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6,782 results for “project”
Hydro-climatic projections_Sweden
<p>The ensemble set consists of 16 available hydro-climatic (river flow (m3s-1), precipitation (mm), and temperature (oC)) projections for Sweden. It includes 3 different emission scenarios (A1B, B1 and A2), 5 GCMs (ECHAM5, ARPEGE, CCSM3, HadCM3Q and BCM), 6 RCMs (SMHI-RCA3, CNRM-ALADIN, KNMI-RACMO, MPI-REMO, HC-HadRM3, and DMI-HIRHAM), and two spatial resolutions (50 and 25 km). The results are provided for 1007 Swedish sub basins on a monthly, seasonal and annual scale. Different statistics (mean, 10th and 90th percentiles) are calculated and provided. The HBV model [Lindström, G., B. Johansson, M. Persson, M. Gardelin, and S. Bergström (1997), Development and test of the distributed HBV-96 hydrological model, <em>J. Hydrol.</em>, <em>201</em>, 272–288] was used to provide the hydrological projections. The results for the whole country of Sweden have been published in: Pechlivanidis, I.G., Gupta, H., and Bosshard, T. 2018. An information theory approach to identifying a representative subset of hydro-climatic simulations for impact modeling studies, Water Resources Research.</p>
NEWBITS – Project video clip
<p>Animated explanatory video clip describing the NEWBITS project, its objectives and expected impacts.</p>
OMS project for Kriging interpolation of precipitation and temperature in Isarco River Valley
<p>The OMS project contains the simulations, jar files of the components, the inputs and the ouputs used in the Chapter 6 of the thesis " A flexible approach to the estimation of water budgets and its connection to the travel time theory ", Bancheri (2017) and in the article "The design and implementatation of Kriging models in the Object Modelling System v.3.", Bancheri et al. 2018. The project allows the Kriging interpolation of the precipitation and temperature, using data from Isarco River Valley. </p>
MERLIN H2020 Project: Promotional Video
<p>MERLIN: Helping today's researchers create tomorrow's enterprises</p> <p>Many breakthrough discoveries fruit of research programmes often fail to realize their potential as an innovation, that is, bringing to market an idea or process.</p> <p>MERLIN will support market-oriented researchers, SMEs and startups across Europe, to consider the full potential of their research and to shape ideas and outputs into innovations to be ready for market validation and commercialisation, by using modern needs-first, market-led methodologies and offering free training workshops and meetup in 7 European countries.</p> <p>Check our website to stay updated <a href="https://www.youtube.com/redirect?event=video_description&v=lqeYWrm4swM&redir_token=Pi1gRtpcbFbP_6TasJfwceKrEYp8MTUyNjQ4NjI2MEAxNTI2Mzk5ODYw&q=https%3A%2F%2Fmerlin-ict.eu">https://merlin-ict.eu</a> We are ready for you at MERLIN.</p> <p>MERLIN has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 780460</p>
Dataset used to perform Focus Groups in Spain (related to m-RESIST project)
<p>Dataset used to perform the following manuscript: </p> <p>- Huerta-Ramos E, Marcó-García S, Escobar-Villegas MS, Rubio-Abadal E, Ochoa S, Grasa Bello EM, Alonso Solís A, Rabella M, Berdun J, Hospedales M, M-Resist G, Corripio I, Usall J. <em>m-RE</em><em>SIST, a complete m-Health solution for patients with treatment resistant schizophrenia: a qualitative study of user needs and acceptability in the Barcelona metropolitan area.</em> Actas Españolas de Psiquiatría. 2017 Nov;45 (6):277-89. Epub 2017 Nov 1.</p> <p>The sample of this article was the data obtained from 14 out-patients with TRS, 9 family members with the role of main carer, and 13 mental health professionals, from March to June (2015). The activities related to the manuscript were the following: 5 discussion groups (2 with professionals, 2 with patients, and 1 with family members) were performed in Parc Sanitari Sant Joan de Déu (PSSJD), representing Barcelona metropolitan area. In addition, 8 individual interviews were held (4 with patients and 4 with family members). All recorded data was analyzed following discourse analysis as the analytical approach. </p>
DarkSkies Project (Cities At Night - 2014)
<p>Results of the DarkSkies application hosted in Crowdcrafting (http://www.crowdcrafting.org) in the year 2014.</p>
Tasks run in DarkSkies Project (Cities At Night - 2016)
<p>Results of the DarkSkies application hosted in Crowdcrafting (http://www.crowdcrafting.org) in the year 2016.</p>
Tasks run in DarkSkies Project (Cities At Night - 2015)
<p>Results of the DarkSkies application hosted in Crowdcrafting (http://www.crowdcrafting.org) in the year 2015.</p>
DarkSkies Projects Tasks (Cities At Night)
<p>List of available tasks in project DarkSkies hosted in the Crowdcrafting platform (http://www.crowdcrafting.org).</p> <p>Time frame: 27/04/2014 - 22/12/2015</p>
Ripple Complex Experiments data set at CIEM large scale wave flume within Hydralab + project.
<p>The RIPCOM experiments (RIPple COMplex experiments) are presented in order to study the ripple growth conditions on large wave flume tests under fine unimodal, coarse unimodal and mixed sands conditions. The main objectives of the experiments is to improve and understand the protocols to perform mixed sediment experiments within the ripple regime and use/improve the equipment developed at Task 9.1 of the COMPLEX Joint Research Activity within Hydralab+. The experiments were carried out in the large scale wave flume CIEM at Universitat Politècnica de Catalunya (UPC), Barcelona.</p> <p>The experimental plan is divided in three steps:</p> <p>1. Find the optimum wave conditions that ensure ripples (based on measured velocities and previous literature studies) on the study area. Test the targeted waves with unimodal fine sediment (d 50 =0.250 mm) and measure the obtained ripples under the tested conditions. From the obtained measurements, the waves to be used on the next two steps are selected in order to fix the best conditions to produce ripples within the experimental constrains.</p> <p>2. The 13 upper cm of the fine sediment is removed and replaced by the coarser sediment (d 50 =0.545 mm). Once that is done the selected waves to be tested are reproduced and the bottom bedforms are measured.</p> <p>3. Mix both sediments fine and coarser sand homogeneously in order to repeat the selected wave conditions and measure the ripples growth and evolution under mixed sediment conditions.</p> <p>Due to its size, the data set can not be placed on this repository and will be provided on demand. Please contact with the authors or with the data manager of the CIEM installation.</p>
Ecosystem-Level Determinants of Sustained Activity in Open-Source Projects: A Case Study of the PyPI Ecosystem
<pre><em>Replication pack, FSE2018 submission #164: </em><em>------------------------------------------ </em></pre> <pre><strong>**</strong>Working title:<strong>** </strong>Ecosystem-Level Factors Affecting the Survival of Open-Source Projects: A Case Study of the PyPI Ecosystem <strong>**</strong>Note:<strong>** </strong>link to data artifacts is already included in the paper. Link to the code will be included in the Camera Ready version as well. <em>Content description </em><em>=================== </em> <strong>- **</strong>ghd-0.1.0.zip<strong>** </strong>- the code archive. This code produces the dataset files described below <strong>- **</strong>settings.py<strong>** </strong>- settings template for the code archive. <strong>- **</strong>dataset_minimal_Jan_2018.zip<strong>** </strong>- the minimally sufficient version of the dataset. This dataset only includes stats aggregated by the ecosystem (PyPI) <strong>- **</strong>dataset_full_Jan_2018.tgz<strong>** </strong>- full version of the dataset, including project-level statistics. It is ~34Gb unpacked. This dataset still doesn't include PyPI packages themselves, which take around 2TB. <strong>- **</strong>build_model.r, helpers.r<strong>** </strong>- R files to process the survival data (`survival_data.csv` in <strong>**</strong>dataset_minimal_Jan_2018.zip<strong>**</strong>, `common.cache/survival_data.pypi_2008_2017-12_6.csv` in <strong>**</strong>dataset_full_Jan_2018.tgz<strong>**</strong>) <strong>- **</strong>Interview protocol.pdf<strong>** </strong>- approximate protocol used for semistructured interviews. <strong>- </strong>LICENSE - text of GPL v3, under which this dataset is published <strong>- </strong>INSTALL.md - replication guide (~2 pages)</pre> <pre><em>Replication guide </em><em>================= </em> <em>Step 0 - prerequisites </em><em>---------------------- </em> <strong>- </strong>Unix-compatible OS (Linux or OS X) <strong>- </strong>Python interpreter (2.7 was used; Python 3 compatibility is highly likely) <strong>- </strong>R 3.4 or higher (3.4.4 was used, 3.2 is known to be incompatible) Depending on detalization level (see Step 2 for more details): <strong>- </strong>up to 2Tb of disk space (see Step 2 detalization levels) <strong>- </strong>at least 16Gb of RAM (64 preferable) <strong>- </strong>few hours to few month of processing time <em>Step 1 - software </em><em>---------------- </em> <strong>- </strong>unpack <strong>**</strong>ghd-0.1.0.zip<strong>**</strong>, or clone from gitlab: git clone https://gitlab.com/user2589/ghd.git git checkout 0.1.0 `cd` into the extracted folder. All commands below assume it as a current directory. <strong>- </strong>copy `settings.py` into the extracted folder. Edit the file: <strong> * </strong>set `DATASET_PATH` to some newly created folder path <strong> * </strong>add at least one GitHub API token to `SCRAPER_GITHUB_API_TOKENS` <strong>- </strong>install docker. For Ubuntu Linux, the command is `sudo apt-get install docker-compose` <strong>- </strong>install libarchive and headers: `sudo apt-get install libarchive-dev` <strong>- </strong>(optional) to replicate on NPM, install yajl: `sudo apt-get install yajl-tools` Without this dependency, you might get an error on the next step, but it's safe to ignore. <strong>- </strong>install Python libraries: `pip install --user -r requirements.txt` . <strong>- </strong>disable all APIs except GitHub (Bitbucket and Gitlab support were not yet implemented when this study was in progress): edit `scraper/init.py`, comment out everything except GitHub support in `PROVIDERS`. <em>Step 2 - obtaining the dataset </em><em>----------------------------- </em> The ultimate goal of this step is to get output of the Python function `common.utils.survival_data()` and save it into a CSV file: # copy and paste into a Python console from common import utils survival_data = utils.survival_data('pypi', '2008', smoothing=6) survival_data.to_csv('survival_data.csv') Since full replication will take several months, here are some ways to speedup the process: <em>####Option 2.a, difficulty level: easiest </em> Just use the precomputed data. Step 1 is not necessary under this scenario. <strong>- </strong>extract <strong>**</strong>dataset_minimal_Jan_2018.zip<strong>** </strong><strong>- </strong>get `survival_data.csv`, go to the next step <em>####Option 2.b, difficulty level: easy </em> Use precomputed longitudinal feature values to build the final table. The whole process will take 15..30 minutes. <strong>- </strong>create a folder `<DATASET_PATH>/common.cache`, where `<DATASET_PATH>` is the value of the variable `DATASET_PATH` in `settings.py` <strong>- </strong>extract <strong>**</strong>dataset_minimal_Jan_2018<strong>** </strong>to the newly created folder <strong>- </strong>rename files: mv backporting.csv monthly_data.pypi_backporting.csv mv cc_degree.csv monthly_data.pypi_cc_degree.csv mv commercial.csv monthly_data.pypi_commercial.csv mv commits.csv monthly_data.pypi_commits.csv mv contributors.csv monthly_data.pypi_contributors.csv mv dc_katz.csv monthly_data.pypi_dc_katz.csv mv downstreams.csv monthly_data.pypi_downstreams.csv mv d_upstreams.csv monthly_data.pypi_d_upstreams.csv mv github_user_info.csv user_info.pypi.csv mv issues.csv monthly_data.pypi_issues.csv mv non_dev_issues.csv monthly_data.pypi_non_dev_issues.csv mv non_dev_submitters.csv monthly_data.pypi_non_dev_submitters mv package_urls.csv package_urls.pypi.csv mv q90.csv monthly_data.pypi_q90.csv # raw_dependencies.csv is not required # raw_packages_info.csv is not required # Feel free to read README.md for more details about the data mv submitters.csv monthly_data.pypi_submitters.csv # In this scenario we'll generate a new survival_data.csv mv university.csv monthly_data.pypi_university.csv mv upstreams.csv monthly_data.pypi_upstreams.csv <strong>- </strong>edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` Then, use the Python code above to obtain `survival_data.csv`. <em>####Option 2.c, difficulty level: medium </em> Use pre-downloaded raw data to build longitudinal feature values, and then the dataset. Despite most of the data is cached, some functions will pull up updates which might take anywhere from days to couple weeks to run. <strong>- </strong>Download <strong>**</strong>dataset_full_Jan_2018.tgz<strong>** </strong>(5.4Gb compressed, 34Gb unpacked). <strong>- </strong>edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` <strong>- </strong>extract the content of this archive into `<DATASET_PATH>`. <strong>- </strong>clean up `<DATASET_PATH>/common.cache` (otherwise you'll get Step 2.a. You can reproduce Step 2.b by deleting only `survival_data.pypi_2008_2017-12_6.csv`) Run the Python code above to obtain `survival_data.csv`. <em>####Option 2.d, difficulty level: hard </em> Build the dataset from scratch. Although most of the processing is parallelized, it will take at least couple months on a reasonably powerful server (32 cores, 512G of RAM, 2Tb+ of HDD space in our setup). <strong>- </strong>ensure the `<DATASET_PATH>` is empty <strong>- </strong>add more GitHub tokens (borrow from your coworkers) to `settings.py`. Run the Python code above to obtain `survival_data.csv`. <em>Step 3 - run the regression </em><em>--------------------------- </em> install R libraries: install.packages(c("htmlTable", "OIsurv", "survival", "car", "survminer", "ggplot2", "sqldf", "pscl", "texreg", "xtable")) Use `build_model.r` (e.g. in RStudio) and produced `survival_data.csv` to build the regressions used in the paper. This process takes at least 16Gb of RAM and takes few hours to run due to the gigantic size of the dataset. </pre>
ERASER project: experimental results
<p>This package contains the experimental results obtained in the ERASER project. ERASER stands for Experimenting with Real Application-specific QoS Guarantees in a Large-scale RINA Demonstrator and is a project funded by the 3rd Open Call of the Fed4FIRE+ project (https://www.fed4fire.eu).</p> <p>Experiments have been conducted over a 5G metro/regional RINA network scenario spanning from the end-user terminal until the server where applications run in the datacentre has been defined. Over the last years, the clean-slate Recursive InterNetwork Architecture (RINA) (http://pouzinsociety.org) has attracted significant interest from the Future Internet research community, seen as a viable solution to solve the well-known limitations of the current TCP/IP-based Internet, e.g., in terms of routing scalability, application-specific Quality of Service (QoS) delivery or built-in security.</p> <p>The ERASER project targeted experimental evaluations of the QoS guarantees that RINA can deliver to heterogeneous applications. These experiments have been conducted over the Fed4FIRE+ Virtual Wall experimentation facility hosted and operated by imec in Ghent (Belgium). </p> <p>First experiments in ERASER have contemplated a 10-node small-scale RINA scenario, aiming to integrate all RINA-related open source implementations and tools needed for the entire experiments and validate their operation to meet the ERASER objectives. Two alternative QTA-Mux (i.e., the policy responsible for the RINA QoS support) deployment scenarios have been considered. This has been followed by a definition of the QoS classes (referred to as QoS Cubes in RINA) that QTA-Mux has to enforce, with their relative requirements in terms of losses and delay, as well as the characteristics of the synthetic application flows that will be injected in the network.</p> <p>Second experiments consider a large-scale metro/regional RINA network scenario composed of 37 nodes, eventually, where measurements of delay and packet losses experienced by synthetic traffic flows end-to-end have been obtained for the two considered QTA-Mux deployment scenarios and under different load conditions (links operating at 70, 80 and 90% of their effective capacity). </p> <p>The obtained results highlight the need for configuring QTA-Mux at both Service Provider Network and Metro-Regional network layers in order to provide effective QoS differentiation in terms of both delay and losses, particularly under high network congestion (link loads of 80% and 90%). Conversely, configuring QTA-Mux only at the Service Provider Network layer falls short of delivering adequate QoS differentiation, as best-effort packet treatment at the underlying Metro-Regional network layer deteriorates it, with all synthetic application flow types experiencing similar delay and packet loss outcomes end-to-end.</p>
Ortholog data from the tuatara genome project
<p>This record contains orthology predictions based on the Maker gene annotation<br> of the tuatara (Sphenodon punctatus) and a set of 25 other species, using the<br> Ensembl methodology.</p> <p>See http://doi.org/10.5281/zenodo.1489354 for the Maker annotation in GFF<br> format.</p> <p>The files all_trees.emf.gz and all_homologies.tsv.gz contain the phylogenetic<br> trees (all_trees.emf.gz, in the EMF alignment format) and the derived pairwise<br> orthologies and paralogies (all_homologies.tsv.gz, in tabular format).</p> <p>From the phylogenetic trees, sets of 1-to-1 orthologues across all 26 species<br> were extracted (pure_one2one_orthologies.txt). Sets of orthologues that span<br> all the species but include paralogues were reduced to 1 copy per species<br> using gene order conservation and sequence similarity. This extra dataset is<br> available in promoted_one2one_orthologies.txt</p> <p>The main Ensembl entry point for tuatara is:<br> http://www.ensembl.org/Sphenodon_punctatus/</p> <p>This work is supported by Ngatiwai iwi, Allan Wilson Centre, University of<br> Otago, New Zealand Genomics Limited, Illumina, National eScience<br> Infrastructure (NeSI NZ).</p>
Dataset of the publication "Cross-sectional study of prevalence of dementia, behavioural symptoms, mobility, pain and other health parameters in nursing homes in Austria and the Czech Republic: results from the DEMDATA project"
<p>Dataset of the publication Stefanie R. Auer , Margit Höfler, Elisabeth Linsmayer, Anna Beránková, Doris Prieschl, Paulina Ratajczak,<br> Michal Šteffl and Iva Holmerová (2018) "Cross-sectional study of prevalence of dementia, behavioural symptoms, mobility, pain and other health parameters in nursing homes in Austria and the Czech Republic: results from the DEMDATA project"</p>
European Market Place - FLEXICIENCY project
<p>The FLEXICIENCY project aims to demonstrate how, inside the European Union, the implementation of novel services dedicated to energy market can be boosted by the introduction of a Market Place that allows the exchange of data, services and tools among the operators. The video gives a general overview of the project and the potential benefits of its implementation.</p>
Guidelines for Data Management Plan implementation of One Health EJP projects: Webinar held on the 19th December 2018
<p>Webinar held on the 19th December 2018 to introduce Data Management Plan to scientists involved in the research and integrative projects of One Health European Joint Program.</p>
Data, plotting scripts, and figures for "A Projective Method for Solving the Single-Group Space-Time Neutron Kinetics Equations with Precursor Advection"
<p>Contains all the files necessary for figure reproduction.</p>
PROGRAMS project. Axis test on FIDIA DL155 on 2019-02-28
<p>Current and position data were recorded during linear axis movements to create a reference for future comparison.</p>
PROGRAMS project. Axis test on FIDIA Cortini L300 on 2019-05-21
<p>Current and position data were recorded during linear axis movements to create a reference for future comparison.</p>
Pelvic arcade and vertebral structure of the second chief specimen of Tyrannosaurus rex, Amer. Mus. 5027, discovered in 1908. An orthogonal projection executed on a very large scale and reproduced one-twelfth natural size. C 1-C 10 cervical series, D 1-D 13 dorsal or thoracic series, S 1-S 5 sacral series, Cd 1- Cd 53 caudal series. The caudals actually preserved are shaded; those drawn in outline are conjectural and restored. The total number of caudals is conjectural. in Skeletal Adaptations of Ornitholestes, Struthiomimus, Tyrannosaurus
Pelvic arcade and vertebral structure of the second chief specimen of Tyrannosaurus rex, Amer. Mus. 5027, discovered in 1908. An orthogonal projection executed on a very large scale and reproduced one-twelfth natural size. C 1-C 10 cervical series, D 1-D 13 dorsal or thoracic series, S 1-S 5 sacral series, Cd 1- Cd 53 caudal series. The caudals actually preserved are shaded; those drawn in outline are conjectural and restored. The total number of caudals is conjectural.
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.