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6,782 results for “project”
HIV+ Tumor Molecular Characterization Project
The Center for Cancer Genomics, along with the Office of HIV and AIDS Malignancies, initiated the HIV+ Tumor Molecular Characterization Project (HTMCP) to gain insight into the genetic events driving HIV-associated cancers and to determine why certain cancers, but not others, have higher incidences in HIV-positive patients. The molecular characterization data from patients identified through HTMCP will be available to the research community worldwide in a publicly available, yet patient privacy-protected database. Acquired immunodeficiency syndrome (AIDS) is a complex and devastating disease caused by infection with human immunodeficiency virus (HIV). The advent of highly active antiretroviral therapy has considerably slowed disease progression from HIV to full-blown AIDS, thereby increasing the number of people living with HIV. Despite this success in survivorship, certain types of cancers are becoming more prevalent in the expanding pool of HIV-infected individuals. While coinfecting viruses and, possibly, immunodeficiency may play a role in the pathogenesis of HIV-associated cancers, our understanding of its etiology is inadequate. Understanding the molecular causes of these tumors may translate into improved therapies for a growing population of patients doubly afflicted with HIV and cancer.
The Institute for Genomic Medicine at Nationwide Children's Hospital Pediatric Cancer and Blood Disorder Project
The aim of this study is to enable the unification of the clinical and research arms of comprehensive genomic profiling in the setting of cancer and hematologic disease. Increasingly, studies of the genomic etiology of cancer and hematologic diseases are being utilized for patient management, including prognostication, informing diagnosis, and evaluating eligibility for targeted therapeutics and clinical trials. Clinically available testing can be limited in scope and follow-up, and thus assessment of the impact on patient outcomes is difficult to ascertain. N-of-1 studies have the ability to more broadly impact our understanding of the mechanisms of cancer and hematologic disease, and can have benefit for the individual as well as the larger population of patients with these diseases. Thus, we seek to study and characterize the genomic underpinnings of cancer and hematologic disease through genomic profiling studies bridging both clinical and research components. We will rigorously perform and vet research sequencing and genomic studies protocols for the purpose of transitioning these assays to clinically valid testing for use in clinical patient care.
MLCQProjects: Towards evolving data set of industry-relevant software projects
<p>Short: This data set contains three snapshots from an evolving software project data set and a script that generates those snapshots. Projects in snapshots are annotated with features used to assess industry-relevance - documentation links, installation means, support channels and whether the project delivers functionalities or samples.</p> <p>Context: Researchers involved in mining software repositories face a challenge that many of existing data sets reflect how software was developed in the past (typically many years ago), instead of in the present. Another challenge is that data sets based on open-source software projects include projects that are not necessarily industry-relevant.</p> <p>Aim: The aim of this paper is to address the aforementioned challenges and provide both: 1) a snapshot-based evolving data set of software projects (thus reflecting their present, as well as previous states), manually enriched with data considered important for industrial relevance assessment, and 2) a method of assessing industrial relevance of software projects.</p> <p>Method: We present a systematic method of selecting data sets of software projects for the purposes of mining software repositories of potentially industry-relevant projects and a semi-systematic method of assessing the industrial relevance of those projects.</p> <p>Data set: The data set contains three snapshots (spanning over 10 months) of popular Java projects from GitHub, manually enriched with industrial relevance and maintenance-related information. The presented acquisition method is sufficient to generate further snapshots and one should be able to assess the industrial relevance of the projects using the presented assessment method. Provided data set open directions of further research, e.g, 1) evaluation of code smells or defect prediction models on industry-relevant software projects prepared by independent authors and not used to build models, 2) analysis of some social aspects of open source projects (such as tooling used for providing support) and how they evolve in time.</p>
Energy company survey of the MEDEA project
<p>This is a data set and supporting presentations to the "Breakthrough Technologies and Incremental Innovation: the Edge of Innovation in Oil and Gas industry, Level of R&D expenditure Versus Results in the Energy Companies" paper and presentation presented at the 2009 Corporate University in Milan, Italy.</p> <p>The data set and supporting presentations cover:</p> <ul> <li>Presentation - Environment Economy-Environmental Kuznets Curve</li> <li>Presentation - Solar Modules Projects</li> <li>Paper - Strategic Environmental Assessment simulation exercise</li> <li>Presentation - The procurement process in the vision of the drilling and completions area: current status, limits and possible areas for improvement</li> <li>Oil and Gas data set</li> </ul> <p>This material is closely related to the Baltic Sea, the Mediterranean Sea and renewable energy, oil & gas activities in general.</p>
DATASET - Baseline data LIFT Synthesis Writing project
<p>Dataset with data on text quality, keystroke logging process measures, and writer characteristics. </p> <p>This dataset serves as a baseline for studies on synthesis writing.</p>
Empirical analysis of Type-Related Defects in Python projects
<p>This is the replication material related to the following paper submitted for TSE.</p> <ul> <li>Faizan Khan, Boqi Chen, Daniel Varro, and Shane McIntosh. An Empirical Study ofType-Related Defects in Python Projects.IEEE Transactions on Software Engineering,2021(under review)</li> </ul>
Empirical analysis of Type-Related Defects in Python projects
<p>This is the replication material related to the following paper submitted for TSE.</p> <ul> <li>Faizan Khan, Boqi Chen, Daniel Varro, and Shane McIntosh. An Empirical Study ofType-Related Defects in Python Projects.IEEE Transactions on Software Engineering,2021(under review)</li> </ul>
Reconstruction of hippocampus CA1 stratum oriens, back-projecting cell
<p>title : Reconstruction of hippocampus CA1 cell morphologies</p> <p>specimen : Rattus norvegicus</p> <p>sex : male</p> <p>weight : 100-200 g</p> <p>This neuron was recorded and filled with biocytin in a 450-500 µm thick coronal slice of adult rat hippocampus, using sharp electrodes (for methods see PMID: 11807843). The fixed slice was resectioned at 50-60 µm and the cell reconstructed with Neurolucida using a 100x oil immersion objective.</p>
Future NG-IDF Dataset for CONUS Design Extreme Event Projections
<p><strong>Future NG-IDF dataset</strong> consists of projections of design extreme events in future climates across the CONUS. Future climates are represented by an ensemble of 10 downscaled CMIP5 global climate models (GCMs) under the RCP8.5 emission scenario. The dataset covers over 200,000 locations at a 1/16th-degree resolution. This dataset extends Sun et al. (<a href="10.5281/zenodo.5827028">2019</a>) by including future projections following the Next-Generation Intensity-Duration-Frequency (NG-IDF) approach (Yan et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR021290">2018</a>). In contrast to traditional precipitation-based IDF methods such as NOAA Atlas 14, which neglects snow processes, NG-IDF accounts for the total water reaching the land surface (<strong>AWR</strong>) from rainfall, snowmelt, and rain-on-snow (ROS) events.</p><p><strong>Downscaled GCM</strong> climate forcing was derived through the Multivariate Adaptive Constructed Analogs (MACA) method for statistical downscaling and were bias-corrected against the Livneh gridded meteorological dataset (Livneh et al., <a href="https://doi.org/10.1175/JCLI-D-12-00508.1">2013</a>), which is the climate forcing used for the historical CONUS NG-IDF (Sun et al., <a href="10.5281/zenodo.5827028">2019</a>). The selection of the 10 GCMs was based on their divergence in projecting future changes in temperature and precipitation for the CONUS, as well as their accuracy in representing historical climate conditions. The chosen GCMs are bcc-csm1-1, BNU-ESM, CCSM4, CNRM-CM5, CSIRO-Mk3-6-0, inmcm4, IPSL-CM5A-LR, IPSL-CM5A-MR, MRI-CGCM3, and NorESM1-M. The MACA downscaled GCM projections for future climate forcing are available at: <a href="http://thredds.northwestknowledge.net:8080/thredds/catalog/NWCSC_INTEGRATED_SCENARIOS_ALL_CLIMATE/macav2livneh/catalog.html">Northwest Knowledge Network</a>.</p><p><strong>Driving Mechanism Classification for Extreme AWR Events:</strong></p><p>Extreme AWR events at each location are classified by their driving mechanisms as follows:</p><blockquote><p>precip: Sum of snowfall and rain, with the assumption that both contribute instantaneously to runoff</p><p>rainfall: Precipitation on snow-free ground.</p><p>melt_only: Decreasing SWE, daily rainfall < 10 mm, and the sum of rainfall and snowmelt has < 20% contribution from rainfall.</p><p>ROS: Rain-on-snow - Decreasing SWE with at least 10 mm of daily rainfall falling on a snowpack with at least 10 mm daily SWE, and snowmelt contributes at least 20% of the total of rain and snowmelt.</p><p>AWR: Amount of water reaching the land surface from rain, snowmelt, and ROS events.</p></blockquote><p><strong>Folder Structure:</strong></p><blockquote><p>historical/matlab_matrix/: baseline period</p><p>rcp85/matlab_matrix/: future period</p></blockquote><p>For each GCM, there are 'historical' and 'rcp85' subfolders for baseline and future periods, respectively. Within the subfolder, files list discrete IDF values by event duration (24h, 48h, and 72h) and mechanism (<i>AWR</i>, <i>precip</i>, <i>rainfall</i>, <i>ROS</i>, and <i>melt_only)</i>.</p><p><strong>File Naming Convention: </strong></p><blockquote><p>The naming convention is [duration]_[mechanism].mat, e.g., 24hr_AWR.mat</p></blockquote><p><strong>Data Dimension</strong>: </p><blockquote><p>The data is structured in a three-dimensional array of 381 x 921 x 7. The first dimension corresponds to the latitude index, and the second to the longitude index, mapping to specific geographic coordinates. The third dimension represents NG-IDF values for return periods of 2, 5, 10, 25, 50, 100, and 500 years. 'NaN' indicates no runoff generated by a specific mechanism, such as ROS. </p></blockquote><blockquote><p><strong>Coordinate Reference:</strong></p></blockquote><blockquote><p>Each latitude and longitude index is directly mapped to the corresponding row or line number in the 'CONUS_livneh_lat.csv' and 'CONUS_livneh_lon.csv' files, which contain 381 and 921 lines, respectively.</p></blockquote><p><strong>Unit of NG-IDF Values</strong>:</p><blockquote><p>millimeter (mm)</p></blockquote><p> </p>
DIAPASON Project: EXPLORING SEX DIFFERENCES IN PHYSICAL ACTIVITY PATTERNS AMONG INDIVIDUALS WITH SCHIZOPHRENIA SPECTRUM DISORDER: INSIGHTS FROM THE DIAPASON PROJECT
<p>Dataset of the paper entitled <i>EXPLORING SEX DIFFERENCES IN PHYSICAL ACTIVITY PATTERNS AMONG INDIVIDUALS WITH SCHIZOPHRENIA SPECTRUM DISORDER: INSIGHTS FROM THE DIAPASON PROJECT from the DIAPASON project</i> of the project "DAILY TIME USE, PHYSICAL ACTIVITY, QUALITY OF CARE AND INTERPERSONAL RELATIONSHIPS IN PSYCHIATRIC RESIDENTIAL FACILITIES: AN ITALIAN MULTICENTRE STUDY" Italian Ministry of Health, Italy, Ricerca Finalizzata (Grant RF-2018-12365514) (https://www.diapason-study.eu/)</p><p> </p>
DIAPASON Project: ASSOCIATION OF WEIGHT STATUS WITH PHYSICAL ACTIVITY LEVELS IN PEOPLE WITH SCHIZOPHRENIA SPECTRUM DISORDERS AND HEALTHY CONTROLS
<p>Dataset of the paper entitled "<i>ASSOCIATION OF WEIGHT STATUS WITH PHYSICAL ACTIVITY LEVELS IN PEOPLE WITH SCHIZOPHRENIA SPECTRUM DISORDERS AND HEALTHY CONTROLS</i>" <i>from the DIAPASON project</i> of the project "DAILY TIME USE, PHYSICAL ACTIVITY, QUALITY OF CARE AND INTERPERSONAL RELATIONSHIPS IN PSYCHIATRIC RESIDENTIAL FACILITIES: AN ITALIAN MULTICENTRE STUDY" Italian Ministry of Health, Italy, Ricerca Finalizzata (Grant RF-2018-12365514) (https://www.diapason-study.eu/)</p>
Working Data and Evaluation Data for STRIDE F4 Project (Project O6 uses same datasets)
<p>As our STRIDE O6 project is the extension of F4. F4 and O6 utilized the same data, which composed of two datasets:</p> <p>1. BSMP1.csv.zip is the working data. This data set was acquired by free download from DATA.GOV at:</p> <p>https://catalog.data.gov/dataset/safety-pilot-model-deployment-data/resource/4029f4ea-43f6-4b2d-8a33-1c2b690ba416</p> <p>As the data size is 55.71 GB, which exceeds the upload limit, we can not upload it here. Notice it can be downloaded free from the link above.</p> <p>2. Export.7z is the evaluation data. This data was purchased from VITT. </p> <p>Both datasets are in the format of CSV. For detail, please refer to our project final report.</p> <p> </p>
Source code and intermediate data for the tradeSeqDTU project
<p>Development: working folder for reproducing the results of the mock and performance benchmarks.</p> <p>Pancreas_case: intermediate data and code for the pancreas case study. The raw data is not yet included as this is still a project in progress, and the upload would be very large (15Gb)</p> <p>iPSC_case: intermediate data and code for the pancreas case study. The raw data is not yet included as this is still a project in progress, and the upload would be very large (32Gb)</p>
PROTECT global mean sea level projections D7.6 (PRELIMINARY)
Open the record for dataset details and reuse information.
Dataset project SG-2019-12369935
<p>The present database collects multidomain variables recorded from a set of 302 infants at higher likelihood of developing autism spectrum disorder (HL-ASD), higher likelihood of developing language learning impairment (HL_LLI) and lower likelihood for both ASD and LLI (LL). Inclusion criteria related to the three groups are reported in Piazza et al., 2023. Below there is the description of each variable available in the database.</p> <p>· Column A, Family risk: 0 = LL subject; 1 = HL_LLI subject; 2 = HL_ASD subject;</p> <p>· Column B, Clinical_risk_T36: 0 = typically developing (TD); 1 = ASD; 2 = LLI; 3 = other; The TD group included only children from the original LL group who at 36 month of age scored above the 30th percentile in the parent-administered Language Development Survey (LDS, Rescorla nad Alley, 2001), a questionnaire aimed at assessing expressive language development (standardized norms available for the Italian population (Rescorla et al., 2014). The clinical risk for ASD was confirmed based on the presence of a diagnosis of ASD according to the criteria of the DSM-5 diagnostic manual (APA, 2013) or the presence of moderate/severe autistic signs, assessed through the administration of the ADOS-2 at 36 months, with comparison scores > 6 (Dodd and Carr, 2003; Lord et al., 2012). The clinical risk for LD was confirmed under the following conditions: (a) obtaining a percentile < 5th percentile or < -2 SD in at least one standardized language test conducted at 36 months (expressive vocabulary, lexical comprehension, naming); (b) obtain a percentile < 10th or < -1.5 SD in at least two of the three tests listed above; (c) obtain a phono-articulatory accuracy score (Percentage of Correct Consonants) < 55% (Dodd and Carr, 2003); or (d) present a clinical indication of delayed/atypical phonological processes.</p> <p>· Column C, T6_age: age in days of the EEG recording performed at 6 months of age.</p> <p>· Column D, T12_age: age in days of the EEG recording performed at 12 months of age.</p> <p>· Column E, Gender: 0 = male; 1 = female.</p> <p>· Column F, Age_m: age of the mother at birth (years).</p> <p>· Column G, Age_f: age of the father at birth (years).</p> <p>· Column H, Pregnancy_diseases: 0 = no; 1 = yes.</p> <p>· Column I, Delivery_problems: 0 = no; 1 =yes.</p> <p>· Column J, Breastfeeding: 1 = yes; 2 = no; 3 = mixed.</p> <p>· Column K, EDUMD: Maternal education scored on an ad-hoc 90-point ordinal scale based on the Italian school system. Scores range between 10, corresponding to the fifth grade of elementary school; 50, equivalent to a high-school diploma; and 90, corresponding to post-doctoral degree.</p> <p>· Column L, EDUPD: Paternal education scored on an ad-hoc 90-point ordinal scale based on the Italian school system. Scores range between 10, corresponding to the fifth grade of elementary school; 50, equivalent to a high-school diploma; and 90, corresponding to post-doctoral degree.</p> <p>· Column M, SES: Socio-Economic Status scored according to Hollingshead 90-point scale, whereby a score ranging 10–90 was assigned to each parental job and the higher of two scores was used when both parents were employed (Hollingshead, 1975). Scores range between 10, corresponding to unskilled workers; 50, corresponding to sales workers; and 90, corresponding to major professionals.</p> <p>· Column N, GW: Gestational age, i.e. number of weeks of gestation at birth.</p> <p>· Column O, BW: Weight at birth in grams.</p> <p>· Column P, T6_IQ: IQ assessed at 6 months of age according to the Bayley Scales of Infant and Toddler Development- Third Edition (Bayley, 2006) and/or Griffiths mental development scales (0-2) (GMDS-R, Luiz et al., 2006).</p> <p>· Column Q, T24_IQ: IQ assessed at 24 months of age according to the Bayley Scales of Infant and Toddler Development- Third Edition (Bayley, 2006) and/or Griffiths mental development scales (0-2) (GMDS-R, Luiz et al., 2006).</p> <p>· Column R, T24_VOC_pct: Expressive vocabulary at 24 months of age, scored according to the Language Development Survey (LDS) (Rescorla et al., 2014) and/or the PVB questionnaire (Caselli e Casadio, 1995). Percentile scores are reported.</p> <p>· Column S, T36_VOC_pct: Expressive vocabulary at 36 months of age, scored according to the Language Development Survey (LDS) (Rescorla et al., 2014) and/or the PVB questionnaire (Caselli e Casadio, 1995). Percentile scores are reported.</p> <p>· Column T, ADOS_CSS_TOT: ADOS-2 (Calibrated Severity Scores-CSS) total score at 36 months. To maximize statistical power, ADOS-2 scores at 18–24 months were used for children who did not have clinical data at 36 months.</p> <p>· Column U, CNTNAP2rs745AT: genotypes of the rs745 SNP of the CNTNAP2 gene, 0 = AA; 1 = AT; 2 = TT.</p> <p>· Column V, CNTNAP2rs102TC: genotypes of the rs745 SNP of the CNTNAP2 gene, 0 = TT; 1 = TC; 2 = CC.</p> <p>· Column W, Time_point: 1 = EEG data acquired at 6 months old (T6); 2 = EEG data acquired at 12 months old (T12).</p> <p>· Column Y-AI, PSD_frequencyband_cluster: power values computed for each frequency band (delta, theta, low_alpha, high_alpha, beta and gamma) in two clusters of frontal electrodes (frontal left, frontal right). See Piazza et al., 2023 for more details.</p> <p>· Column AJ-AK, DFA _cluster: exponent of the Detrend Fluctiation Analysis (DFA) computed in two clusters of frontal electrodes (frontal left, frontal right, Piazza et al., 2023). The amplitude envelope of the signal was extracted using the Hilbert transform and used for the DFA, implemented as described in Peng et al. (1995), obtaining the DFA exponent.</p> <p>· Column AL-AQ, MSE values. MSE was computed using the mMSE toolbox (Kosciessa et al., 2020) for scale 1 to 30. Successively, the MSE computed for scale 1 to 10, 11 to 20 and 21 to 30 were averaged for each analyzed channel, thus obtaining three MSE values related respectively to low, medium and high scales. Again two clusters of frontal electrodes have been analyzed (frontal left, frontal right, Piazza et al., 2023).</p> <p>· Column AR-BC, Functional Connectivity metrics (magnitude squared coherence, MSC, and phase lag index, PLI) computed for all frequency bands (delta, theta, low_alpha, high_alpha, beta and gamma). See Falivene et al., 2024 for the details.</p> <p>· Column BD-BG, offset (off) and exponent (exp) of the spectrum aperiodic component computed for two clusters of frontal electrodes (frontal left, frontal right, Piazza et a., 2023). The specparam algorithm version 1.1.0 of Donoghue et al. 2020 was employed for the calculation of aperiodic exponent and offset.</p> <p> </p> <p>If you use this dataset please cite the following manucripts:</p> <p>Piazza, C., Dondena, C., Riboldi, E. M., Riva, V., & Cantiani, C. (2023). Baseline EEG in the first year of life: Preliminary insights into the development of autism spectrum disorder and language impairments. <em>Iscience</em>, <em>26</em>(7).</p> <p>Falivene, A.; Cantiani, C.; Dondena, C.; Riboldi, E.M.; Riva, V.; Piazza, C. EEG Functional Connectivity Analysis for the Study of the Brain Maturation in the First Year of Life. Sensors 2024, 24, 4979. </p> <p> </p> <p>This research was funded by the Italian Ministry of Health (Ricerca Finalizzata SG-2019-12369935; Ricerca corrente; "5 per mille" funds for biomedical research).</p>
Toxicological evaluation of Vanillin Flavor in E-Liquid Aerosols on Endothelial Cell Function: Findings from the Replica Project
<p><span>Background: There are challenges that require collaboration among researchers to ensure that tobacco harm reduction strategies are evidence-based. One key challenge is evaluating the safety of flavors used in electronic cigarettes (e-cigarettes). While many flavorings are approved as food additives or deemed “generally recognized as safe” (GRAS) for ingestion, this does not guarantee their safety when inhaled. In this context, the international research group Replica replicated a study conducted by Fetterman and colleagues in 2018, investigating the effects of aerosolized vanillin - one of the most popular flavors in vaping - on vascular endothelium when vaporized by an electronic cigarette. Methods: We used Aspire Zelos 3 e-cigarette and prepared e-liquids containing propylene glycol, vegetable glycerin and vanillin. The e-liquids were vaporized under two settings - regular (1 ohm coil using wattage control mode at 14 watts) and sub-ohm (0.3 ohm coil using temperature control mode at 200 °C) – using a vaping machine, following the standardized puffing regime, ISO20768:2018. The vapor was then collected into a trapping solution to prepare aqueous extracts for the treatment of human aortic endothelial cells. We evaluated cytotoxicity, oxidative stress, nitric oxide bioavailability, and inflammation addressing some gaps reported in the original study. Results: We observed some harmful effects, mostly attributable to ethanol, used to dilute vanillin in the original work by Fetterman, but no harmful effects on cell viability, their ability to produce nitric oxide, or oxidative stress from vanillin. Furthermore, no pro-inflammatory effects of vanillin were observed in terms of ICAM-1 and IL-6 gene expression. Conclusions: Our results confirm the endothelial cell dysfunction observed in the original paper, but clarify that these effects are mainly attributable to ethanol and not to vaporized vanillin. These findings suggest that vanillin could be a safer flavoring agent for e-cigarette, without causing adverse effects on the cardiovascular system.</span></p>
Test Project
<p>sample description</p>
CRDM@NCRC Test Data Set Wizard and Witch vaccination project against dragon pox
<p>This is a dummy data set to test restricted access. We created test data about vaccination of wizards and witches against dragon pox. The data set contains over 60 entries of the vaccination.</p>
Datasets for HC's AI4ER MRes Project
<p>This repository contains all of the datasets required to replicate the results within the report. Note that these datasets are not the most up to date available. For current datasets, visit the source websites provided within the report. The required file structure for compatibility with the preprocessing and modelling code is given within the GitHub repo <a href="https://github.com/Hamish-Cam/HuntingPressure">here</a>. </p> <p>The following datasets are provided: </p> <ol> <li><a href="https://zenodo.org/api/files/8d6cb14a-c5c4-4f14-8bfb-c57030755e33/ebd_sampling_relApr-2022.txt?versionId=e763a184-5dca-45e1-8822-1c451f8f1065">ebd_sampling_relApr-2022.txt</a> - eBird Sampling Data (April 2022)</li> <li><a href="https://zenodo.org/api/files/8d6cb14a-c5c4-4f14-8bfb-c57030755e33/ebd_checklists.zip">ebd_checklists.zip</a> - eBird Species Checklist Data x20 (April 2022)</li> <li><a href="https://zenodo.org/api/files/8d6cb14a-c5c4-4f14-8bfb-c57030755e33/BOTW.gdb.zip">BOTW.gdb.zip</a> - Species Range Data (2021)</li> <li><a href="https://zenodo.org/api/files/8d6cb14a-c5c4-4f14-8bfb-c57030755e33/modis.zip">modis.zip</a> - MODIS Landcover Classification Tiles (2001-2020)</li> <li><a href="https://zenodo.org/api/files/8d6cb14a-c5c4-4f14-8bfb-c57030755e33/elevation_1KMmd_GMTEDmd.tif?versionId=f7f7f50a-1e6f-4d21-b2ae-8b853a1967c7">elevation_1KMmd_GMTEDmd.tif</a> - Elevation Data (2010)</li> <li><a href="https://zenodo.org/api/files/8d6cb14a-c5c4-4f14-8bfb-c57030755e33/Grid_mammal_amph_threat_predictions.dbf?versionId=8210b55b-1cbc-4dc4-a20a-3d2b71296790">Grid_mammal_amph_threat_predictions.*</a> - Harfoot et. al Threat Maps (2021)</li> <li><a href="https://zenodo.org/api/files/8d6cb14a-c5c4-4f14-8bfb-c57030755e33/2015_accessibility_to_cities_v1.0.tif?versionId=20e3ba2e-178e-4e36-903f-ccc057a53f0b">2015_accessibility_to_cities_v1.0.tif</a> - Accessibility to Cities (2015)</li> </ol>
Project Data: Industrial Innovation in Transition - coded interview data
<p>Industrial innovation has changed fundamentally over the last ten years. Companies have widely adopted new tools such as open innovation, innovation networks and ecosystems, systemic innovations, public/private partnerships, crowd sourcing, social media, and demand based innovations. These new practices have improved the innovation<br> capabilities of companies and brought new challenges to the traditional innovation policy instruments. This project<br> examined widely companies’ current use of new innovation practices around Europe and the good practice experience of their use. It also evaluated existing innovation policy portfolios at national and European levels, and analyzed the differences between innovation processes and management practices in different industrial sectors. With 694 interviews of company representatives the project covered five key industrial sectors in 11 EU Member States.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.