Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

6,782

datasets available to search

ShareScore release 0.9.0

Dataset results

6,782 results for “project”

Learn how ShareScore rates datasets ↗

zenodo20/100

Dataset for the project "Novel tailored network-based rTMS treatments in Alzheimer's disease: an integrated multi-imaging approach" (GR-2016-02364718)

Specific Aim 1: To test the efficacy of two network-based rTMS treatment protocols in reducing cognitive impairment. In the first protocol, the treatment will aim at stimulating the Default Mode Network (DMN) whereas the second protocol will target the Central Executive Network (CEN). Specific Aim 2: To investigate the neural mechanisms underlying response to treatment. We will investigate the modulations of brain reactivity, plasticity and connectivity induced by the treatments and how they correlate with the clinical response, thus defining quantitative markers of clinical improvement. To this aim, we will use an integrated multi-imaging approach, which will involve the use of innovative measures obtained with MRI and neurophysiological tools (TMS-EEG co-registration and theta burst stimulation - TBS protocols of rTMS). The present dataset contains the following folders: RawData_Aim1 [Unpublished Data] -                Materials and Methods_RawData_Aim1 -                RawData_Aim1: a dataset (excel file) containing the following sheets: o   Demographic data (sheet 1) o   Cognitive data – primary outcome (sheet 2) o   Cognitive data – secondary outcomes (sheet 3) RawData_Aim2 [Unpublished Data] -                Materials and Methods_RawData_Aim2 -                RawData_Aim2 - MRI: a dataset (excel file) containing the following sheet: o   Sample (col A), scanner (col B) and timepoint (col C) o   Rs-fMRI: functional connectivity (FC) values (col D-G) o   DTI: microstructural integrity values: FA (col H-K) and MD (col L-O) o   ASL: cerebral blood flow (CBF) values (col P-S)        RawData_Aim2 - TMS-EEG: a folder containing individual folders for each patient, each of which includes the following subfolders: o   T0: TEPs acquired for each target stimulation (l-CEN, r-CEN, l-DMN, r-DMN, Sham). o   T1: TEPs acquired for each target stimulation (l-CEN, r-CEN, l-DMN, r-DMN, Sham). o   T2: TEPs acquired for each target stimulation (l-CEN, r-CEN, l-DMN, r-DMN, Sham).        RawData_Aim2 - TBS: a folder containing individual folders for each patient, each of which includes the following subfolders: o   T0: MEPs acquired at baseline, at 0, 10 and 20 minutes after TBS. o   T1: MEPs acquired at baseline, at 0, 10 and 20 minutes after TBS. o   T2: MEPs acquired at baseline, at 0, 10 and 20 minutes after TBS.  Restricted Access Conditions: Users must clarify how they intend to use data here uploaded and whether the research protocol has been approved by an Ethics committee. Data on unpublished results will be available, under the same restrictions, after publication.

restrictedcc-by-nc-4.0May 2024View details →

zenodo20/100

Dataset for the project "Corticocortical signal transmission and brain connectivity alterations at prodromal stage and during the progression of Alzheimer's disease: A multimodal approach of TMS-EEG and advanced MRI" (GR-2016-02364132)

Aim 1: Validation of STT as measure of WM integrity and as diagnostic marker of AD. The first step to test the potential of the STT as AD marker is to establish if the SST can differentiate between patients (LOAD, aMCI) and healthy individuals and its relationship with clinical symptoms and with well-established measures of connectivity.  Aim 2: Connectivity changes, evaluated as in aim 1, as prognostic marker of disease progression. Another crucial point is to establish if STT can capture brain connectivity alterations with disease progression, if they parallel WM integrity alterations and cognitive decline. Moreover, the potential contribution of STT as prognostic marker can be established by investigating which measure, or combination of measures, of brain connectivity at the baseline best predicts the conversion to AD in aMCI cases.  Aim3: Connectivity changes in EOAD patients. The comparison of connectivity alterations between EOAD and LOAD at the baseline and during the disease progression will be a crucial step to establish the efficacy of STT as a marker of differential diagnosis in atypical forms of AD.   The present dataset contains the following files and folders:   File: Demographics_allparticipants.xlsx contains demographic and clinical data of the entire sample File: Hippocampal volume.xlsx data used to extract hippocampal atrophy as AD biomarker   Folder: Aim1 -        READ ME AIM1.txt explains folder structure -        TMS-EEG data of HC, MCI and LOAD at T0 are organized in 3 files with TMS-Evoked Potentials (.mat format). The resting motor threshold (rMT) measured to set TMS intensity (.xlsx file) -        Neuropsychological data of HC, MCI and LOAD at T0 (.xlsx format) -        fMRI folder contains coordinates of the hotspot for the stimulated functional networks of HC, MCI and LOAD at T0 -        DTI data (.xlsx format) reports microstructural integrity data for major inter-hemispheric and intra-hemispheric tracts of HC, MCI and LOAD at T0   -         Folder: Aim2 -        READ ME AIM2.txt explains folder structure -        TMS-EEG data are organized in one file that includes TMS-Evoked Potentials of HC, MCI and LOAD at T0 and T1 (.mat format). The resting motor threshold (rMT) measured to set TMS intensity (.xlsx file) -        Neuropsychological data of HC, MCI and LOAD at T1 (.xlsx format) -        fMRI folder contains coordinates of the hotspot for the stimulated functional networks of HC, MCI and LOAD at T1 -        DTI data (.xlsx format) reports microstructural integrity data for major inter-hemispheric and intra-hemispheric tracts of HC, MCI and LOAD at T0 and T1   Folder: Aim3  READ ME AIM2.txt explains folder structure -        TMS-EEG data are organized in two files that includes TMS-Evoked Potentials of EOAD and LOAD at T0 and T1 (.mat format). The resting motor threshold (rMT) measured to set TMS intensity (.xlsx file) -        Neuropsychological data of EOAD and LOAD at T0 and T1 (.xlsx format) -        fMRI folder contains coordinates of the hotspot for the stimulated functional networks of EOAD and LOAD at T0 and T1 -        DTI data (.xlsx format) reports microstructural integrity data for major inter-hemispheric and intra-hemispheric tracts of EOAD and LOAD at T0 and T1   Restricted Access Conditions: Users must clarify how they intend to use data here uploaded and whether the research protocol has been approved by an Ethics committee. Data on unpublished results will be available, under the same restrictions, after publication.

restrictedcc-by-nc-4.0May 2024View details →

zenodo20/100

MoHAT: Global monthly high-resolution (1 km) near-surface air temperature projections from 2001 to 2100

Please be advised that our dataset previously referred to as “MoHAT: Global monthly high-resolution (1 km) near-surface air temperature projections from 2001 to 2100” has been updated and is now available under the title “MoCHAT: Global monthly CMIP6-downscaled high-resolution (1 km) near-surface air temperature projections from 1950 to 2100”. The latest dataset can be accessed via the following URL: [<a href="https://data.tpdc.ac.cn/zh-hans/data/40d649d6-d99e-45df-9814-c0115a109396">https://data.tpdc.ac.cn/zh-hans/data/40d649d6-d99e-45df-9814-c0115a109396</a>] If you have any questions when using the MoHAT dataset, please feel free to contact Miss Xuwen Lei via <a href="mailto:leixuewen22@mails.ucas.ac.cn">leixuewen22@mails.ucas.ac.cn</a>, Dr. Qingyan Meng via <a href="mailto:mengqy@radi.ac.cn">mengqy@radi.ac.cn</a>, or Mr Qikang Zhao via <a href="mailto:yc27963@umac.mo">yc27963@umac.mo</a>.

restrictedcc-by-4.0May 2024View details →

zenodo20/100

Regional Sea-Level Change Projections to 2150 and 2300 (PROTECT)

Full-track projections of regional sea-level change from PROTECT. Due to file size constraints, only quantiles are shared here instead of complete distributions. Please check the deliverable text for details regarding these data.

restrictedmit-licenseJun 2024View details →

zenodo20/100

3D Reconstruction of Neuronal Allometry and Neuromuscular Projections in Asexual Planarians Using Expansion Tiling Light Sheet Microscopy dataset1

Open the record for dataset details and reuse information.

opencc-by-4.0Jun 2024View details →

zenodo20/100

USING EDUCATIONAL PROJECTS WITH THE HELP OF ICT IN THE EDUCATIONAL PROCESS

Open the record for dataset details and reuse information.

opencc-by-4.0Jul 2024View details →

zenodo20/100

Data and code supporting the project "A global map of travel time to access veterinarians"

The folder "data" includes 1) the dataset "vets_observed.rda", which reports the locations of veterinary practices, 2) the dataset "vets_national.csv", which reports the national estimates of veterinarians, 3) the dataset "vet_schools.csv", which reports the list of veterinary schools per country, along with the range of veterinary graduates per year, 4) the dataset "faostat_livestock_2019.csv", which reports the number and species of animals raised for food per country, 5) the dataset "sources.xlsx", which reports all the sources inspected to collect addresses of veterinary practices, and 6) the raster "obs_all_r.tif" which is the rasterized version of the "vets_observed.rda" dataset at the resolution of 10x10 km. The folder "scripts" includes the R and python code used for assembling the dataset of the locations of veterinarians and for performing all the analyses.

restrictedcc-by-4.0Jul 2024View details →

zenodo20/100

Processing RNASequencing files to a count table for the IL-10 project

Open the record for dataset details and reuse information.

opencc-by-4.0Jul 2024View details →

zenodo20/100

Source data for: Direct retino-iridal projections and intrinsic iris contraction mediate the pupillary light reflex in early vertebrates (Communications Biology)

Open the record for dataset details and reuse information.

opencc-by-4.0Jul 2024View details →

zenodo20/100

Extreme events changes over China under 1.5-4°C global warming targets: projected by an ensemble of regional climate model simulations

This file is for the upload of data for 2019JD031057R.

opencc-by-4.0Jul 2019View details →

zenodo20/100

SPLOA project raw data_updated HJbodies

Open the record for dataset details and reuse information.

opencc-by-4.0Apr 2024View details →

zenodo20/100

Fig. 7 in Is the copulation of silver-washed fritillary inverted? Function of the female genital projection (Lepidoptera: Nymphalidae: Argynnini)

Fig. 7 Relationship between forewing length (A) or cornucopia size (B) and the number of deciduous cornuti in once- (circle) and twice- (triangle) mated female Argynnis paphia

opennotspecifiedOct 2023View details →

zenodo20/100

Fig. 6 in Is the copulation of silver-washed fritillary inverted? Function of the female genital projection (Lepidoptera: Nymphalidae: Argynnini)

Fig. 6 CLSM images of the male cornuti (A) and section of the female cornucopia (B). White arrowheads in A indicate broken cornuti. In B, v and d indicate the ventral and dorsal sides in the copulatory condition. Red indicates strongly sclerotized portions, and blue indicates resilin-rich portions

opennotspecifiedOct 2023View details →

zenodo20/100

Fig. 5 A in Is the copulation of silver-washed fritillary inverted? Function of the female genital projection (Lepidoptera: Nymphalidae: Argynnini)

Fig. 5 A. Copulatory wounds (indicated by red arrowheads) observed on the dorsal membranous region of the female cornucopia. Two rows of wound lines (indicated by arrowheads in different directions) were detected in this individual. B. Copulatory wound occurred at an abnormal position in the female without cornucopia. "S" indicates surgically removed cornucopia (ventral view). Head comes to bottom

opennotspecifiedOct 2023View details →

zenodo20/100

Dataset: Applicability of open science practices to completed research projects from different disciplines and research paradigms

This is the archived data for the reserach project "Applicability of open science practices to completed research projects from different disciplines and research paradigms". Data access is restricted.

restrictedcc-by-4.0Aug 2024View details →

zenodo20/100

Dataset for the project "The effects of a multimodal approach for the treatment of Primary Progressive Aphasia" (GR-2018-12365105)

Dataset for the project “The effects of a multimodal approach for the treatment of Primary Progressive Aphasia” (GR-2018-12365105) Manenti Rosa (https://orcid.org/0000-0002-5955-2323); Premi Enrico (https://orcid.org/0000-0002-4342-6963  Dataset for the project “The effects of a multimodal approach for the treatment of Primary Progressive Aphasia” Italian Ministry of Health, Italy, Ricerca Finalizzata Young Researcher (Grant GR-2018-12365105) Specific Aim 1 Evaluate the specific short-term and long-term effects of active tDCS over the dorsolateral prefrontal cortex (DLPFC) combined with language training vs. placebo DLPFC-tDCS with language training vs. active DLPFC-tDCS alone. In particular, we aim to establish if the combined use of active tDCS and language training in avPPA patients can induce an improvement in a) cognitive and functional performances and b) a change in molecular markers such as neurogranin. Specific Aim 2 a) Evaluate the brain activity changes related to the intervention in targeted brain regions to establish BOLD signal fluctuations (fMRI); b) Evaluate the variations in spontaneous hemodynamic cortical activity and reactivity with a fNIRS assessment.  To this aim, we will perform fMRI scanning and fNIRS assessment before and after the application of the three therapy protocols to provide real-time measures of whole-brain activity modulation that are induced in the activated network. The present dataset contains the following data: •    RawData_Aim1a [Unpublished Data] o    RawData_Aim1a_Clinical_Neuropsychiological_Assessment: a dataset (excel file) containing the variables referred to overall cognitive and functional performance at timing T0 (baseline), T1 (post-treatment) and T2 (follow-up)   •    RawData_Aim1b_ Molecular markers [Unpublished Data] o    RawData_Aim1b_ Molecular markers: an excel file containing:       - RawData_Aim1b_ Plasma_BDNF_Neurogranin: an excel sheet containing a dataset with the following variables:          Sample (Col. A);          Raw Values Plasma BDNF T0 (Col. B-D);          Raw Values Plasma BDNF T1 (Col. E-G);          Raw Values Plasma Neurogranin T0 (Col. H-J);           Raw Values Plasma Neurogranin T1 (Col. K-M).        - RawData_Aim1b_ RawData_BDNF_Simoa_Test: an excel sheet containing a dataset with the following variables:          Sample (Col. A);          Raw Simoa Values Plasma BDNF T0 (Col. B-D);           Raw Simoa Values Plasma BDNF T1 (Col. E-G). •    RawData_Aim2a_ MRI [Unpublished Data] o    RawData_Aim2a_ MRI: a folder containing:       - RawData_Aim2a_structural_MRI: a folder containing images referred on structural changes from timing T0 (baseline) to T1 (post-treatment)        - RawData_Aim2a_functional_MRI: a folder containing images referred on functional changes from timing T0 (baseline) to T1 (post-treatment)  •    RawData_Aim2b_ fNIRS [Unpublished Data] o    RawData_Aim2b_ fNIRS: a folder containing:       - RawData_Aim2b_ fNIRS: raw fNIRS files for each subject at timing T0 (baseline), T1 (post-treatment) and T2 (follow-up) and a dataset (excel file) containing the following variables:        Code (Col. A)        Age (Col. B)        Education (Col. C)        Gender (Col. D)        fNIRS condition at timing T0 (baseline) (Col. E)        Duration of conditions at timing T0 (baseline) (Col. F, G, H, I)        fNIRS condition at timing T1 (post-treatment) (Col. J)        Duration of conditions at timing T1 (post-treatment) (Col. K, L, M, N)        fNIRS condition at timing T2 (follow-up) (Col. O)        Duration of conditions at timing T2 (follow-up) (Col. P, Q, R, S) Preliminary Data [Published Data] Preliminary results of the project are published in: •    Premi&Dukart et al., Human Brain Map 2023 (doi: 10.1002/hbm.26206) and the raw data supporting the results are published in the JuSpace toolbox at https://github.com/juryxy/JuSpace •    Premi&Cotelli et al., Front Aging Neurosci. 2022 (doi: 10.3389/fnagi.2022.942095) and the raw data supporting the results are published in the Zenodo Data Repository at doi: 10.5281/zenodo.6874926 and here reported:      - RawData_ Premi&Cotelli:  a dataset (excel file) containing variables referred to correlations between grey matter volume and language deficits in avPPA at timing T0  You need to satisfy these conditions in order for your request to be accepted: Users must clarify how they intend to use data here uploaded and whether the research protocol has been approved by an Ethics committee. Data on unpublished results will be available, under the same restrictions, after publication.

restrictedcc-by-4.0May 2024View details →

zenodo20/100

NFRF Project Patient Dataset with Febrile Diseases

This dataset contains detailed records of patients diagnosed with febrile diseases, collected as part of a research project funded by the New Frontier Research Fund under grant number NFRFE-2019-01365. The dataset includes comprehensive patient data such as symptoms, risk factors, suspected diagnosis, further investigation, and confirmed diagnoses, specifically focusing on various febrile conditions.

restrictedcc-by-4.0Sep 2024View details →

zenodo20/100

Dataset for the project "Emotional dysregulation in adolescents: clinical and biological correlates" Italian Ministry of Health, Ricerca Finalizzata (GR-2018-12366754)

Users must clarify how they intend to use data here uploaded and whether the research protocol has been approved by an Ethics committee. Data on unpublished results will be available, under the same restrictions, after publication.   Dataset for the project “Emotional dysregulation in adolescents: clinical and biological correlates”, Italian Ministry of Health, Ricerca Finalizzata (GR-2018-12366754)  Specific Aim 1: To analyze the level of emotional dysregulation (ED) in two samples of adolescents (a community sample of adolescent students; a clinical sample of adolescent patients) and to assess the association between ED, clinical and social variables by comparing subgroups of adolescents showing different levels of ED. Specific Aim: 1.1: To evaluate the level of ED and clinical variables before and after the pandemic on a sample of adolescent students. Specific Aim 2: To investigate whether alterations in biological features related to stress response and inflammation can underlie ED and the vulnerability for psychopathology. Specific Aim 3: To assess the outcomes of a brief psychoeducational interventions for adolescent students by evaluating change in maladaptive behaviors and emotional regulation skills.    The present dataset contains the following four folders:          Folder Rawdata_Aim1 [Unpublished Data]: it includes two files: ·        File DB_aim1_students.sav: a dataset containing in the first column the sample ID the following rawdata: →   Demographic data →   Health-related data →   Lifestyle habits →   Social context →   Academic functioning →   Difficulties in Emotion Regulation Scale →   Checklist for impulsive, dysfunctional behaviors and emotional regulation skills →   Personality Inventory →   Emotion Regulation Questionnaire →   Toronto Alexythymia Scale →   State-Trait Anger Expression Inventory →   Screen for Child Anxiety Related Emotional Disorders →   Barratt Impulsiveness Scale →   Patient Health Questionnaire →   Childhood Trauma Questionnaire →   Strengths and Difficulties Questionnaire ·        File DB_aim1_patients.sav: a dataset containing in the first column the sample ID and the following rawdata →   Demographic data →   Health-related data →   Lifestyle habits →   Social context →   Academic functioning →   Difficulties in Emotion Regulation Scale →   Checklist for impulsive, dysfunctional behaviors and emotional regulation skills →   Personality Inventory →   Emotion Regulation Questionnaire →   Toronto Alexythymia Scale →   State-Trait Anger Expression Inventory →   Screen for Child Anxiety Related Emotional Disorders →   Barratt Impulsiveness Scale →   Patient Health Questionnaire →   Childhood Trauma Questionnaire →   Strengths and Difficulties Questionnaire     Folder Rawdata_Aim1.1 [published Data] Results from aim 1.1 are published in: Pedrini, L., Meloni, S., Lanfredi, M., Ferrari, C., Geviti, A., Cattaneo, A., & Rossi, R. (2022). Adolescents' mental health and maladaptive behaviors before the Covid-19 pandemic and 1-year after: analysis of trajectories over time and associated factors. Child and adolescent psychiatry and mental health, 16(1), 42. Pedrini, L., Meloni, S., Dawson, J., Geviti, A., Bonfiglio, N. S., Cattaneo, A., & Rossi, R. (2024). The Associations between Lifestyle Habits and Anxiety: A Prospective Study on Adolescents One Year after the Outbreak of the COVID-19 Pandemic. Children (Basel, Switzerland), 11(3), 282.   The folder Rawdata_Aim1.1 includes the following file: ·        File DB_ PrePost pandemic.sav: a dataset containing in the first column the sample ID and the following rawdata →   Demographic data →   Lifestyle habits at T0 →   Lifestyle habits at T1 →   Social context variables →   Experience during lockdown →   Checklist for impulsive, dysfunctional behaviors and emotional regulation skills at T0 →   Checklist for impulsive, dysfunctional behaviors and emotional regulation skills at T1 →   Difficulties in Emotion regulation Regulation Scale at T0 →   Difficulties in Emotion regulation Regulation Scale at T1 →   Screen for Child Anxiety Related Emotional Disorders at T0 →   Screen for Child Anxiety Related Emotional Disorders at T1 →   Barratt Impulsiveness Scale at T0 →   Barratt Impulsiveness Scale at T1 →   Patient Health Questionnaire at T0 →   Patient Health Questionnaire at T1         Folder Rawdata_Aim2 [Unpublished Data]: ·        Rawdata_Aim2: A dataset (folder) containing the following excel files: -        Rawdata_GeneExpression:  An excel file containing in the first column the sample ID and in the following columns output of gene expression analyses (one column for each gene): →   GAPDH →   FKBP5   →   NR3C1  →   SGK1     →   FOXO1  →   MIF       →   TNF       →   CLOCK  →   IL6         →   IL10      →   IL13Ra  →   IL6ST    →   NLRP3  →   PER2     →   TSC22D3             →   RGS2     →   IL1B      →   UBC      →   DISC1   →   CXCL10 →   DUSP1  →   P2RX7   →   IL18      →   CRY2     →   IL1RN   →   TNFRSF1B          →   TNFRSF1A          →   CCL20   →   CDNF    →   CXCL8   →   EIF4a2  →   CCL2     →   HLA_DQA1         →   ITGAM  →   JAK2      →   MECP2 →   NFE2L2 →   PNRP    →   S100a10             →   STAT1    →   SATB1   →   TGFB1   →   WDR12               →   NCOA3 →   SIRT1    →   PPARa   →   NCOR1 →   NCOR2 →   MT2A   →   CD68    →   CD40    →   CD14    →   BMPR2 →   BCL2     →   BAX       →   A2M      →   AIF1L    →   BDNF ISO VI-IX →   IGF1R   →   IRF7      →   NOS      →   PICK1    →   COX20 -        Rawdata_ProteinExpression: An excel file containing two worksheets: →   RawValues IMMUNE-Luminex assay, listing sample ID (Col. A) and Proteins Concentration Values (Col. B – Col. V); →   RawValues CORTISOL -ELISA assay, listing sample ID (Col. A) and Cortisol Concentration Values at three distinct times of the day (Col. B – Col. D).     Ø  Folder Rawdata_Aim3 [Unpublished Data]: it includes the following file: ·        File DB_aim3.sav: a dataset containing in the first column the sample ID and the following rawdata: →   sociodemographic data →   Checklist for impulsive, dysfunctional behaviors and emotional regulation skills at T0 →   Checklist for impulsive, dysfunctional behaviors and emotional regulation skills at T1 →   Checklist for impulsive, dysfunctional behaviors and emotional regulation skills at T3 →   Checklist for impulsive, dysfunctional behaviors and emotional regulation skills at T6 →   Social Problem-Solving Inventory-Revised Short Form at T0 →   Social Problem-Solving Inventory-Revised Short Form at T1 →   Social Problem-Solving Inventory-Revised Short Form at T3 →   Social Problem-Solving Inventory-Revised Short Form   at T6 →   Psychological Well-being Scale at T0 →   Psychological Well-being Scale at T1 →   Psychological Well-being Scale at T3 →   Psychological Well-being Scale at T6

restrictedcc-by-4.0Sep 2024View details →

zenodo20/100

Reduced-Order Model of Time-Projection Chamber

These datasets are generated by a reduced-order model (ROM) of the sPHENIX Time-projection chamber (TPC), inner layer group. We have two datasets: <ol> <li>`dataset.zip` and</li> <li>`dataset_with_label.zip`.</li> </ol> Each dataset contains 8000 training examples and 2000 test examples. Each sample in the dataset contains ~50 to 100 trajectories. Samples in both dataset contain fields: `response`, `tracks`, and `params `. Samples in the `dataset_with_label.zip` has an additional field `label`.  Here are the description of fields: <ul> <li>`response` (shared): float tensor of shape (16, 256, 1152) in radial (layer), axial (horizontal), and azimuthal directions. </li> <li>`tracks` (shared): float tensor of shape (N, 2, 16), where N is the number of trajectories. Each trajectory is recorded as a tensor of shape (2, 16). The two numbers for each layer (last axis) is the angle (in the azimuthal direction) and axial location of the trajectory. </li> <li>`params` (shared): float tensor of shape (N, 4). The four numbers for the n-th entry are initial momentum (3D) and mass-charge ratio (scalar) of the particle that generates the n-th trajectory.</li> <li>`label` (`dataset_with_label` only): integer tensor of shape (16, 256, 1152), the value for voxels with positive response is the trajectory id that contribute the most to the voxel. The value for voxels with zero response is random and should not be used.</li> </ul>

restrictedcc-by-4.0Jul 2024View details →

zenodo20/100

SITLESS project - Efficacy data from scales and assessments

This data collection comprises four different sub-datasets: - Efficacy_A1_A2_A3_A4.csv and Thight_A1_A2_A3_A4.csv contain original data from the SITLESS project, the first corresponding to efficacy and Actigraph-based physical activity assessments, and the second corresponding to efficacy and thigh-worn physical activity assessments.  - Efficacy_A1_A2_A3_A4 imputed.dta and Thigh_A1_A2_A3_A4_imputed.dta contain multiple imputation data for the two first datasets.  Efficacy_A1_A2_A3_A4.csv contains collected data on sociodemographic and lifestyle characteristics, physical function, morbidity, disability, quality of life, social capital and safety. The data was collected by a self-reported questionnaire, by direct interview, direct functional performance assessments. The list of items obtained by self-report includes smoking status; diagnosed diseases in a pre-defined list (e.g. hypertension, myocardial infarction, stroke, heart failure, diabetes); self-rated health and health-related quality of life; mental wellbeing and social capital; and physical activity self-regulation. The list of items obtained by interview includes disability, fatigue and institutionalization in a nursing home; self-efficacy for exercise; and safety and cost parameters (falls, injuries and use of sports and health services). The list of items measured by performance assessment includes anthropometric measures and bioimpedance; physical performance; mean strength and power; handgrip strength; and summaries of physical activity volume and patterns derived from the raw data obtained from the Actigraph, ActivPAL and Axivity activity monitors (which conform Thigh_A1_A2_A3_A4.csv and Thigh_A1_A2_A3_A4_imputed.dta datasets ) . Participants were assessed four times (baseline, end of intervention, follow up at 12 months, follow up at 18 months after end of intervention) through clinical interview. Data in this collection are anonymized, and identified by the anonymized participant code.

restrictedJun 2021View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record

Find research datasets worth reusing

Dataset results

Dataset for the project "Novel tailored network-based rTMS treatments in Alzheimer's disease: an integrated multi-imaging approach" (GR-2016-02364718)

Dataset for the project "Corticocortical signal transmission and brain connectivity alterations at prodromal stage and during the progression of Alzheimer's disease: A multimodal approach of TMS-EEG and advanced MRI" (GR-2016-02364132)

MoHAT: Global monthly high-resolution (1 km) near-surface air temperature projections from 2001 to 2100

Regional Sea-Level Change Projections to 2150 and 2300 (PROTECT)

3D Reconstruction of Neuronal Allometry and Neuromuscular Projections in Asexual Planarians Using Expansion Tiling Light Sheet Microscopy dataset1

USING EDUCATIONAL PROJECTS WITH THE HELP OF ICT IN THE EDUCATIONAL PROCESS

Data and code supporting the project "A global map of travel time to access veterinarians"

Processing RNASequencing files to a count table for the IL-10 project

Source data for: Direct retino-iridal projections and intrinsic iris contraction mediate the pupillary light reflex in early vertebrates (Communications Biology)

Extreme events changes over China under 1.5-4°C global warming targets: projected by an ensemble of regional climate model simulations

SPLOA project raw data_updated HJbodies

Fig. 7 in Is the copulation of silver-washed fritillary inverted? Function of the female genital projection (Lepidoptera: Nymphalidae: Argynnini)

Fig. 6 in Is the copulation of silver-washed fritillary inverted? Function of the female genital projection (Lepidoptera: Nymphalidae: Argynnini)

Fig. 5 A in Is the copulation of silver-washed fritillary inverted? Function of the female genital projection (Lepidoptera: Nymphalidae: Argynnini)

Dataset: Applicability of open science practices to completed research projects from different disciplines and research paradigms

Dataset for the project "The effects of a multimodal approach for the treatment of Primary Progressive Aphasia" (GR-2018-12365105)

NFRF Project Patient Dataset with Febrile Diseases

Dataset for the project "Emotional dysregulation in adolescents: clinical and biological correlates" Italian Ministry of Health, Ricerca Finalizzata (GR-2018-12366754)

Reduced-Order Model of Time-Projection Chamber

SITLESS project - Efficacy data from scales and assessments

Understand access before you commit

Allen Brain Atlas

Annotated Behaviour and Observability Dataset (ABODe)

DANDI Archive for NWB datasets

International Brain Laboratory public data

OpenNeuro

Your academic CV and NIH biosketch