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468 results for “Merging”
Merged discrete water-column data from PAL LTER research cruises along the Western Antarctic Peninsula, from 1991 to 2024.
Water samples are collected throughout the water column along the Western Antarctic Peninsula at regular LTER grid stations where CTD casts are preformed and in surface waters at underway stations, where CTD casts are not done, using the ship's flow-through seawater system. This dataset is the compilation of water samples collected at these stations, merged from other PAL-LTER datasets. Data includes water column Chlorophyll and Phaeopigment concentrations; phytoplankton accessory pigments -including other chlorophyll's (e.g. chlorophyll b), xanthophylls, and carotenes; primary production rates; bacterial production; dissolved organic carbon; particulate organic carbon and nitrogen; dissolved inorganic nutrients; dissolved oxygen, and dissolved inorganic carbon and alkalinity. Measurements of phytoplankton Fv/Fm measured using a FIRe (Fluorescence Induction and Relaxation) fluorometer are also included, though caution is urged as FIRe data has not been corrected nor QCed. Conductivity, temperature, depth, and associated data from instruments on the sampling rosette (e.g. PAR, beam transmission) for each sampling depth are also included. Phytoplankton accessory pigment data is unavailable for the LMG10-01 cruise due to instrumentation problems and for the LMG12-01 cruise due to a freezer failure which resulted in the loss of samples. Dissolved oxygen measurements were discontinues after 2012, and thus no data is available from 2013 onwards. Dissolved Organic Carbon data is unavailable after 2012 due to instrumentation problems. There is no Particulate Organic Carbon data for cruise PD94-01.
Merged discrete water-column data from annual PAL LTER field seasons at Palmer Station, Antarctica, from 1991 to 2025.
Water samples are collected throughout the water column at regular stations within the Palmer LTER region at Palmer Station, on Anvers Island, Antarctica. CTD casts are preformed and water collected using go-flo bottles or a rosette. Near-surface waters are also sampled occassionally at the Palmer Station seawater intake (pumphouse). This dataset is the compilation of water samples collected at these stations, merged from other PAL-LTER datasets. Data includes water column Chlorophyll and Phaeopigment concentrations; phytoplankton accessory pigments -including other chlorophyll's (e.g. chlorophyll b), xanthophylls, and carotenes; primary production rates; bacterial production; dissolved organic carbon; particulate organic carbon and nitrogen; dissolved inorganic nutrients; dissolved oxygen, and dissolved inorganic carbon and alkalinity. Measurements of phytoplankton Fv/Fm measured using a FIRe (Fluorescence Induction and Relaxation) fluorometer are also included, though caution is urged as FIRe data has not been corrected nor QCed. Conductivity, temperature, depth, and associated data from instruments on the CTD/sampling rosette (e.g. PAR, beam transmission) for each sampling depth are also included. Phytoplankton accessory pigment data is unavailable for the Palmer 2009-2010 season due to instrumentation problems and for the Palmer 2011-2012 season due to a freezer failure which resulted in the loss of samples. There is a temporary data gap for the Palmer 2015-2016, 2016-2017, 2019-2020, 2020-2021, 2023-2024, and 2024-2025 seasons because those samples have not been analyzed yet. Dissolved oxygen measurements and dissolved inorganic carbon/alkalinity measurements are not available for any season. Dissolved Organic Carbon data is unavailable after 2012 due to instrumentation problems. There is no Particulate Organic Carbon after the 2012 season.
S0 | SUSDAT | Merged NORMAN Suspect List: SusDat
<p>This is the collection associated with list S0 SUSDAT on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a> and <a href="https://www.norman-network.com/nds/susdat/">https://www.norman-network.com/nds/susdat/</a></p> <p>S0 SUSDAT <strong>Merged NORMAN Suspect List: SusDat</strong></p> <p><a href="https://www.norman-network.com/nds/susdat/">Interactive Data table</a> (csv of latest version included in dataset here)</p> <p>UPDATED <strong><em>Jun 3, 2025 to latest version</em></strong>. Compiled by Reza Aalizadeh, University of Athens, including RTI and toxicity values, support by Nikiforos Alygizakis, EI. <em>Work in progress ... please report any issues!</em></p> <p>For an explanation of column headers, please see metadata files (xlsx and csv). For curation notes, see "SusDat_curation_notes.txt". </p>
The extrAIM dataset: A merged satellite-based daily precipitation dataset for the Mediterranean region (including an ensemble of 20 synthetic realisations)
<p><strong>extrAIM </strong>dataset is a <strong>new merged daily precipitation product</strong> (extraim_merged_data.nc) for the Mediterranean region with the following characteristics:</p> <ul> <li><strong>Dataset format:</strong> NetCDF</li> <li><strong>Spatial resolution:</strong> 25 x 25 km</li> <li><strong>Temporal resolution:</strong> 1 day</li> <li><strong>Spatial coverage:</strong> Longitude: from -6.25 to 38.25, Latitude: 27.75 to 49</li> <li><strong>Temporal coverage: </strong>01-01-2007 to 30-09-2021</li> <li><strong>Merging approach: </strong>Two-step merging (classification and regression) <ul> <li><strong>Algorithm: </strong>Random Forest for both classification and regression</li> <li><strong>Training strategy:</strong> Full training strategy</li> </ul> </li> <li><strong>Merged precipitation products: </strong>SM2Rain-ASCAT and GPM Late Run</li> <li><strong>Reference precipitation product:</strong> EMO5</li> <li><strong>Static covariates: </strong>Longitude, Latitude and Elevation, in both classification and regression step <ul> <li><strong>Classification step:</strong> probability dry and probability dry of the 5 neighboring points around the target locations</li> <li><strong>Regression step:</strong> mean, standard deviation and skewness of daily precipitation, of the entire series and non-zero amounts, as well as mean precipitation of the 5 neighboring points around the target locations</li> </ul> </li> </ul> <p>In addition, an <strong>ensemble of 20 synthetic realizations</strong> (equiprobable and bias-adjusted) of the merged dataset is provided (files named: “extraim_realisation_XX.nc”). The synthetic realisations were produced using the extrAIM’s uncertainty-quantification approach and the associated conditional sampling method.</p>
The Spitzer Spectroscopic Data Fusion - Merged Spectroscopic Redshift Catalogs in Spitzer Fields
<p>The Spitzer Spectroscopic Data Fusion merges miscellaneous spectroscopic information available within "popular" extragalactic survey fields.</p> <p>Last Updated on 20 March 2025 - <a href="https://zenodo.org/record/6368347">https://zenodo.org/record/6368347</a> - <a href="https://www.mattiavaccari.net/df/specz">https://www.mattiavaccari.net/df/specz</a></p> <p>Based on the Spitzer Data Fusion Project - <a href="https://doi.org/10.5281/zenodo.7850783">https://doi.org/10.5281/zenodo.7850783</a> - <a href="https://mattiavaccari.net/df">https://mattiavaccari.net/df</a></p> <p>Merged Spec-Z ("specz-merged") catalogs merge miscellaneous spec-z information available within a given field. Different spec-z catalogs available within a given field are merged (using a search radius of 1.0 arcsec), and for sources with multiple spec-z measurements the most reliable one is chosen following the (largely arbitrarily) assumed order of decreasing reliability indicated below for each field. If CAT1,...,CATN spec-z catalogs are available in a given field, Z_1 from CAT1 (i.e. NED) is adopted as "best" redshift (i.e. ZBEST), if available, otherwise Z_2 from CAT_2 is adopted if available, and so on up to Z_N and CAT_N. In using ZBEST it's thus important to bear in mind that this is not necessarily actually the "best" redshift for science purposes, and in particular that the choice of NED as CAT1 is often not ideal. However, Z_1,...,Z_N are included to allow users to define the "best" redshift based on their science needs when multiple redshift estimates are available for a given source. ZFLAG specifies which catalog is providing the ZBEST value according to the CATN number below. ZCLASS is meant to provide further info about the class/quality of the spectroscopic redshift measurement, but for the time being is not populated and is simply a copy of ZFLAG. ZWHERE is an additional binary/bit flag indicating in which of the N catalogs each source was given a spec-z estimate in. ZWHERE will e.g. be 2^0=1 if a redshift if available <em>only</em> from CAT_1, whereas it will be 2^0+2^1=3 if a redshift is available *only* from CAT1 and CAT2,<br>so that a source with a redshift available from all catalogues will have ZWHERE=2^0+...+2^n.</p> <p>See AAAREADME.SPECZ-MERGED within the ZIP archive for further information.</p>
Merging Morphological and Genetic Evidence to assess hybridization in Eurasian Late Pleistocene hominins
<pre>Previous scientific consensus saw human evolution as defined by adaptive differences (behavioural and/or biological) and the emergence of Homo sapiens as the ultimate replacement of non-modern groups by a modern, adaptively more competitive one. However, recent research has shown that the process underlying our origins was considerably more complex. While archaeological and fossil evidence suggests that behavioural complexity may not be confined to the modern human lineage, recent paleogenomic work shows that gene flow between distinct lineages (e.g., Neanderthals, Denisovans, early H. sapiens) occurred repeatedly in the Late Pleistocene, likely contributing elements to our genetic make-up that might have been crucial to our success as a diverse, adaptable species. Following these advances, the prevailing human origins model has shifted from one of near-complete replacement to a more nuanced view of partial replacement with considerable reticulation. Here we provide a brief introduction to the current genetic evidence for hybridization among hominins, its prevalence in, and effects on, comparative mammal groups, and especially how it manifests in the skull. We then explore the degree to which cranial variation seen in the fossil record of Late Pleistocene hominins from Western Eurasia corresponds with our current genetic and comparative data. We are especially interested in understanding the degree to which skeletal data can reflect admixture. Our findings indicate some correspondence between these different lines of evidence, flag individual fossils as possibly admixed, and suggest that different cranial regions may preserve hybridisation signals differentially. We urge further studies of the phenotype in order to expand our ability to detect the ways in which migration, interaction and genetic exchange have shaped the human past, beyond what is currently visible with the lens of ancient DNA. </pre>
The population of merging compact binaries inferred using gravitational waves through GWTC-3 - Data release
<p>Data associated with Figures, Tables, and population parameter samples associated with <br><strong>The population of merging compact binaries inferred using gravitational waves through GWTC-3 , </strong><br><strong><a href="https://dcc.ligo.org/LIGO-P2100239/public">LIGO DCC</a>, <a href="https://arxiv.org/abs/2111.03634">arXiv</a>, <a href="https://journals.aps.org/prx/abstract/10.1103/PhysRevX.13.011048">PRX</a>. </strong><br>This is v3, superseding v2. Please see the README.md for more information.</p>
Merged SCIAMACHY-OMPS limb ozone time series
<p>This data set contains the time series of merged monthly mean ozone profiles retrieved at the University of Bremen from SCIAMACHY and OMPS-LP limb observations. The merging is performed on deseasonalized anomalies, but the data set contains also the reconstructed number density time series. The data set is longitudinally resolved, with a 5° latitude and 20° longitude resolution, and a vertical grid with 3.3 km spacing. </p>
Local Governance in Ukraine during the full-scale Russian invasion. – Merged data from online surveys of local self-government authorities by the Congress of Local and Regional Authorities of the Council of Europe in 2022 and Kyiv School of Economics in 2024.
The dataset includes responses from two waves of online surveys targeting local self-government representatives in Ukraine, with a focus on crisis governance during the ongoing Russian war. The first wave was conducted from August 30 to September 20, 2022, by the Congress of Local and Regional Authorities of the Council of Europe, yielding 241 responses (16% of all Ukrainian local communities). The second wave was conducted by Kyiv School of Economics from January 1 to March 12, 2024, with 181 responses (14% of government-controlled municipalities). Data formats include CSV and SAV files, along with an XSL codebook for both waves. The merged dataset comprises 442 responses from small, medium, and large municipalities under varied security conditions, with a total file size of approximately 4 MB.
Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint Models Using Similarity to Training Data
<p>The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training data. In this work, we developed “similarity-based merger models” which combined the output of individual models trained on cell morphology (based on Cell Painting) and chemical structure (based on chemical fingerprints) and the structural and morphological similarities of the test compounds to training compounds. We applied these similarity-based merger models using logistic equations to weigh individual features and predicted assay hit calls of 177 assays from ChEMBL, PubChem and the Broad Institute, where the required Cell Painting annotations were available. We found that the similarity-based merger models outperformed other models with an additional 20% assays (79 out of 177 assays) with an AUC>0.70 compared with 65 out of 177 assays using structural models and 50 out of 177 assays using Cell Painting models. Our results demonstrate that similarity-based merger models combining structure and cell morphology models can more accurately predict a wide range of biological assay outcomes and expand the applicability domain by better extrapolating to new structural and morphology spaces.</p>
GapMinder World subset merged
<pre>The data consists of multiple <a href="https://www.gapminder.org/data/">"Gapminder World"</a> datasets merged together. Gapminder provides several datasets containing information about (almost) all countries around the world over the last two centuries. The transformation consists of merging the CSV files and correcting the country and the region names when necessary</pre>
Data for paper "Efficient Convex Zone Merging in Parametric Timed Automata"
<p>This is the experimental data for paper "Efficient Convex Zone Merging in Parametric Timed Automata"</p> <p>It comes in the form of two archives:</p> <ol> <li><strong>merging-artifact.zip</strong>: the whole set of benchmarks with the scripts to run</li> <li><strong>merging-artifact-FORMATS22-results.zip</strong>: all results executed 5 times</li> </ol>
A new merged dataset of global ocean chlorophyll-a concentration for better trend detection
<p>Chlorophyll-a concentration (Chla) is recognized as an essential climate variable and is one of the primary parameters of ocean-color satellite products. Ocean-color missions have accumulated continuous Chla data for over two decades since the launch of SeaWiFS in 1997. However, the on-orbit life of a single mission is about five to ten years. To build a dataset with a time span long enough to serve as a climate data record (CDR), it is necessary to merge the Chla data from multiple sensors. The European Space Agency has developed two sets of merged Chla products, namely GlobColour and OC-CCI, which have been widely used. Nonetheless, issues remain in the long-term trend analysis of these two datasets because the intermission differences in Chla have not been completely corrected. To obtain more accurate Chla trends in the global and various oceans, we produced a new dataset by merging Chla records from the Sea-viewing Wide Field-of-view Sensor, Medium-spectral Resolution Imaging Spectrometer, Moderate Resolution Imaging Spectroradiometer, Visible Infrared Imaging Radiometer Suite, and Ocean and Land Colour Instrument with intermission differences corrected in this work. The fitness of the dataset as a CDR was validated by using in situ Chla and comparing the trend estimates to the multi-annual variability of different satellite Chla records. </p>
Total O3 columns at polar regions: TOMCAT/SLIMCAT passive and active tracers and merged SAOZ-MSR2 dataset
<p>Passive and active total ozone columns simulated by the chemical transport model TOMCAT/SLIMCAT (Chipperfield, 1999) and the merged dataset of total ozone from Système d'Analyse par Observation Zénithale (SAOZ, Pommereau and Goutail, 1988) ground-based instruments and Multi-Sensor Reanalysis (MSR2, van der A et al., 2010, 2015) for the polar stations described in the Table here below.</p> <p><strong>Table. Arctic and Antarctic stations included in the study: station name and ID, latitude, longitude and measurement periods of SAOZ and MSR2 datasets.</strong></p> <table> <tbody> <tr> <td> <p><strong>Station (ID)</strong></p> </td> <td> <p><strong>Lat, Lon</strong></p> </td> <td> <p><strong>SAOZ dataset</strong></p> </td> <td> <p><strong>MSR2 dataset </strong></p> </td> </tr> <tr> <td> <p>Eureka, Nunavut (EU)</p> </td> <td> <p>80.1°N, 86.4°W</p> </td> <td> <p>2005-2020</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Ny-Alesund, Svalbard (NY)</p> </td> <td> <p>78.9°N, 11.9° E</p> </td> <td> <p>1991-2022</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Thule, Greenland (TH)</p> </td> <td> <p>76.5°N, 68.8°W</p> </td> <td> <p>1999-2003, 2005-2016</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Scoresbysund, Greenland (SC)</p> </td> <td> <p>70.5°N, 22.0°W</p> </td> <td> <p>1991-2017, 2019-2022</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Sodankyla, Finland (SK)</p> </td> <td> <p>67.4°N, 26.6° E</p> </td> <td> <p>1991-2022</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Sondre Stromfjord, Greenland (SS)</p> </td> <td> <p>67.0°N, 50.6°W</p> </td> <td> <p>2018-2022</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Zhigansk, Russia (ZH)</p> </td> <td> <p>66.8°N, 123.4° E</p> </td> <td> <p>1992-2013</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Salekhard, Russia (SA)</p> </td> <td> <p>66.5°N, 66.7°E</p> </td> <td> <p>2002-2016</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Marambio, Antarctica (MB)</p> </td> <td> <p>64.2°S, 56.7°W</p> </td> <td> <p>-</p> </td> <td> <p>1989-2021</p> </td> </tr> <tr> <td> <p>Dumont d’Urville, Antarctica (DD)</p> </td> <td> <p>66.7°S, 140.0°E</p> </td> <td> <p>1989-2021</p> </td> <td> <p>1989-2021</p> </td> </tr> <tr> <td> <p>Rothera, Antarctica (RO)</p> </td> <td> <p>67.6°S, 68.1°W</p> </td> <td> <p>2007-2021</p> </td> <td> <p>1989-2021</p> </td> </tr> <tr> <td> <p>Syowa, Antarctica (SW)</p> </td> <td> <p>69.0°S, 39.6°E</p> </td> <td> <p>-</p> </td> <td> <p>1989-2021</p> </td> </tr> <tr> <td> <p>Neumayer, Antarctica (NM)</p> </td> <td> <p>70.7°S, 8.3°W</p> </td> <td> <p>-</p> </td> <td> <p>1989-2021</p> </td> </tr> <tr> <td> <p>Terra Nova, Antarctica (TN)</p> </td> <td> <p>74.8°S, 164.5°E</p> </td> <td> <p>-</p> </td> <td> <p>1989-2021</p> </td> </tr> <tr> <td> <p>Concordia, Antarctica (DO)</p> </td> <td> <p>75.1°S, 123.4°E</p> </td> <td> <p>2007-2021</p> </td> <td> <p>1989-2021</p> </td> </tr> <tr> <td> <p>Halley, Antarctica (HB)</p> </td> <td> <p>75.6°S, 26.8°W</p> </td> <td> <p>-</p> </td> <td> <p>1989-2021</p> </td> </tr> </tbody> </table> <p> </p> <p>Each file corresponds to the whole winter time data set of the station identified by its ID: O3_PassActSLIMCAT_CompositeMSR2SAOZ_ID.txt</p> <p>1<sup>st</sup> column: Year</p> <p>2<sup>nd</sup> column: Day of Year (DoY)</p> <p>3<sup>rd</sup> column: Passive ozone column (without chemistry) modelled by TOMCAT/SLIMCAT (O3pasS).</p> <p>4<sup>th</sup> column: Active ozone column (with full chemistry) modelled by TOMCAT/SLIMCAT (O3actS).</p> <p>5<sup>th</sup> column: merged data from SAOZ observations and MSR2 (O3comp)</p> <p>Passive and active tracers of SLIMCAT were normalized to O3comp data in the beginning of the winter.</p> <p>NaN is used when there is no observation either from SAOZ instrument or MSR2 dataset or from the model.</p> <p> </p> <p><strong>References</strong></p> <p>Chipperfield, M. P.: New version of the TOMCAT/SLIMCAT offline chemical transport model: Intercomparison of stratospheric tracer experiments, Q. J. Roy. Meteor. Soc., 132, 1179–1203, <a href="https://doi.org/10.1256/QJ.05.51">https://doi.org/10.1256/QJ.05.51</a>, 2006.</p> <p>Pommereau, J.-P., and Goutail, F.: O<sub>3</sub> and NO<sub>2</sub> ground-based measurements by visible spectrometry during arctic winter and spring 1988, Geophys. Res. Lett., 15, 891–894, <a href="https://doi.org/10.1029/GL015i008p00891">https://doi.org/10.1029/GL015i008p00891</a>,1988.</p> <p>van der A, R. J., Allaart, M. A. F., and Eskes, H. J.: Multi sensor reanalysis of total ozone, Atmos. Chem. Phys., 10, 11277–11294, <a href="https://doi.org/10.5194/acp-10-11277-2010">https://doi.org/10.5194/acp-10-11277-2010</a>, 2010.</p> <p>van der A, R. J., Allaart, M. A. F., and Eskes, H. J.: Extended and refined multi sensor reanalysis of total ozone for the period 1970–2012, Atmos. Meas. Tech., 8, 3021–3035, <a href="https://doi.org/10.5194/amt-8-3021-2015">https://doi.org/10.5194/amt-8-3021-2015</a>, 2015.</p>
Cypherpunks Mailing List (merge 1992-2023/03)
<p>Merged dataset with cypherpunks mailing list </p> <p>Data source: <a href="https://github.com/cryptoanarchywiki">https://github.com/cryptoanarchywiki</a></p> <ul> <li>1992-2000: <a href="https://mailing-list-archive.cryptoanarchy.wiki/">https://mailing-list-archive.cryptoanarchy.wiki/</a> / <a href="https://cypherpunks.venona.com/raw/">https://cypherpunks.venona.com/raw/</a> (228 MB| 98807 mails)</li> <li>2000-2016: <a href="https://github.com/cryptoanarchywiki/2000-to-2016-raw-cypherpunks-archive">https://github.com/cryptoanarchywiki/2000-to-2016-raw-cypherpunks-archive</a> (246,7 MB | /92193 mails)</li> <li>July 2013 to Present/March 2023: <a href="https://lists.cpunks.org/pipermail/cypherpunks/">https://lists.cpunks.org/pipermail/cypherpunks/</a> (1206 MB | 65795 mails)</li> </ul> <p>Last version (511.6 MB) with all metadata:</p> <p>from_address = msg['From']</p> <p>to_address = msg['To']</p> <p>message_id = msg['Message-ID']</p> <p>date = msg['Date']</p> <p>subject = msg['Subject']</p> <p>in_reply_to = msg['In-Reply-To']</p> <p>mime_version = msg['MIME-Version']</p> <p>content_type = msg['Content-Type']</p> <p>body = msg.get_payload()</p> <p> </p>
Benchmarks for Evaluation and Comparison of Three-way Model Merging Techniques
<p>The attached files are intended to allow the interaction of researchers in the field of Model Merging Conflict Detection and Resolution. For people who want to add the results of evaluating a new technique and contribute to the creation of the actual body of knowledge, it is necessary to download the raw files and fill them out.</p>
Dataset of merge conflicts collected from GitHub repositories
<p>Within each nested folder of the archive you will find files A,O,B and M. They each represent a conflict where file O was altered in two different ways, resulting in A and B. Finally, a developer solved the merge conflict committing M as the solution.</p> <p>We have selected these by manually searching for a programming language on GitHub and selecting those repositories that had a large number of forks, commits and contributors.</p>
eedward3/Opera-Company-IRS-990-Merge: Opera_Company_IRS_990_Merge_Release1
<p>The purpose of this script is to merge a compiled list of all Opera Companies in the US with their corresponding IRS 990 data, if available.</p>
4DMED precipitation product: 1 km merged precipitation from CPC, IMERG-LR and SM2RAIN-ASCAT for mediterranean basin
<p>The high resolution satellite precipitation product is based on the integration of multiple precipitation and rainfall datasets to generate a high spatial (1 km) and temporal (daily) resolution precipitation product over the Mediterranean area. The following precipitation and rainfall datasets are downscaled and merged together: GPM-Late run, CPC, SM2RAIN-ASCAT. All these products are originally at coarse spatial resolution (>10 km) and have been downscaled to 1 km spatial resolution using CHELSA (1 km) climatology. The three products are then merged with a triple collocation technique. In the areas covered by snow only GPM-Late run and CPC are merged together, again using the triple collocation to obtain the relative weights (third product: ERA5 Land precipitation). The product has been developed in the framework of the 4D-MED Hydrology project. The product is available in the period 2015-2022. </p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The work is supported by the European Space Agency (ESA) through the 4D-MED Hydrology project (grant no. ESA 4000136272/21/I-EF)</p>
RoadTrafficMARKS: Dataset of 10057 images (256x256 pixels) of Curved arrow, Straight arrow, Pedestrian crossing, Straight-right merge arrow, Merge arrow, Left arrow, Yield, Stop, Right arrow, Straight-left merge arrow, Speed limit and Straight-right-left merge arrow markings and their BoundingBox labels
<p><strong>The dataset consists of 10057 PNG images (256x256 pixels) of </strong><strong>high resolution aerial orthoimages </strong><strong>taggged with </strong><strong>twelve classes of traffic signals</strong><strong>: (1) Curved arrow, (2) Straight arrow, (3) Pedestrian crossing, (4) Straight-right merge arrow, (5) Merge arrow, (6) Left arrow, (7) Yield, (8) Stop, (9) Right arrow, (10) Straight-left merge arrow, (11) Speed limit and (12) Straight-right-left merge arrow markings, together with their corresponding Bounding Boxes. The dataset has been created in the framework of the SROADEX project to train an identification process based on artificial neural networks.</strong></p> <p><strong>The dataset was created by manually tagging the twelve class of marks on orthoimage tiles of 256x256 pixels </strong><strong><strong>with the LabelMe tool</strong>. After the semantic labeling (manual digitalization of the contour of the signals), a transformation and random splitting process has been carried to prepare the data for the neural networks</strong><strong><strong> training</strong>. It resulted in 80% of the images for training (8031), 10% for validation (1000) and 10% for testing (1021).</strong></p> <p><strong>The next table presents the number of images of each class on the </strong><strong><strong>"train", "valid" and "test" </strong>sets.</strong></p> <table> <tbody> <tr> <td> </td> <td>Train</td> <td>Valid</td> <td>Test</td> <td>Total</td> </tr> <tr> <td>CURVED ARROW</td> <td>319</td> <td>43</td> <td>48</td> <td>410</td> </tr> <tr> <td>STRAIGHT ARROW</td> <td>6730</td> <td>806</td> <td>843</td> <td>8379</td> </tr> <tr> <td>PEDESTRIAN CROSSING</td> <td>3881</td> <td>465</td> <td>485</td> <td>4831</td> </tr> <tr> <td>STRIGNT-RIGHT MERGE ARROW</td> <td>1373</td> <td>187</td> <td>177</td> <td>1737</td> </tr> <tr> <td>MERGE ARROW</td> <td>550</td> <td>91</td> <td>59</td> <td>700</td> </tr> <tr> <td>LEFT ARROW </td> <td>238</td> <td>33</td> <td>31</td> <td>302</td> </tr> <tr> <td>YIELD</td> <td>356</td> <td>47</td> <td>40</td> <td>443</td> </tr> <tr> <td>STOP</td> <td>141</td> <td>19</td> <td>15</td> <td>175</td> </tr> <tr> <td>RIGHT ARROW</td> <td>836</td> <td>93</td> <td>111</td> <td>1040</td> </tr> <tr> <td>STRIGNT-LEFT MERGE ARROW</td> <td>152</td> <td>19</td> <td>21</td> <td>192</td> </tr> <tr> <td>SPEED LIMIT</td> <td>392</td> <td>50</td> <td>45</td> <td>487</td> </tr> <tr> <td>STRIGNT-RIGHT-LEFT MERGE ARROW</td> <td>82</td> <td>9</td> <td>9</td> <td>100</td> </tr> </tbody> </table>
ScienceDex guides
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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.