Find research datasets worth reusing

opencc-zeroDec 2015View details →

Data from: Watch out for your neighbor: climbing onto shrubs is related to risk of cannibalism in the scorpion Buthus cf. occitanus

The distribution and behavior of foraging animals usually imply a balance between resource availability and predation risk. In some predators such as scorpions, cannibalism constitutes an important mortality factor determining their ecology and behavior. Climbing on vegetation by scorpions has been related both to prey availability and to predation (cannibalism) risk. We tested different hypotheses proposed to explain climbing on vegetation by scorpions. We analyzed shrub climbing in Buthus cf. occitanus with regard to the following: a) better suitability of prey size for scorpions foraging on shrubs than on the ground, b) selection of shrub species with higher prey load, c) seasonal variations in prey availability on shrubs, and d) whether or not cannibalism risk on the ground increases the frequency of shrub climbing. Prey availability on shrubs was compared by estimating prey abundance in sticky traps placed in shrubs. A prey sample from shrubs was measured to compare prey size. Scorpions were sampled in six plots (50 m x 10 m) to estimate the proportion of individuals climbing on shrubs. Size difference and distance between individuals and their closest scorpion neighbor were measured to assess cannibalism risk. The results showed that mean prey size was two-fold larger on the ground. Selection of particular shrub species was not related to prey availability. Seasonal variations in the number of scorpions on shrubs were related to the number of active scorpions, but not with fluctuations in prey availability. Size differences between a scorpion and its nearest neighbor were positively related with a higher probability for a scorpion to climb onto a shrub when at a disadvantage, but distance was not significantly related. These results do not support hypotheses explaining shrub climbing based on resource availability. By contrast, our results provide evidence that shrub climbing is related to cannibalism risk.

opencc-zeroDec 2010View details →

Data from: Relative risks of inbreeding and outbreeding depression in the wild in endangered salmon

Conservation biologists routinely face the dilemma of keeping small, fragmented populations isolated, wherein inbreeding depression may ensue, or mixing such populations, which may exacerbate population declines via outbreeding depression. The joint evaluation of inbreeding and outbreeding risks in the wild cannot be readily conducted in endangered species, so a suggested 'safe' strategy is to mix ecologically and genetically similar populations. To evaluate this strategy, we carried out a reciprocal transplant experiment involving three neighbouring populations of endangered Atlantic salmon (Salmo salar) now bred in captivity and maintained in captive and wild environments. Pure, inbred, and outbred (first and second generation) cross types were released and recaptured in the wild to simultaneously test for local adaptation, inbreeding depression and outbreeding depression. We found little evidence of inbreeding depression after one generation of inbreeding and little evidence of either heterosis or outbreeding depression via genetic incompatibilities after one or two generations of outbreeding. A trend for outbreeding depression via the loss of local adaptation was documented in one of three populations. The effects of inbreeding were not significantly different from the effects of outbreeding. Hence, at the geographic scale evaluated (34-50 km), inbreeding for one generation and outbreeding over two generations may have similar effects on the persistence of small populations. The results further suggested that outbreeding outcomes may be highly variable or unpredictable at small genetic distances. Our work highlights the necessity of evaluating the relative costs of inbreeding and outbreeding in the conservation and management of endangered species on a case-by-case basis.

opencc-zeroDec 2017View details →

Data from: Use of environmental sites by mule deer: a proxy for relative risk of chronic wasting disease exposure and transmission

Prions that cause chronic wasting disease (CWD) in cervids can remain infective for years outside the host. Infectious cervids shed prions for a long time, consequently depositing prions in frequently used areas. These environmental prions are important in CWD epidemiology. Unfortunately, effective tools for quantifying CWD prions in soil, water, and other environmental sources are not currently available. Our goal was to investigate relative differences in visits by mule deer (Odocoileus hemionus hemionus) to various environmental site types as an indicator of the relative risk of prion contamination and disease transmission. For this, we deployed a system of triggered-by-movement cameras at eight site types in a CWD-endemic area in Saskatchewan, Canada. We first assessed whether the relative differences among site types in the frequency of visits by mule deer of any sex-and-age class, males, and females varied by season and site type. We then assessed whether the rate of behaviors with a high risk of environmental prion transmission (either contamination or acquisition) differed by season and site type. Finally, we assessed whether the intensity of visitation, based on the number of animals per picture, differed by season and site type. We found that grain sources and beds were key attractants for mule deer: (1) The greatest number of pictures with mule deer per camera-day occurred at grain sources across all seasons, except in fawning, when beds were the most visited sites; (2) during pre-rut and early gestation, mule deer visited grain sources at least twice as often as most other sites; (3) females were more likely to visit beds and grain sources, but there was no significant site preferences for males after accounting for season; (4) mule deer were most likely to be pictured contacting the environment at grain sources in early gestation; and (5) beds and grain sources were the most intensively visited sites. We also found that environmental contacts at waterholes were more frequent during spring. We discuss the potential importance of various sites in the transmission of CWD and how their modification could potentially reduce the risk of prion environmental exposure among mule deer.

opencc-zeroDec 2017View details →

Data from: Using risk of bias domains to identify opportunities for improvement in food- and nutrition-related research: an evaluation of research type and design, year of publication, and source of funding

Purpose: This retrospective cross-sectional study aimed to identify opportunities for improvement in food and nutrition research by examining risk of bias (ROB) domains. Methods: Rating were extracted from critical appraisal records for 5675 studies used in systematic reviews conducted by three organizations. Variables were as follows: ROB domains defined by the Cochrane Collaboration (Selection, Performance, Detection, Attrition, and Reporting), publication year, research type (intervention or observation) and specific design, funder, and overall quality rating (positive, neutral, or negative). Appraisal instrument questions were mapped to ROB domains. The kappa statistic was used to determine consistency when multiple ROB ratings were available. Binary logistic regression and multinomial logistic regression were used to predict overall quality and ROB domains. Findings: Studies represented a wide variety of research topics (clinical nutrition, food safety, dietary patterns, and dietary supplements) among 15 different research designs with a balance of intervention (49%) and observation (51%) types, published between 1930 and 2015 (64% between 2000-2009). Duplicate ratings (10%) were consistent (k=0.86-0.94). Selection and Performance domain criteria were least likely to be met (57.9% to 60.1%). Selection, Detection, and Performance ROB ratings predicted neutral or negative quality compared to positive quality (p<0.001). Funder, year, and research design were significant predictors of ROB. Some sources of funding predicted increased ROB (p<0.001) for Selection (Interventional: industry only and none/not reported; Observational: other only and none/not reported) and Reporting (Observational: university only and other only). Reduced ROB was predicted by combined and other-only funding for intervention research (p<0.005). Performance ROB domain ratings started significantly improving in 2000; others improved after 1990 (p<0.001). Research designs with higher ROB were nonrandomized intervention and time series designs compared to RCT and prospective cohort designs respectively (p<0.001). Conclusions: Opportunities for improvement in food and nutrition research are in the Selection, Performance, and Detection ROB domains.

opencc-by-4.0Dec 2019View details →

Annexes to the Risk to human health related to the presence of perfluoroalkyl substances in food

Annexes  to the Risk to human health related to the presence of perfluoroalkyl substances in food - available at: https://www.efsa.europa.eu/en/efsajournal/pub/6223 Annex A - Occurrence and exposure data Annex B - Distribution of analytical results Annex C - Comparison of PFOA and PFOS occurrence and exposure data with previous assessment (EFSA CONTAM Panel, 2018). Also the raw (no data cleaning applied to it)  occurrence dataset as extracted from EFSA DWH is provided in csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. delete record or update values in specific fields).

opencc-by-4.0Feb 2024View details →

Dataset for "Activation of the mPFC-NAc pathway reduces motor impulsivity but does not affect risk-related decision-making in innately high-impulsive male rats"

Attention-deficit/hyperactivity disorder (ADHD) and substance use disorders (SUD) are characterized by exacerbated motor and risk-related impulsivities, which are associated with decreased cortical activity. In rodents, the medial prefrontal cortex (mPFC) and nucleus accumbens (NAc) have been separately implicated in impulsive behaviors, but studies on the specific role of the mPFC-NAc pathway in these behaviors are limited. Here, we investigated whether heightened impulsive behaviors are associated with reduced mPFC activity in rodents, and determined the involvement of the mPFC-NAc pathway in motor and risk-related impulsivities. We used the Roman High- (RHA) and Low-Avoidance (RLA) rat lines, which display divergent phenotypes in impulsivity. To investigate alterations in cortical activity in relation to impulsivity, regional brain glucose metabolism was measured using positron emission tomography and [18F]-fluorodeoxyglucose ([18F]FDG). Using chemogenetics, the activity of the mPFC-NAc pathway was either selectively activated in high-impulsive RHA rats or inhibited in low-impulsive RLA rats, and the effects of these manipulations on motor and risk-related impulsivity were concurrently assessed using the rat gambling task. We showed that basal [18F]FDG uptake was lower in the mPFC and NAc of RHA compared to RLA rats. Activation of the mPFC-NAc pathway in RHA rats reduced motor impulsivity, without affecting risk-related decision-making. Conversely, inhibition of the mPFC-NAc pathway had no effect in RLA rats. Our results suggest that the mPFC-NAc pathway controls motor impulsivity, but has limited involvement in risk-related decision-making. Our findings suggest that reducing fronto-striatal activity may help attenuate motor impulsivity in patients with impulse control dysregulation like ADHD or SUD.

opencc-by-4.0Apr 2022View details →

"Early COVID-related Acute Kidney Injury Recovery May Course with Hydroelectrolytic Disorders in Patients With High Risk of Insensible Fluid Loss" dataset

The dataset used and analyzed during the study "Early COVID-related Acute Kidney Injury Recovery May Course with Hydroelectrolytic Disorders in Patients With High Risk of Insensible Fluid Loss"

opencc-zeroJun 2022View details →

Habitat availability alters the relative risk of a bovine tuberculosis breakdown in the aftermath of a commercial forest clearfell disturbance

1. Human modification of landscapes and associated disturbances may facilitate the emergence and spread of zoonotic diseases. Policy-makers need better understanding of the link between anthropogenic disturbances and wildlife disease hosts at the interface of human society and the natural environment, e.g. agriculture, forestry and aquaculture. Empirical research is strongly needed for the control of novel zoonoses which might emerge, as well as the management of existing zoonoses with significant economic repercussions such as bovine tuberculosis (bTB). 2. We aimed to examine the link between ecological disturbance and relative bTB risk using Ireland as a case study. We analysed clearfell forestry operations and assessed bTB breakdowns within cattle farms across different spatio-temporal scales over multiple years, examining how ecological conditions may modulate this relationship using conditional logistic regression models. 3. We found a significant effect of the interaction between the extent of clearfell forestry removed and the extent of natural grassland and mixed forestry present on relative bTB risk. This interaction was dynamic, leading to an increase or decrease of the relative bTB risk depending on where (between 2 and 6 km from the farm) and when (between 0 and 36 months prior to the bTB outbreak) the clearfell operations occurred. 4. Our study provides empirical evidence of the link between mechanised forestry operations and fluctuating relative bTB risk in cattle farms, although the mechanism behind it is yet to be elucidated. Given our data, we hypothesise that wildlife hosts may abandon the area subjected to clearfell when disturbance is highest (during active operations and shortly afterward) but are subsequently attracted back to the site as they regenerate, potentially affecting the contact rates with livestock and thus, relative bTB risk. 5. Our analysis demonstrates that landscape modification is correlated with a change in relative bTB risk that is dynamic in time and space, allowing managers to understand the risk in landscape modification and inform policy accordingly. Landscape-level studies are necessary to unveil subtle ecological processes, shifting research and management efforts away from cattle herd-centric and toward macroecological surveillance of wildlife hosts and longitudinal assessment of bTB risk.

opencc-by-4.0Sep 2022View details →

Data for risk factors related to child abuse, Japan

Supplemental file for "Risk factors of child abuse during the COVID-19 pandemic in Japan, 2021: A cross-sectional study".

opencc-zeroOct 2018View details →

Supplementary material 2 from: Dickey JWE, Cuthbert RN, Rea M, Laverty C, Crane K, South J, Briski E, Chang X, Coughlan NE, MacIsaac HJ, Ricciardi A, Riddell GE, Xu M, Dick JTA (2018) Assessing the relative potential ecological impacts and invasion risks of emerging and future invasive alien species. NeoBiota 40: 1-24. https://doi.org/10.3897/neobiota.40.28519

Table S2. Summary of the GB online survey outlining which of the four species of turtle was being sold :

opencc-by-nc-nd-4.0Oct 2024View details →

MPIC Environment-related Asthma Risk (MEAR) Dataset

The MPIC Environment-related Asthma Risk (MEAR) Dataset provides global evidence on associations between environmental exposure and risk of childhood and adult asthma. In the current stage, evidence on associations between long-term postnatal exposure to ambient fine particulate matters (PM2.5) is provided. The summarized evidence is from literatures published until May 2023, including the risk for asthma prevalence, incidence, and mortality. We will be updating the evidence and corresponding data continuously. In the near future, evidence on associations between prenatal air pollution exposure and risk of childhood asthma will be publically avaiable. If the MEAR dataset is used, please cite the following publication and our zenodo record: 10.5281/zenodo.13376288, thank you. Ni, R., Su, H., Burnett, R. T., Guo, Y., and Cheng, Y. Long-term exposure to PM2.5 has significant adverse effects on childhood and adult asthma: A global meta-analysis and health impact assessment. One Earth, 2024. https://doi.org/10.1016/j.oneear.2024.09.022

opencc-zeroDec 2015View details →

Data from: Correlates of extinction risk in squamate reptiles: the relative importance of biology, geography, threat and range size

Aim Evaluating the relative roles of biological traits and environmental factors that predispose species to an elevated risk of extinction is of fundamental importance to macroecology. Identifying species that possess extinction-promoting traits allows targeted conservation action before precipitous declines occur. Such analyses have been carried out for several vertebrate groups, with the notable exception of reptiles. We identify traits correlating with high extinction risk in squamate reptiles, assess whether these differ with geography, taxonomy and threats, and make recommendations for future Red List assessments. Location Global. Methods We collected data on biological traits and environmental factors for a representative sample of 1139 species of squamate reptiles. We used phylogenetically controlled regression models to identify general correlates of extinction risk, threat-specific correlates of risk and realm-specific correlates of risk. We also assessed the relative importance of range size versus other factors through multiplicative bivariate models, partial regressions and variance partitioning. Results Range size was the most important predictor of extinction risk, reflecting the high frequency of reptiles assessed under range-based IUCN criteria. Habitat specialists occupying accessible ranges were at a greater risk of extinction: although these factors never contributed more than 10% to the variance in extinction risk, they showed significant interactions with range size. The predictive power of our global models ranged from 23% to 29%. The general overall pattern remained the same among geographical, taxonomic and threat-specific data subsets. Main conclusions Proactive conservation requires shortcuts to identify species at high risk of extinction. Regardless of location, squamate reptiles that are range-restricted habitat specialists living in areas highly accessible to humans are likely to become extinct first. Prioritizing species that exhibit such traits could forestall extinction. Integration of data sources on human pressures, such as accessibility of species ranges, may aid robust and time-efficient assessments of species extinction risk.

opencc-by-4.0Sep 2021View details →

Data Set: Software-Related Fatal Failures: An Empirical Exploration of RISKS Reports

The data set used in the empirical exploration of RISKS reports. This data set acts as a basis for further research and investigation to categorize and analyze software-related fatal failures spanning more than 30 years.

opencc-by-4.0Dec 2022View details →

Changes in patterns of age-related network connectivity are associated with risk for schizophrenia

This is the online data repository accompanying the following manuscript: Changes in patterns of age-related network connectivity are associated with risk for schizophrenia Roberta Passiatorea,b,c, Linda A. Antonuccia, Thomas P. DeRamusb, Leonardo Faziod, Giuseppe Stolfaa, Leonardo Sportellia,e, Gianluca C. Kikidisa,e, Giuseppe Blasia,f, Qiang Chene, Juergen Dukartc,g, Aaron L. Goldmane, Venkata S. Mattaye,h, Teresa Popolizioi, Antonio Rampinoa,f, Fabio Sambataroj, Pierluigi Selvaggia,f, William Ulriche, Apulian Network on Risk for Psychosisa,k,l,m,n,o, Daniel R. Weinbergere,h,p,q,r, Alessandro Bertolinoa,f1, Vince D. Calhounb1, Giulio Pergolaa,e,p1 a Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124 Bari, Italy b Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, 30303 Atlanta, GA c Institute of Neuroscience and Medicine, Brain and Behavior, Research Centre Jülich, 52428 Jülich, Germany d Department of Medicine and Surgery, Libera Università Mediterranea Giuseppe Degennaro, 70010 Casamassima, Italy e Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, MD f Psychiatric Unit, University Hospital, 70124 Bari, Italy g Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany h Department of Neurology and Radiology, Johns Hopkins Medical Campus, 21287 Baltimore, MD i Neuroradiology Unit, Scientific Institute for Research, Hospitalization and Health Care, Casa Sollievo della Sofferenza, 71013 San Giovanni Rotondo, Foggia, Italy j Section of Psychiatry, Department of Neuroscience, University of Padova, 35121 Padova, Italy k Department of Mental Health, Azienda Sanitaria Locale Foggia, 71121 Foggia, Italy l Department of Clinical and Experimental Medicine, University of Foggia, 71122 Foggia, Italy m Department of Mental Health, Azienda Sanitaria Locale Barletta-Andria-Trani, 76123 Andria, Italy n Department of Mental Health, Azienda Sanitaria Locale Bari, 70132 Bari, Italy o Department of Mental Health, Azienda Sanitaria Locale Brindisi, 72100 Brindisi, Italy p Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, 21205 Baltimore, MD q Department of Neuroscience, Johns Hopkins University School of Medicine, 21287 Baltimore, MD r Department of Genetic Medicine, Johns Hopkins University School of Medicine, 21287 Baltimore, MD   Members of the Apulian Network on Risk for Psychosis include Ileana Andriola, Lucia Mare, Nicolò Parente, Alessandra Raio, Veronica D. Toro, (Department of Translational Biomedicine and Neuroscience – University of Bari Aldo Moro, Bari, IT), Mario Altamura, Marimar Castrigno, Melania Difino (Department of Mental Health, ASL Foggia, Foggia, IT; Department of Clinical and Experimental Medicine, University of Foggia, Foggia, IT), Flora Brudaglio, Domenico S. Savino, Rossana Vista (Department of Mental Health, ASL Barletta-Andria-Trani, Andria, IT), Angela Carofiglio, Barbara Gelao, Marina Mancini (Department of Mental Health, ASL Bari, Bari, IT), Alessandro Saponaro, Anna Manzari, Domenico Suma (Department of Mental Health, ASL Brindisi, Brindisi, IT). Abstract Alterations in fMRI-based brain functional network connectivity (FNC) are associated with schizophrenia (SCZ) and the genetic risk or subthreshold clinical symptoms preceding the onset of SCZ, which often occurs in early adulthood. Thus, age-sensitive FNC changes may be relevant to SCZ risk-related FNC. We used independent component analysis to estimate FNC from childhood to adulthood in 9,236 individuals. To capture individual brain features more accurately than single- session fMRI, we studied an average of three fMRI scans per individual. To identify potential familial risk-related FNC changes, we compared age-related FNC in first-degree relatives of SCZ patients mostly including unaffected siblings (SIB) with neurotypical controls (NC) at the same age-stage. Then, we examined how polygenic risk scores for SCZ influenced risk-related FNC patterns. Finally, we investigated the same risk-related FNC patterns in adult SCZ patients (oSCZ) and young individuals with subclinical psychotic symptoms (PSY). Age-sensitive risk-related FNC patterns emerge during adolescence and early adulthood, but not before. Young SIB always followed older NC patterns, with decreased FNC in a cerebellar-occipitoparietal circuit and increased FNC in two prefrontal-sensorimotor circuits when compared to young NC. Two of these FNC alterations were also found in oSCZ, with one exhibiting reversed pattern. All were linked to polygenic risk for SCZ in unrelated individuals (R2 varied from 0.02 to 0.05). Young PSY showed FNC alterations in the same direction as SIB when compared to NC. These results suggest that age-related neurotypical FNC correlate with genetic risk for SCZ and are detectable with MRI in young participants. DOI: 10.1073/pnas.2221533120   Files: - NeuroMark_1.0_template  <ol> <li>NeuroMark_1.0.nii:  4D Nifti containing the 100 Independent Components (ICs) spatial maps that was used for individual IC extraction based on Du Y, Fu Z, Sui J, Gao S, Xing Y, Lin D, Salman M, Abrol A, Rahaman MA, Chen J, Hong LE, Kochunov P, Osuch EA, Calhoun VD; Alzheimer's Disease Neuroimaging Initiative. NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders. Neuroimage Clin. 2020;28:102375. doi: 10.1016/j.nicl.2020.102375. Epub 2020 Aug 11. PMID: 32961402; PMCID: PMC7509081.</li> <li>NeuroMark_1.0_Label.doc: document containing labels for each IC extracted including brain regions, peak coordinates of ICs, corresponding functional network in which ICs are arranged, and the corresponding image number in the NeuroMark_1.0.nii file. NOTE</li> </ol> - AveragedMatrix <ol> <li>vectorized_FNC_LIBD.RData, vectorized_FNC_UNIBA1.RData, vectorized_FNC_UNIBA1.RData:</li> <li>FNC_all_averaged (UNIBA1, UNIBA2, LIBD): mean FNC matrix on all subjects averaged across groups, sessions and cohorts</li> <li>FNC_all_rest (UNIBA1, UNIBA2, LIBD): mean FNC matrix on all subjects during resting state fMRI averaged across groups and cohorts</li> <li>FNC_all_enc (UNIBA1, UNIBA2, LIBD): mean FNC matrix on all subjects during encoding (RISE/PEAR) fMRI averaged across groups and cohorts</li> <li>FNC_all_ret (UNIBA1, UNIBA2, LIBD): mean FNC matrix on all subjects during retrieval (RISE/PEAR) fMRI averaged across groups and cohorts</li> <li>FNC_all_nback (UNIBA1, UNIBA2, LIBD): mean FNC matrix on all subjects during working memory (2-Back) fMRI averaged across groups and cohorts</li> <li>FNC_all_emo (UNIBA1, UNIBA2, LIBD): mean FNC matrix across subjects during emotion recognition (Faces/FMT) fMRI averaged across groups and cohorts</li> <li>FNC_yNC_averaged (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC averaged across sessions</li> <li>FNC_yNC_rest (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC during resting state fMRI</li> <li>FNC_yNC_enc (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC during encoding (RISE/PEAR) fMRI</li> <li>FNC_yNC_ret (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC during retrieval (RISE/PEAR) fMRI</li> <li>FNC_yNC_nback (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC during working memory (2-Back) fMRI</li> <li>FNC_yNC_emo (UNIBA1, UNIBA2, LIBD): mean FNC matrix on yNC during emotion recognition (Faces/FMT) fMRI</li> <li>FN_oNC_averaged (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC averaged across sessions</li> <li>FNC_oNC_rest (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC during resting state fMRI</li> <li>FNC_oNC_enc (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC during encoding (RISE/PEAR) fMRI</li> <li>FNC_oNC_ret (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC during retrieval (RISE/PEAR) fMRI</li> <li>FNC_oNC_nback (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC during working memory (2-Back) fMRI</li> <li>FNC_oNC_emo (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oNC during emotion recognition (Faces/FMT) fMRI</li> <li>FNC_ySIB_averaged (UNIBA1, LIBD): mean FNC matrix on ySIB averaged across sessions</li> <li>FNC_ySIB_rest (LIBD): mean FNC matrix on ySIB during resting state fMRI</li> <li>FNC_ySIB_enc (UNIBA1, LIBD): mean FNC matrix on ySIB during encoding (PEAR) fMRI</li> <li>FNC_ySIB_ret (LIBD): mean FNC matrix on ySIB during retrieval (PEAR) fMRI</li> <li>FNC_ySIB_nback (UNIBA1, LIBD): mean FNC matrix on ySIB during working memory (2-Back) fMRI</li> <li>FNC_ySIB_emo (UNIBA1, LIBD): mean FNC matrix on ySIB during emotion recognition (Faces/FMT) fMRI</li> <li>FNC_oSIB_averaged (UNIBA1, LIBD): mean FNC matrix on oSIB averaged across sessions</li> <li>FNC_oSIB_rest (LIBD): mean FNC matrix on oSIB during resting state fMRI</li> <li>FNC_oSIB_enc (UNIBA1, LIBD): mean FNC matrix on oSIB during encoding (PEAR) fMRI</li> <li>FNC_oSIB_ret (LIBD): mean FNC matrix on oSIB during retrieval (PEAR) fMRI</li> <li>FNC_oSIB_nback (UNIBA1, LIBD): mean FNC matrix on oSIB during working memory (2-Back) fMRI</li> <li>FNC_oSIB_emo (LIBD): mean FNC matrix on oSIB during emotion recognition (FMT) fMRI</li> <li>FNC_yPSY_averaged (UNIBA2): mean FNC matrix on yPSY averaged across sessions</li> <li>FNC_yPSY_rest (UNIBA2): mean FNC matrix on yPSY during resting state fMRI</li> <li>FNC_yPSY_enc (UNIBA2): mean FNC matrix on yPSY during encoding (RISE) fMRI</li> <li>FNC_yPSY_ret (UNIBA2): mean FNC matrix on yPSY during retrieval (RISE) fMRI</li> <li>FNC_yPSY_nback (UNIBA2): mean FNC matrix on yPSY during working memory (2-Back) fMRI</li> <li>FNC_yPSY_emo (UNIBA2): mean FNC matrix on yPSY during emotion recognition (Faces) fMRI</li> <li>FNC_oSCZ_averaged (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oSCZ averaged across sessions</li> <li>FNC_oSCZ_rest (UNIBA2, LIBD): mean FNC matrix on oSCZ during resting state fMRI</li> <li>FNC_oSCZ_enc (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oSCZ during encoding (PEAR/RISE) fMRI</li> <li>FNC_oSCZ_ret (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oSCZ during retrieval (PEAR/RISE) fMRI</li> <li>FNC_oSCZ_nback (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oSCZ during working memory (2-Back) fMRI</li> <li>FNC_oSCZ_emo (UNIBA1, UNIBA2, LIBD): mean FNC matrix on oSCZ during emotion recognition (Faces/FMT) fMRI</li> <li>PlotMatrixFNC.m (Script): code used to generate FNC plots. Example of FNC plots are included in Figure 1.</li> </ol> - Connectogram <ol> <li>MeanMatrix.mat: matrix representing the difference (delta) in functional network connectivity (FNC) along the 54 ICs between oNC and yNC. This matrix was used to generate Figure 2. Matlab object.</li> <li>MeanMatrix.txt: matrix representing the difference (delta) in functional network connectivity (FNC) along the 54 ICs between oNC and yNC. This matrix was used to generate Figure 2. Text file.</li> <li>ConnectogramNeuroMark_1.0.m (Script): code used to generate Figure 2. Graphical and statistical options are provided. Please refer to <a href="https://trendscenter.wpengine.com/trends/software/gift/docs/v4.0b_gica_manual.pdf">https://trendscenter.wpengine.com/trends/software/gift/docs/v4.0b_gica_manual.pdf</a> for further details.</li> </ol> - ResFNCAgeDifferences <ol> <li>data_example.RData (Data): example data frame for AgeDiff code.</li> <li>AgeDiff_real.R (Script): code to test age differences through Linear mixed effect model on multiple sessions across age-groups.</li> <li>AgeDiff_permutation.R (Script): code to permute age differences through Linear mixed effect model on multiple sessions across age-groups.</li> <li>ResFNCAgeDifference.RData: summary statistics obtained on UNIBA1, UNIBA2, LIBD cohorts included in the manuscript.</li> <li>boxplot_permutation_p_LIBD.pdf, boxplot_permutation_p_UNIBA1.pdf, boxplot_permutation_p_UNIBA2.pdf: error bars showing results obtained after permutation (10,000 iteration) across IC pairs.</li> </ol> - dataFNC <ol> <li>data_final.RData: de-identified individuals' aggregate FNC data for each cohort (UNIBA1, UNIBA2, LIBD, PNC, ABCD, UKB)</li> <li>plotData.R (Script): code used to plot FNC differences across groups (Figure 3). Code used to assess group differences - Wilcoxon Rank sum test - is included.</li> </ol> - ResFNCRiskDifferences <ol> <li>res_cerebellar_occipitoparietal_ICs.csv: summary statistics of differences across groups assessed through the Wilcoxon Rank-sum test (p.adjustedFDR<0.05) on the cerebellar-occipitoparietal FNC</li> <li>res_dorsolateralPFC_sensorimotor_ICs.csv: summary statistics of differences across groups assessed through the Wilcoxon Rank-sum test (p.adjustedFDR<0.05) on medial PFC-sensorimotor FNC</li> <li>res_medialPFC_sensorimotor_ICs.csv: summary statistics of differences across groups assessed through the Wilcoxon Rank-sum test (p.adjustedFDR<0.05) on dorsolateral PFC-sensorimotor FNC</li> </ol> - ResFNCxPRS <ol> <li>ResMetanalysisPRS_FNC_R.csv: summary statistics of metanalysis on the averaged FNC x PRS association across PNC, UNIBA1, UNIBA2, LIBD, UKB cohorts on younger and older groups</li> <li>ResMetanalysisPRS_FNC_children_R.csv: summary statistics of  metanalysis on the averaged FNC x PRS association across PNC and ABCD cohorts on children.</li> <li>t2r.R (Script): code for t to R2 transform</li> <li>MetanalysisAveragedFNC_PRS.R (Script): code used perform metanalysis on the averaged FNC x PRS association across PNC, UNIBA1, UNIBA2, LIBD, UKB cohorts on younger and older groups.</li> <li>MetanalysisAveragedFNC_PRS_children.R (Script): code used perform metanalysis on the averaged FNC x PRS association across PNC and ABCD cohorts on children.</li> <li>MetanalysisPRS_FNC_sessions_R.R (Script): code used perform metanalysis on the session-specific FNC x PRS association across PNC, UNIBA1, UNIBA2, LIBD, UKB cohorts on younger and older groups.</li> </ol>   The present work includes data from UK Biobank (ID #41655), Adolescence Brain Cognitive Development (ABCD; ID #12036) Study, and Philadelphia Neurodevelopmental Cohort (PNC; ID #20998). Data are publicly accessible under approved request at the following links: - UK Biobank: <a href="https://www.ukbiobank.ac.uk/">https://www.ukbiobank.ac.uk/</a> - ABCD: <a href="https://abcdstudy.org/">https://abcdstudy.org/</a> - PNC: <a href="https://www.med.upenn.edu/bbl/philadelphianeurodevelopmentalcohort.html">https://www.med.upenn.edu/bbl/philadelphianeurodevelopmentalcohort.html</a> The ABCD data used in this report came from NIMH Data Archive DOI: https://doi.org/10.15154/1528793. Additional code for MRI data processing and Independent Component Analysis are publicly available at the following link: <a href="https://trendscenter.org/software/">https://trendscenter.org/software/</a>   Please, check https://doi.org/10.5281/zenodo.7853030 for future updates. For any data inquiries please contact: <ul> <li>Roberta Passiatore: Roberta.Passiatore@uniba.it</li> <li>Giulio Pergola: Giulio.Pergola@uniba.it - Giulio.Pergola@libd.org</li> <li>Vince D Calhoun: VCalhoun@gsu.edu</li> <li>Alessandro Bertolino: Alessandro.Bertolino@uniba.it</li> </ul>   Funding This work received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 798181 awarded to G.P. (PI) and A.B., D.R.W.; from the European Union funding within the MUR PNRR Extended Partnership initiative on Neuroscience and Neuropharmacology (Project no. PE00000006 CUP H93C22000660006 “MNESYS, A multiscale integrated approach to the study of the nervous system in health and disease”) to G.B., A.R., A.B., and G.P.; from the funding initiative Horizon Europe Seeds 2021 (Next Generation 8 EU–MUR D.M. 737/2021) for the project S68 CUP: H99J21017550006 to G.P. (PI) and L.A.A., G.B., A.R., A.B.; from the Apulian regional government for the project: “Early Identification of Psychosis Risk” to A.B.; from the Research Projects of National Relevance (PRIN) 2017 Prot. 2017K2NEF4 awarded to G.P.; from a Collaboration Grant from Exprivia Spa to G.P. under the ministerial decree D.M. n. 352/22 and A.B. under a collaboration agreement; from a Collaboration Grant from ITEL Telecomunicazioni Srl awarded to A.B.; from the 2021 Helmholtz Information and Data Science Academy Grant No. 12429 awarded to R.P. and J.D.; and from the NIH grant #R01MH118695 awarded to V.D.C. The collection of the MRI and genetic data for the LIBD cohort was supported by direct funding from the Intramural Research Program of the NIMH to the Clinical Brain Disorders Branch (PI: D.R.W., protocol 95-M-0150).

closedIPD-NOFeb 2026View details →

Understanding Lung Cancer Related Risk Factors and Their Impact

ClinicalTrials.gov study NCT06473870. IPD Sharing: NO. Countries: 3. Publications: 25.

restrictedIPD-UNDECIDEDFeb 2026View details →

Evaluation of a Neck Strengthening Program to Reduce the Risk of Sport-related Concussion

ClinicalTrials.gov study NCT02455037. IPD Sharing: Not stated. Countries: 1. Publications: 9.

controlledIPD-YESFeb 2026View details →

Evaluation of a New Eye-specific Multivitamin Formula in Participants at Risk of Age-related Macular Degeneration (AMD)

ClinicalTrials.gov study NCT03946085. IPD Sharing: YES. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

Helping In-patients to Quit Smoking by Understanding Their Risk Perception, Behavior, and Attitudes Related to Smoking

ClinicalTrials.gov study NCT02866760. IPD Sharing: NO. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

The Risk Factors Related to Rupture Flow-related Aneurysms in Posterior AVM

ClinicalTrials.gov study NCT07050381. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.