong></p> <ol> <li>Back_output_done.csv: GWAS summary statistics for the chronic back pain</li> <li>gpc1_output_done.csv: GWAS summary statistics for the GIP1</li> <li>gpc2_output_done.csv: GWAS summary statistics for the GIP2</li> <li>gpc3_output_done.csv: GWAS summary statistics for the GIP3</li> <li>gpc4_output_done.csv: GWAS summary statistics for the GIP4</li> <li>Hip_output_done.csv: GWAS summary statistics for the chronic hip pain</li> <li>Knee_output_done.csv: GWAS summary statistics for the chronic knee pain</li> <li>Neck_output_done.csv: GWAS summary statistics for the chronic neck pain</li> </ol> <p><strong>Column headers:</strong></p> <ol> <li>gwas_id: uninformative field</li> <li>rs_id: dbSNP rsID (GRCh37 build) </li> <li>snp_num: uninformative field</li> <li>chr: chromosome (GRCh37 build) </li> <li>bp: position (GRCh37 build) </li> <li>ea: effect allele (coded as "1")</li> <li>ra: reference allele (coded as "0")</li> <li>eaf: effect allele frequency</li> <li>af_ref: uninformative field</li> <li>beta: effect size of effect allele</li> <li>se: standard error of effect size</li> <li>p: P-value of association (without GC correction)</li> <li>n:Total sample size</li> <li>z: Z-statistic of association</li> <li>info: uninformative field</li> <li>af_outlier: uninformative field</li> <li>pz_outlier: uninformative field</li> </ol>
Data from: Grand Theft Empathy? Evidence for the absence of effects of violent video games on empathy for pain and emotional reactivity to violence
<p><strong>Abstract:</strong></p><p>Influential accounts claim that violent video games (VVG) decrease players' emotional empathy by desensitizing them to both virtual and real-life violence. However, scientific evidence for this claim is inconclusive and controversially debated. To assess the causal effect of VVGs on the behavioral and neural correlates of empathy and emotional reactivity to violence, we conducted a prospective experimental study using functional magnetic resonance imaging (fMRI). We recruited eighty-nine male participants without prior VVG experience. Over the course of two weeks, participants played either a highly violent video game, or a non-violent version of the same game. Before and after this period, participants completed an fMRI experiment with paradigms measuring their empathy for pain and emotional reactivity to violent images. Applying a Bayesian analysis approach throughout enabled us to find substantial evidence for the absence of an effect of VVGs on the behavioral and neural correlates of empathy. Moreover, participants in the VVG group were not desensitized to images of real-world violence. These results imply that short and controlled exposure to VVGs does not numb empathy nor the responses to real-world violence. We discuss the implications of our findings regarding the potential and limitations of experimental research on the causal effects of VVGs. While VVGs might not have a discernible effect on the investigated subpopulation within our carefully controlled experimental setting, our results cannot preclude that effects could be found in special vulnerable subpopulations, or in settings with higher ecological validity.<br> </p><p><strong>Dataset:</strong><br>This dataset contains the fMRI data collected for the study in the BIDS-format (https://bids.neuroimaging.io/)</p><ul><li>functional neuroimaging (*_bold.nii.gz) data of 89 human participants, collected during two tasks:<ul><li>Empathy-for-Pain paradigm (Session 1 & 2)</li><li>Emotional Reactivity paradigm (Session 2)</li></ul></li><li>associated event files (*_events.tsv) containing event onsets, durations, and behavioral covariates</li><li>metadata</li></ul><p>FMRI bold timeseries are fully preprocessed, as described in the manuscript.</p><p>Additional data, such as behavioral data in a simpler format, can be accessed on https://osf.io/yx423/</p><p> </p>
Insect Pain Database: Sting Pain review
First hand reports by biologists of relative intensity of pain for bites and stings of various fauna, literature sources.
Pain Interventions in Dementia - Pain events dataset
<p>This is data collected for a quasi-experimental nurse-led intervention trial based on a convenience sample of three nursing homes. It was collected in the Swiss Canton of Zurich and Thurgau and serves to examine the effects on dementia patients, the healthcare institution, and the qualification level of the healthcare workers using an event analysis and a multilevel analysis. Healthcare workers have been individually trained on how to assess, intervene and evaluate acute and chronic pain with BESD and/or VAS. There are three data-monitoring cycles (T0, T1, T2) and two intervention cycles (I1, I2) with a total study duration of 425 days. The raw data has been cryptographically anonymized using an SSL stream and further de-identification techniques. </p> <p>Also see: 10.1186/s12904-017-0200-5 </p>
Multidimensional pain profiling in people living with obesity and attending weight management services: a protocol for a longitudinal cohort study
<p><strong>Please note: The final dataset will not be available until data collection has been completed in the Autumn of 2024.<br> This dataset currently contains the following:</strong></p> <p>1. Details of the study, including authorship, ethical approval, funding, registration details, an abstract for the protocol of the study and details of data being collected (both in Microsoft Word and open access .txt formats)</p> <p>2. Outline of the data in the process of collection (Microsoft Excel)</p> <p>3. Ethical approval letters from relevant Research Ethics Committees (PDF)<br> <br> </p> <p> </p> <p><strong>Project Title: </strong>Multidimensional pain profiling in people living with obesity and attending weight management services: a longitudinal cohort study</p> <p> </p> <p><strong>Authors:</strong> Keith M. Smart<sup>1,2</sup>, Natasha Hinwood<sup>1</sup>, Colin G. Dunlevy<sup>3</sup>, Catherine Doody<sup>1</sup>, Catherine Blake<sup>1</sup>, Brona Fullen<sup>1</sup>, Jean O’Connell<sup>3</sup>, Carel W. Le Roux<sup>4</sup>, Clare Gilsenan<sup>5</sup>, Francis M. Finucane<sup>6,7</sup>, Gráinne O’Donoghue<sup>1</sup>.</p> <p> </p> <p><strong>Corresponding author</strong>: Natasha Hinwood</p> <p><strong>Address:</strong> UCD School of Public Health, Physiotherapy and Sport Science, University College Dublin, Dublin, Ireland</p> <p><strong>Email:</strong> <a href="mailto:natasha.hinwood@ucdconnect.ie">natasha.hinwood@ucdconnect.ie</a></p> <p><strong>Phone:</strong> +353 1 716 6511</p> <p> </p> <p>Full name, department, institution, city, and country of all co-authors.</p> <p><sup>1</sup>UCD School of Public Health, Physiotherapy and Sport Science, University College Dublin, Dublin, Ireland</p> <p><sup>2</sup>Physiotherapy Department, St. Vincent’s University Hospital, Dublin, Ireland</p> <p><sup>3</sup>Weight Management Service, St Columcille’s Hospital, Dublin, Ireland</p> <p><sup>4</sup>Diabetes Complications Research Centre, University College Dublin, Dublin, Ireland</p> <p><sup>5</sup>Physiotherapy Department, Beaumont Hospital, Dublin, Ireland</p> <p><sup>6</sup> School of Medicine, College of Nursing and Health Sciences, University of Galway</p> <p><sup>7</sup>Bariatric Medicine Service, Centre for Diabetes, Endocrinology and Metabolism, Galway University Hospitals</p> <p> </p> <p><strong>ORCID</strong></p> <p>1. Keith M. Smart: 0000-0002-1598-5215</p> <p>2. Natasha Hinwood: 0000-0001-9382-716X</p> <p>3. Colin G. Dunlevy:</p> <p>4. Catherine Doody:</p> <p>5. Catherine Blake: 0000-0002-0600-629X</p> <p>6. Brona Fullen: 0000-0003-4408--2063</p> <p>7. Carel W. Le Roux: 0000-0001-5521-5445</p> <p>8. Jean O’Connell: 0000-0001-7241-8025</p> <p>9. Clare Gilsenan:</p> <p>10. Francis M. Finucane: 0000-0002-5374-7090</p> <p>11. Gráinne O’Donoghue: 0000-0002-9126-2094</p> <p> </p> <p> </p> <p><strong>Project abstract (Protocol): </strong></p> <p><em>Introduction</em>:</p> <p>Pain is prevalent in people living with overweight and obesity. Obesity is associated with increased self-reported pain intensity and pain-related disability, reductions in physical functioning and poorer psychological well-being. People living with obesity tend to respond less well to pain treatments or management compared to people living without obesity. Mechanisms linking obesity and pain are complex and may variously include contributions from and interactions between physiological, behavioural, psychological, socio-cultural, biomechanical, and genetic factors. Our aim is to study the multidimensional pain profiles of people living with obesity, over time, in an attempt to better understand the relationship between obesity and pain.<br> </p> <p><em>Methods and analysis: </em></p> <p>This longitudinal observational cohort study will recruit (n=216) people living with obesity and who are newly attending three weight management services in Ireland. Participants will complete questionnaires that assess their multidimensional biopsychosocial pain experience at baseline and at 3, 6, 12 and 18-months post-recruitment. Quantitative analyses will characterise the multidimensional pain experiences and trajectories of the cohort as a whole and in defined sub-groups.<br> </p> <p><em>Ethics and dissemination: </em></p> <p>The study protocol has been approved by the Ethics and Medical Research Committee of St Vincent’s Healthcare Group, Dublin, Ireland (Reference No.: RS21-059) and the University College Dublin Human Research Ethics Committee (Reference No.: LS-E-22-41-Hinwood-Smart). Findings will be disseminated through peer-reviewed journals, conference presentations, public and patient advocacy groups, and social media.</p>
Radiographic outcomes for Biodentine group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months
<p>Radiographic outcomes for Biodentine group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months</p>
Clinical outcome for TheraCal LC group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months
<p>Clinical outcome for TheraCal LC group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months</p>
Clinical outcome for MTA group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months
<p>Clinical outcome for MTA group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months</p>
Radiographic outcomes for MTA group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months
<p>Radiographic outcomes for MTA group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months</p>
Clinical outcome for Biodentine group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months
<p>Clinical outcome for Biodentine group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months</p>
Common factor GWAS and TWAS output for nociplastic type pain
<p>file: GSEM_commonFactorGWAS_COPC_6trait_30MAY2023.csv.gz</p> <p>description: Common factor GWAS output for GenomicSEM analyses of 6 COPC traits (see doi: https://doi.org/10.1101/2023.06.27.23291959)</p> <p>columns:</p> <p>SNP = rsID SNP identifier </p> <p>CHR = chromosome</p> <p>BP = base pair position</p> <p>MAF = minor allele frequency</p> <p>A1 = effect allele</p> <p>A2 = other allele</p> <p>i = index (1 - n SNPs)</p> <p>lhs = left hand side of equation </p> <p>op = equation operator (lavaan syntax)</p> <p>rhs = right hand side of equation</p> <p>est = effect size (beta)</p> <p>se_c = standard error of effect estimate</p> <p>Z_Estimate = Z value</p> <p>Pval_Estimate = p value of effect</p> <p>Q = Q (heterogeneity) value</p> <p>Q_df = degrees of freedom for Q</p> <p>Q_pval = Q p value</p> <p>fail = GSEM fail message if applicable</p> <p>warning = GSEM warning message if applicable </p> <p>Z_smooth = smoothing parameter if applicable </p> <p>N_estimate = N estimate </p> <p> </p> <p>file: GSEM_commonFactorTWAS_COPC_6trait_30MAY2023.csv.gz</p> <p>description: Common factor TWAS output for GenomicSEM analyses of 6 COPC traits (see doi: https://doi.org/10.1101/2023.06.27.23291959)</p> <p>columns:</p> <p>Gene = ensembl gene ID </p> <p>Panel = which model (tissue+gene) i.e. reference weights file</p> <p>HSQ = gene heritability </p> <p>i = index (1 - n gene-tissue models)</p> <p>lhs = equation left hand side</p> <p>op = operator (lavaan syntax)</p> <p>rhs = equation right hand side</p> <p>est = association estimate (beta)</p> <p>se_c = standard error of beta</p> <p>Z_Estimate = Z value </p> <p>Pval_Estimate = p value of association test</p> <p>Q = Q (heterogeneity) value</p> <p>Q_df = degrees of freedom for Q</p> <p>Q_pval = p value for Q</p> <p>fail = GSEM fail message if applicable </p> <p>warning = GSEM warning message if applicable </p> <p>tissue = tissue</p> <p>p_bonf_tissue = adjusted p value - bonferroni adjustment within tissue </p> <p>p_fdr_tissue = adjusted p value - false discovery rate adjustment within tissue</p> <p>threshold_bonf_tissue = p value threshold for bonferroni adjustment within tissue </p> <p>p_bonf_experiment = adjusted p value - bonferroni adjustment experiment-wide</p> <p>p_threshold_bonf_experiment = p value threshold (bonferroni, experiment-wide)</p> <p>Q_bonf_tissue = adjusted p value for Q, bonferroni within-tissue </p> <p> </p>
Pain Catastrophizing Predicts Alcohol Hangover Severity and Symptoms - Raw Data
<p>Raw data from investigation published as 'Pain Catastrophizing Predicts Alcohol Hangover Severity and Symptoms.'</p>
Genome-wide association summary statistics for sex- and age-specific analysis of chronic back pain
<p>The dataset comprises summary-level statistics for age- and sex-specific genome-wide association study of chronic back pain (cBP) in individuals of European descent from UK Biobank (<a href="https://www.ukbiobank.ac.uk/">https://www.ukbiobank.ac.uk/</a>). The study was carried out under UK Biobank approved project #18219. </p> <p><strong>The dataset accompanies the paper (please cite if using the dataset):</strong></p> <p><a href="https://pubmed.ncbi.nlm.nih.gov/33021770/">Freidin, Maxim B.; Tsepilov, Yakov A.; Stanaway, Ian B.; Meng, Weihua; Hayward, Caroline; Smith, Blair H.; Khoury, Samar; Parisien, Marc; Bortsov, Andrey; Diatchenko, Luda; Børte, Sigrid; Winsvold, Bendik S.; Brumpton, Ben M.; Zwart, John-Anker; HUNT All-In Pain; Aulchenko, Yurii S.; Suri, Pradeep; Williams, Frances M.K. Sex- and age-specific genetic analysis of chronic back pain. Pain. 2020. doi:10.1097/j.pain.0000000000002100.</a></p> <p>The phenotype of cBP was defined as back pain for 3+ months. Linear mixed-effects additive model was fitted adjusting for age, genotyping array type, and 10 genetic PCs provided by UK Biobank. The following filters were applied: minor allele frequency >0.001, genotyping and individual call rates >0.98%, imputation quality score (INFO) >0.7. GWAS were carried out in males and females separately in the whole sample (<strong>allages</strong>) as well as in groups of younger than 65 years (<strong>under65</strong>) and 65+ years old (<strong>65plus</strong>) as detailed in the paper. Accordingly, 6 files are deposited here, corresponding to each group. </p> <p><strong>Column headers:</strong></p> <p>SNP, SNP rsID </p> <p>CHR, chromosome</p> <p>BP, genomic position (GRCh37 build)</p> <p>EA, effect allele (coded as "1")</p> <p>OTHER, other allele (coded as "0")</p> <p>A1FREQ, frequency of effect allele</p> <p>INFO, imputation quality</p> <p>BETA, effect size (for effect allele)</p> <p>SE, standard error of effect size</p> <p>PVAL, p-value for association</p>
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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.