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6,782 results for “project”
Development of a mass production method for the smut fungus Doassansia niesslii , a potential biological control agent of the invasive weed Butomus umbellatus (IC_MScEA_Final project data)
<p>Data of my MSc final project (MSc Ecological Applications, Imperial College London). Student ID: 02175995.</p>
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-08]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier / NGC / IC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS), Benoît Vandame (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p> <p> </p>
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-07]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier / NGC / IC catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS), Benoît Vandame (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com/</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu/</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p>
Raw experimental data obtained during the project NCN Miniatura 4 DEC-2020/04/X/ST5/00539 for the Cu/Ge system
<p>Raw experimental data obtained during the project "An attempt to synthesize new metal phases of the hcp type in the process of temperature-induced self-organization in the nanoscale" (In Polish: "Próba syntezy nowych faz metali typu hcp w procesie samoorganizacji indukowanej temperaturowo w skali nano") financed by Polish National Science Centre (NCN) under the grant Miniatura 4 DEC-2020/04/X/ST5/00539 for the Cu/Ge system.</p> <p>Data collected using SEM, AFM, SEM EDX, SEM EBSD and STEM microscopy for the study of the temperature-induced self-assembly process on the germanium (001) surface under the ultrahigh vacuum (UHV) conditions.</p> <p>For the Cu/Ge system, 30 ML Cu (atomic monolayers) were deposited. The self-organization process was carried out at the temperatures of 200C, 400C, 500C, 600C, 650C, 700C.</p> <p><br> The experimental data are sorted according to the temperature of the self-organization process in the appropriate catalogs and according to the measurement technique used in the sub-catalogs.</p> <p> </p> <p>Data for the Cu/Ge system are in the file "CuGe.zip"</p> <p> </p> <p>The obtained experimental data can be read using free software.</p> <p> </p> <p>AFM data - Gwyddion Software http://gwyddion.net/</p> <p>SEM data - Imagej / FIJI software https://fiji.sc/</p> <p>SEM EDX and STEM data - HyperSpy software https://hyperspy.org/</p> <p>SEM EBSD data - Kikuchipy software https://kikuchipy.org/</p>
Raw experimental data obtained during the project NCN Miniatura 4 DEC-2020/04/X/ST5/00539 for the Ag/Ge system
<p>Raw experimental data obtained during the project "An attempt to synthesize new metal phases of the hcp type in the process of temperature-induced self-organization in the nanoscale" (In Polish: "Próba syntezy nowych faz metali typu hcp w procesie samoorganizacji indukowanej temperaturowo w skali nano") financed by Polish National Science Centre (NCN) under the grant Miniatura 4 DEC-2020/04/X/ST5/00539 for the Ag/Ge system.</p> <p>Data collected using SEM, AFM, SEM EDX, SEM EBSD and STEM microscopy for the study of the temperature-induced self-assembly process on the germanium (001) surface under the ultrahigh vacuum (UHV) conditions.</p> <p>For the Ag/Ge system, 30 ML Cu (atomic monolayers) were deposited. The self-organization process was carried out at the temperatures of 500C, 600C, 650C, 700C.</p> <p><br> The experimental data are sorted according to the temperature of the self-organization process in the appropriate catalogs and according to the measurement technique used in the sub-catalogs.</p> <p> </p> <p>Data for the Ag/Ge system are in the file "AgGe.zip"</p> <p> </p> <p>The obtained experimental data can be read using free software.</p> <p> </p> <p>AFM data - Gwyddion Software http://gwyddion.net/</p> <p>SEM data - Imagej / FIJI software https://fiji.sc/</p> <p>SEM EDX and STEM data - HyperSpy software https://hyperspy.org/</p> <p>SEM EBSD data - Kikuchipy software https://kikuchipy.org/</p>
Replication Package for Paper "How Early Participation Determines Long-Term Sustained Activity in GitHub Projects"
<p>This replication package can be used for replicating results in the paper. It contains 1) a dataset of 290,255 repositories; and 2) Python scripts for training and interpreting models. </p> <p>We recommend manually setup the required environment in a commodity Linux machine with at least 1 CPU Core, 8GB Memory and 100GB empty storage space. We conduct development and execute all our experiments on a Ubuntu 20.04 server with two Intel Xeon Gold CPUs, 320GB memory, and 36TB RAID 5 Storage.</p> <p>We use GHTorrent to restore historical states of 290,255 repositories with more than 57 commits, 4 PRs, 1 issue, 1 fork and 2 stars. The raw data of repositories are stored in `Replication Package/data/prodata.pkl`, and the contribution of features resulting from LIME model is stored in `Replication Package/data/limeres_m2_k1.pkl`. We sort items by the order in `Replication Package/data/randind.npy`, which can be used to reproduce the same results as in the paper. <br> `Replication Package/data/X_test_m2_k1.pkl` and `Replication Package/data/y_test_m2_k1.pkl` store the test dataset for the LIME model. You can run `Replication Package/fitdata.py` to get the results in Table III and IV, run `Replication Package/draw_compare_variable.py` to get Figure 2 and run `Replication Package/allvari_statistics.py` to get Table II. In `Replication Package/Variable_comparison_with_different_parameter.pdf`, we show the LIME results under different parameters. In `Replication Package/sample_pros.csv`, we also provide the list of randomly selected repositories in Section III.B.</p>
Replication Package for Paper "How Early Participation Determines Long-Term Sustained Activity in GitHub Projects"
<p>This replication package can be used for replicating results in the paper. It contains 1) a dataset of 290,255 repositories; and 2) Python scripts for training and interpreting models. </p> <p>We recommend manually setup the required environment in a commodity Linux machine with at least 1 CPU Core, 8GB Memory and 100GB empty storage space. We conduct development and execute all our experiments on a Ubuntu 20.04 server with two Intel Xeon Gold CPUs, 320GB memory, and 36TB RAID 5 Storage.</p> <p>We use GHTorrent to restore historical states of 290,255 repositories with more than 57 commits, 4 PRs, 1 issue, 1 fork and 2 stars. The raw data of repositories are stored in `Replication Package/data/prodata.pkl`, and the contribution of features resulting from LIME model is stored in `Replication Package/data/limeres_m2_k1.pkl`. We sort items by the order in `Replication Package/data/randind.npy`, which can be used to reproduce the same results as in the paper. <br> `Replication Package/data/X_test_m2_k1.pkl` and `Replication Package/data/y_test_m2_k1.pkl` store the test dataset for the LIME model. You can run `Replication Package/fitdata.py` to get the results in Table III and IV, run `Replication Package/draw_compare_variable.py` to get Figure 2 and run `Replication Package/allvari_statistics.py` to get Table II. In `Replication Package/Variable_comparison_with_different_parameter.pdf`, we show the LIME results under different parameters. In `Replication Package/sample_pros.csv`, we also provide the list of randomly selected repositories in Section III.B.</p>
Dataset of FP7 projects related to remote sensing
<p>A dataset for R data analysis and article.</p>
Dataset of H2020 projects related to remote sensing
<p>dataset for R data analysis and article.</p>
Plastic Litter Project 2021 dataset
<p>The dataset contains Sentinel-2 multispectral, UAS RGB and UAS hyperspectral data, along with the corresponding orthophoto maps and ancillary data, acquired during the PLP2021 data acquisition campaign, using artificial floating targets. The field campaign took place in the Gulf of Gera, in the Island of Lesvos, in Greece, by the Marine Remote Sensing Group from the University of the Aegean's department of Marine Sciences from June to October 2021. The campaign was funded by the Discovery Element of the European Space Agency’s Basic Activities (ESA Contract No. 4000131040/20/NL/GLC).</p> <p>Data description:</p> <p>- Each folder corresponds to a specific acquisition designated by date in the Sentinel-2 product format.</p> <p>- Folder contents:<br> -- Sentinel-2 Level-1C product.<br> -- ACOLITE atmospherically corrected product (.netcdf format).<br> -- Orthophoto map of the deployment area where available (.tiff format).<br> -- UAS RGB or field image file showing target configuration for given date where available (.jpeg format).</p> <p>- The ancillary_data_log.pdf contains a table with ancillary data measurements taken during each Sentinel-2 acquisition.</p>
MILAN project: raw images of deep sky objects captured with a Stellina observation station -- [2022-09]
<p>During the MILAN research project (MachIne Learning for AstroNomy), the research team uses Stellina observation stations to collect raw images of deep sky objects. </p> <p>The Stellina observation station has an apochromatic ED doublet with an aperture of 80 mm and a focal length of 400 mm - focal ratio of f/5. It is equipped with a Sony IMX 178 CMOS sensor with a resolution of 6.4 million pixels (3096 x 2080 pixels). An anti-pollution filter is placed in front of the camera sensor. The station also has a fully automated alt-azimuth mount: setup, object tracking and focusing are also automatic. Stellina also has an integrated field rotator that adapts to the target.</p> <p>Thus, the research team built a dataset that faithfully represents what can be obtained during classical Electronically Assisted Astronomy sessions in the Luxembourg Greater Region:<br> - targets: representative set of deep sky objects from the Messier / NGC / IC / Barnard / Sharpless catalogs during the period.<br> - type of targets: nebulaes, planetary nebula, galaxies, globular clusters, open clusters<br> - for each observation session: sub-frames of 10 seconds exposure time and total cumulative duration of ~15/30 minutes (depending of the target) -- bad sub-frames were not filtered.</p> <p>The dataset is composed of ZIP files -- and each zip file contain raw images in FITS format (Flexible Image Transport System): data come directly from the Sony IMX178 sensor of the Stellina observation station (no debayerisation and no post processing).</p> <p>This research was funded by the Luxembourg National Research Fund (FNR), grant reference 15872557.</p> <p>To refer to or to cite this work, please use: "Parisot, O., Hitzelberger, P., Bruneau, P., Krebs, G., Destruel, C., & Vandame, B. (2023). MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope. Data in Brief, 48, 109133."</p> <p> </p> <p>MILAN project team members: Olivier Parisot (LIST), Patrik Hitzelberger (LIST), Pierrick Bruneau (LIST), Gilles Krebs (VAONIS, Christophe Destruel (VAONIS), Benoît Vandame (VAONIS).</p> <p>More information about the MILAN project: https://www.fnr.lu/results-2021-1-bridges-call/.</p> <p>More information about VAONIS instruments: https://vaonis.com</p> <p>More information about Luxembourg of Science and Technology (LIST): https://www.list.lu</p> <p>Data license for files: Attribution-NonCommercial-NoDerivatives 4.0 International</p>
EEG dataset: Engineers' brain activity when CAD modelling from isometric and orthographic projections
<p>The database contains raw and clean EEG data of 20 engineers, captured while they were performing visuospatially-intensive design tasks of CAD modelling based on the technical drawings with orthographic and isometric projections.</p>
MAgPIE model runs outputs: Climate change-driven global land-use system adaptation under CMIP6-based crop model projections
<p>Each folder contains the fulldata.gdx and the configuration files for each MAgPIE run based on the nine crop impact models and 5 gcms used in the paper.</p>
Ensembled projection outputs of Lake Sunapee using multiple General Circulation models and Lake models
<p>This dataset contains the ensemble projection output of Lake Sunapee, NH. The conception and methods of the dataset is explained in the manuscript "Uncertainty in projections of future lake thermal dynamics is differentially driven by global climate models and lake models."</p>
Sand Painting Project Model
Source: Objaverse 1.0 / Sketchfab
Project Bonaire, Amboina: spoor 180
Source: Objaverse 1.0 / Sketchfab
Varun French Project
My french project Source: Objaverse 1.0 / Sketchfab
Sword Project
Sword Assignment Source: Objaverse 1.0 / Sketchfab
3d Past project logo
Source: Objaverse 1.0 / Sketchfab
Supplementary material 1 from: Just A, Gourvil J, Millet J, Boullet V, Milon T, Mandon I, Dutrève B (2015) SIFlore, a dataset of geographical distribution of vascular plants covering five centuries of knowledge in France: Results of a collaborative project coordinated by the Federation of the National Botanical Conservatories. PhytoKeys 56: 47-60. https://doi.org/10.3897/phytokeys.56.5723
Numerical appendix: Explanation note: A shapefile representing the dataset completeness (based on the Jackknife 1, a non-parametric estimator) on a grid of 10 km by 10 km cells. The number of records in each cell was used as an estimator of the sampling effort. The ratio between the observed and estimated richness of species measures the completeness of the inventory in each surveyed cell (Vallet et al. 2012).
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.