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6,410 results for “results”
Lamminaho Wooden Estate: 3D laser scanner survey and post production results.
<p>The dataset collects detailed information about the architecture and the environment of the historic place of Lamminaho, in Vaala region, Finland. The topic is included in the list of the case studies chosen by post doctoral fellow Sara Porzilli, who is working under Marie S. Curie Fellowship at the University of Oulu, Finland (School of Architecture, Department of "History of Architecture and Restoration Studies"). Supervisor: Prof. Arch. Anna-Maija Ylimaula. The study was promoted also by the "National Board of Antiquities (NBA) - Museovirasto "based in Helsinki (Responsible: Arch. Helena Hirviniemi) operating under the Directorate of the Finnish Ministry of Education and Culture and "Senate Properties" (Responsible: Dr. Juha Keranen), partner and manager working under the Finnish government for the protection and protection of the Finnish heritage present on the territory of Italy and abroad. The research was dedicated to defining the methods for carrying out survey activities on historical wooden architecture, identifying the fundamental aspects of laser scanners methodologies and photogrammetric activities. The work has produced a detailed info-graphic atlas concerning all the buildings located in Lamminaho. The work had a theoretical approach, devoted on research and archival documentation. The results of the research are going to support all the practical activities of restoration and repair necessary in the process of musealization of the area.</p>
Results of the ice sheet model initialisation experiments initMIP-Greenland: an ISMIP6 intercomparison
<p>This archive provides the forcing data and ice sheet model output produced as part of the publication "Design and results of the ice sheet model initialisation experiments initMIP-Greenland: an ISMIP6 intercomparison", published in The Cryosphere, https://www.the-cryosphere.net/12/1433/2018/</p> <p>Goelzer, H., Nowicki, S., Edwards, T., Beckley, M., Abe-Ouchi, A., Aschwanden, A., Calov, R., Gagliardini, O., Gillet-Chaulet, F., Golledge, N. R., Gregory, J., Greve, R., Humbert, A., Huybrechts, P., Kennedy, J. H., Larour, E., Lipscomb, W. H., Le clec´h, S., Lee, V., Morlighem, M., Pattyn, F., Payne, A. J., Rodehacke, C., Rückamp, M., Saito, F., Schlegel, N., Seroussi, H., Shepherd, A., Sun, S., van de Wal, R., and Ziemen, F. A.: Design and results of the ice sheet model initialisation experiments initMIP-Greenland: an ISMIP6 intercomparison, The Cryosphere, 12, 1433-1460, 2018, doi:10.5194/tc-12-1433-2018.</p> <p>Contact: Heiko Goelzer, h.goelzer@uu.nl</p> <p>Further information on ISMIP6 and initMIP-Greenland can be found here:<br> http://www.climate-cryosphere.org/activities/targeted/ismip6<br> http://www.climate-cryosphere.org/wiki/index.php?title=InitMIP-Greenland</p> <p>Users should cite the original publication when using all or part of the data. <br> In order to document CMIP6’s scientific impact and enable ongoing support of CMIP, users are also obligated to acknowledge CMIP6, ISMIP6 and the participating modelling groups.</p> <p><br> *** Important note ***<br> For consistency with future ISMIP6 intercomparison exercises and some observational data sets, we have re-gridded all output to a diagnostic grid following the EPSG:3413 specifications, which differs from the grid originally used to distribute the forcing data. We also provide the forcing data conservatively interpolated to the new grid. </p> <p><br> Archive overview<br> ----------------<br> README.txt - this information</p> <p>dSMB.zip - The original surface mass balance anomaly forcing data and description<br> dSMB/<br> dsmb_01B13_ISMIP6_v2.nc<br> dsmb_05B13_ISMIP6_v2.nc<br> dsmb_10B13_ISMIP6_v2.nc<br> dsmb_20B13_ISMIP6_v2.nc<br> README_dSMB_v2.txt</p> <p>dSMB_epsg3413.zip - The surface mass balance anomaly forcing data and description, interpolated to the new grid on EPSG:3413<br> dSMB_epsg3413/<br> dsmb_01e3413_ISMIP6_v2.nc<br> dsmb_05e3413_ISMIP6_v2.nc<br> dsmb_10e3413_ISMIP6_v2.nc<br> dsmb_20e3413_ISMIP6_v2.nc<br> README_dSMB_v2_epsg3413.txt</p> <p><group>_<model>_<experiment>.zip - The model output per group, model and experiment (init, ctrl, asmb)<br> <group1>_<model1>_init/<br> acabf_GIS_<group1>_<model1>_init.nc<br> ...<br> <group1>_<model1>_ctrl/<br> acabf_GIS_<group1>_<model1>_ctrl.nc<br> ...<br> <group1>_<model1>_asmb/<br> acabf_GIS_<group1>_<model1>_asmb.nc<br> ...</p> <p><group1>_<model2>_init/<br> ...<br> <group1>_<model2>_ctrl/<br> ...<br> <group1>_<model2>_asmb/<br> ...</p> <p><group2>_<model1>_init/<br> ...<br> <group2>_<model1>_ctrl/<br> ... <br> <group2>_<model1>_asmb/</p> <p>...</p> <p> </p> <p>The following script may be used to download the content of the archive.</p> <p>#!/bin/bash<br> wget https://zenodo.org/record/1173088/files/README.txt<br> wget https://zenodo.org/record/1173088/files/dSMB_epsg3413.zip<br> wget https://zenodo.org/record/1173088/files/dSMB.zip<br> <br> for amodel in ARC_PISM AWI_ISSM1 AWI_ISSM2 BGC_BISICLES1 BGC_BISICLES2 BGC_BISICLES3 DMI_PISM1 DMI_PISM2 DMI_PISM3 DMI_PISM4 DMI_PISM5 IGE_ELMER1 IGE_ELMER2 ILTS_SICOPOLIS ILTSPIK_SICOPOLIS IMAU_IMAUICE1 IMAU_IMAUICE2 IMAU_IMAUICE3 JPL_ISSM LANL_CISM LSCE_GRISLI MIROC_ICIES1 MIROC_ICIES2 MPIM_PISM UAF_PISM1 UAF_PISM2 UAF_PISM3 UAF_PISM4 UAF_PISM5 UAF_PISM6 UCIJPL_ISSM ULB_FETISH1 ULB_FETISH2 VUB_GISM1 VUB_GISM2; do</p> <p>wget https://zenodo.org/record/1173088/files/${amodel}_init.zip<br> wget https://zenodo.org/record/1173088/files/${amodel}_ctrl.zip<br> wget https://zenodo.org/record/1173088/files/${amodel}_asmb.zip</p> <p>done</p> <p> </p>
MICCAI 2016 MS lesion segmentation challenge: supplementary results
<p>This package contains supplementary material for our article prepared for publication and under revision. It contains omitted results due to space limits of the article as well as detailed, patient per patient and team per team results for all metrics. Additional figures redundant with those of the article are also provided. </p> <p>The readme file Readme_SupplementalMaterial.txt provides details about each individual file content.</p>
Mobile Cloud Computing Bibliographic Results from Google Scholar
<p>This dataset contains all the results for the term "Mobile Cloud Computing" on Google Scholar until June 2018. The data was acquired using Publish or Perish. The data has been cleaned such that the wrong and invalid results have been removed, duplicates have been removed. Titles are accurate and fine but authors and publishers info. etc. is still unclean. For textual analysis based on paper titles, this dataset is fine. For any other factor, such as institutional or journal or authorship analysis, this isn't a good choice. </p>
Mobile Edge Computing Bibliographic Results from Google Scholar
<p>This dataset contains all the results for the term "Mobile Edge Computing" on Google Scholar until June 2018. The data was acquired using Publish or Perish. The data has been cleaned such that the wrong and invalid results have been removed, duplicates have been removed. Titles are accurate and fine but authors and publishers info. etc. is still unclean. For textual analysis based on paper titles, this dataset is fine. For any other factor, such as institutional or journal or authorship analysis, this isn't a good choice. </p>
Edge Computing Bibliographic Results from Google Scholar
<p>This dataset contains all the results for the term "Edge Computing" on Google Scholar until June 2018. The data was acquired using Publish or Perish. The data has been cleaned such that the wrong and invalid results have been removed, duplicates have been removed. Titles are accurate and fine but authors and publishers info. etc. is still unclean. For textual analysis based on paper titles, this dataset is fine. For any other factor, such as institutional or journal or authorship analysis, this isn't a good choice. </p>
Fog Computing Bibliographic Results from Google Scholar
<p>This dataset contains all the results for the term "Fog Computing" on Google Scholar until June 2018. The data was acquired using Publish or Perish. The data has been cleaned such that the wrong and invalid results have been removed, duplicates have been removed. Titles are accurate and fine but authors and publishers info. etc. is still unclean. For textual analysis based on paper titles, this dataset is fine. For any other factor, such as institutional or journal or authorship analysis, this isn't a good choice. </p>
Meta-analysis results of epigenome-wide association studies in neonates reveals widespread differential DNA methylation associated with birthweight
<p>Birthweight is associated with health outcomes across the life course, DNA methylation may be an underlying mechanism. In this meta-analysis of epigenome-wide association studies of 8,825 neonates from 24 birth cohorts in the Pregnancy And Childhood Epigenetics Consortium, DNA methylation in neonatal blood is associated with birthweight at 914 sites, with a difference in birthweight ranging from -183 to 178 grams per 10% increase in methylation (P<sub>Bonferroni</sub><1.06x10<sup>-7</sup>).</p>
Recognising innovative companies by using a diversified stacked generalisation method for website classification – the raw results
<p><strong>Introduction</strong></p> <p>The classification models were trained out by using the Classification and Regression Training package (caret) [1]. The models' parameters were fine-tuned by the 10-fold cross-validation procedure [2].</p> <p><strong>Cluster parameters</strong></p> <p>Most computations were carried out on a cluster having the following parameters:</p> <ul> <li>GPU: NVIDIA Tesla P100;</li> <li>CPU: 2.0 GHz Intel® Xeon® Platinum 8167M;</li> <li>The number of GPUs: 2;</li> <li>The number of CPU cores: 28;</li> <li>The number of CPU threads: 56;</li> <li>RAM: 192 GB;</li> <li>Storage: 3 TB.</li> </ul> <p>Only one model (k-nn) was calculated on a cluster having the following parameters:</p> <ul> <li>Processor: Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz 3.40 GHz;</li> <li>RAM: 16 GB;</li> <li>Windows 64 bit.</li> </ul> <p><strong>Performance statistics</strong></p> <p>All performance statistics are stored in cvs files. Each file corresponds to a particular machine learning method such as a file, "methodName-stat.csv" contains all data regarding a method, "methodName." All files cover the following columns:</p> <ul> <li><em>dataSetName – </em>a name of a data set on which evaluation was carried out; there are three possible values: (i) <em>firstPages</em> refers to the first data set (<em>L<sub>D</sub></em>) that contains textual description of a company; (ii) <em>firstPageLabels</em> refers to the second data set (<em>L<sub>L</sub></em>) that involves link labels that were extracted from an index page; (iii) <em>aggregateDocument</em> refers to the third data set (<em>L<sub>B</sub></em>) that consists of a so-called big document;</li> <li><em>fmeasure</em> - the number of features that were taken into account during evaluation;</li> <li><em>method</em> - the name of function in the caret package;</li> <li><em>parameters</em> - the values of parameters received from a tuning phase of a given classification method;</li> <li><em>precision </em>– the value of method’s precision;</li> <li><em>recall </em>– the value of method’s recall;</li> <li><em>fmeasure</em> - the value of method’s F-measure; </li> <li><em>error</em> - the value of method’s error;</li> <li><em>acc </em>– the value of method’s.</li> </ul> <p><strong>Time processing statistics</strong></p> <p>All time processing statistics, like the performance statistics, are stored in cvs files. Each file corresponds to a particular machine learning method such as a file, "methodName-time.csv". All files cover the following columns:</p> <ul> <li><em>dataSetName – </em>a name of a data set on which evaluation was carried out; there are three possible values: (i) <em>firstPages</em> refers to the first data set (<em>L<sub>D</sub></em>) that contains textual description of a company; (ii) <em>firstPageLabels</em> refers to the second data set (<em>L<sub>L</sub></em>) that involves link labels that were extracted from an index page; (iii) <em>aggregateDocument</em> refers to the third data set (<em>L<sub>B</sub></em>) that consists of a so-called big document;</li> <li><em>featureNo</em> - the number of features that were taken into account during evaluation;</li> <li><em>method</em> - the name of function in the caret package;</li> <li><em>user</em> - user time elapsed for executing a <em>method</em> as an R process;</li> <li><em>system</em> - system time elapsed for executing a <em>method</em> as an R process;</li> <li><em>elapsed</em> - total time elapsed for executing a <em>method</em> as an R process.</li> </ul> <p>For more information about user, system and total elapsed time, please see documentation [3].</p> <p><strong>References</strong></p> <p>[1] https://cran.r-project.org/web/packages/caret/</p> <p>[2] https://topepo.github.io/caret/model-training-and-tuning.html</p> <p>[3] https://stat.ethz.ch/R-manual/R-devel/library/base/html/proc.time.htm</p>
Global model results for transition potential
<p>Global model results including areas of high potential to transition from tree cover to tree cover loss to areas of low potential to transition. Values are unitless and range from 0–1.</p>
Regional model results (combined) for the six transition potentials (one for Africa, Australia, Asia, Europe, North America, and South America)
<p>Results for the six regional models showing areas of high potential to transition from tree cover to tree cover loss to areas of low potential to transition.</p>
WP5_TST_Tunable-vcsel-results_V1
<p>The Excel file contains the experimental results obtained in the characterization and exploitation of a 7-GHz tunable VCSEL. In particular, the laser behavior in function of bias current, MEMS current and temperature is reported both in terms of emitted optical power and emission wavelength. Moreover, the performance of a 8-GHz DMT modulation with and without vestigial sideband filtering on up to 40 km of SMF is reported.</p>
Experimental result to investigate the influence of user's tweets and diversification on serendipitous research paper recommendations
<p>This is a raw dataset of the experiment result to investigate the influence of user's tweets and diversification on serendipitous research paper recommendations.</p> <p> </p>
Results of the DYNAMO (Dynamic MEC Orchestration of Cellular Networks) experiment in the Fed4FIRE+ testbeds
<p>The main objective of the DYNAMO Fed4FIRE+ experiment was to perform Network Function Virtualization (NFV) Management and Network Orchestration (MANO) of a cellular network on top of cloud infrastructures, exploring one of the key enabling technologies for 5G systems and beyond. DYNAMO used cloud and radio access facilities at the IRIS testbed and cloud facilities at the University of Vigo (UVIGO) to deploy an end-to-end (E2E) cellular network and perform elastic changes on it if needed. The geographic distance in between facilitated the setup of a realistic Multi-Access Edge Computing (MEC) use case, where the virtual Evolved Packet Core (vEPC) was deployed at UVIGO (Spain) and the access network, i.e., the User Equipment (UE), the e-Node-B (eNB) and edge cloud, were implemented on IRIS testbed (Ireland).<br> <br> While the initial deployment of the E2E cellular network may be considered as static, DYNAMO showcases the elasticity that an E2E cellular network may need in runtime. Hence, we presented a use case consisting of a latency sensitive E2E cellular network (network slice), where the endpoint of the UE connection was initially located in the core (UVIGO) but then migrated to the edge (IRIS), in case the UE's latency ranges were unacceptable.<br> <br> In this regard, the UE reported the experienced latency to Open Network Automation Platform (ONAP), which is responsible to trigger specific policy-driven control actions if a predefined Service-Level Agreement (SLA) is violated.<strong> <em>This datased includes the reports provided by the UE to ONAP.</em></strong><br> <br> As a result, the endpoint of the data plane of the UE is automatically moved to the access network (IRIS) thus reducing significantly the latency for the UE. For the access part of the network, we implemented one srsLTE e-Node-B (eNB), one srsLTE User Equipment (UE) and a Devstack (Edge Cloud) in virtual machines on IRIS testbed. In addition, we also implemented an SDN switch controlled by an ONOS SDN controller. For the core part of the network, we considered a disaggregated vEPC from Open Air Interface (OAI) on a Devstack (Core Cloud) at UVIGO.<br> <br> DYNAMO has succeeded in the integration of a broad set of network elements and technologies between the two different domains (UVIGO and IRIS testbed) and fulfilled all initial objectives: (i) establishing communication between ONAP and IRIS testbed to deploy generic VNFs on core and edge clouds, (ii), deployment of an E2E cellular network with UE and eNB in IRIS and the vEPC at UVIGO, (iii), sending telemetry of the UE to ONAP and (iv) designing and testing closed-loop control actions in ONAP to migrate the data plane of the UE to the edge in case of unsatisfactorily SLA. DYNAMO paves the way to a broad set of future 5G experiments that will require resource orchestration, such as the deployment of network slices or the automatic scheduling of services in the limited resources of Edge Clouds.</p> <p>This repository contains the information sent from the UE to ONAP, in order to decide if the latency between the UE and the PGW is OK or if an action has to be considered to reduce such latency.<br> </p> <p> </p>
GLM2_modified and Results as used in Ma et al: Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2, Geosci. Model Dev., 2020
<p>Code modified GLM2, scripts and result as used in Ma et al 2019, Ma et al 2019, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-146</p>
HydroGeoSphere Model Input Files and Results for Validation of Pesticide Leaching to Groundwater for Nine EU FOCUS Scenarios
<p>This dataset includes HydroGeoSphere (HGS) model (Aquanty, 2024) input and output files for nine Forum for the Co-ordination of Pesticide Models and their Use (FOCUS) scenarios (EC, 2014) for simulation of leaching of four test contaminants to groundwater. It is recommended that users are familiar with HGS software in order to best make use of the available files. Scenarios are included in separate subfolders named using the first four letters of the FOCUS scenario location name, e.g. folder "chat" contains the model run for the "Chateaudun" scenario. It is recommended that users familiarize themselves with the (EC, 2014) groundwater scenarios. HGS model inputs are specified in the *.grok ASCII text file for each scenario in each subfolder. Soil material properties and evapotranspiration properties are included in HGS input files in ASCII text format in the "material_properties" subfolder. Solute application timing for each scenario are include in the "solute_app" subfolder. And climate times series inputs are included in the "weather" subfolder.</p>
Test results for Competition for CPU resources experiments
Open the record for dataset details and reuse information.
Profiling results for electron-positron plus jets production with Pepper
<h2>Profiling results for electron positron plus jets production with Pepper v1.1.1</h2> <h3>Contents</h3> <p>This includes data/output for the following GPU accelerated unweighted event generation runs:</p> <ul> <li>A run over a few seconds to produce pepper-internal timing results (`*timers.csv` files)</li> <li>The same run, but with `nvprof` (`*.nvprof` files)</li> <li>A run with a single event batch, with `ncu --import-source on --set full` (`*.ncu-rep` files)</li> </ul> <p>The simulated process is electron-positron pair production with n jets, with n = 0...4, at 13 TeV. The configuration files (`pepper.ini`) are included. Also the `pepper_cache` directories are included.</p> <p>All results are for the "main" pepper variant (which uses Kokkos to utilise the GPU) and for the "native" pepper variant (which uses CUDA directly).</p> <h3>Software stack and CPU/GPU hardware details</h3> <p>The software/hardware used for the profiling are as follows:</p> <ul> <li>GPU Driver: NVIDIA-SMI: 550.100, Driver Version: 550.100, CUDA Version: 12.4</li> <li>GPU: Tesla V100S-PCIE-32GB</li> <li>Cuda compilation tools: release 11.6, V11.6.124</li> <li>Kokkos 4.3.01</li> <li>gcc (GCC) 11.4.1 20231218 (Red Hat 11.4.1-3)</li> <li>Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz</li> </ul> <p>Note that the event generation includes write-out of event files. The LHEH5 files are written to local SSD storage. The event files are not included in this dataset.</p> <h3>Additional run parameters</h3> <table> <tbody> <tr> <td>e^+ e^-</td> <td>number of batches</td> <td>batch size</td> <td>rate (main variant)</td> <td>rate (native variant)</td> </tr> <tr> <td>+0j</td> <td>20</td> <td>1,179,648</td> <td>5.7e9</td> <td>7.4e9</td> </tr> <tr> <td>+1j</td> <td>20</td> <td>1,179,648 * 2</td> <td>1.4e10</td> <td>3.3e10</td> </tr> <tr> <td>+2j</td> <td>80</td> <td>1,179,648 / 2</td> <td>1.3e10</td> <td>5.4e10</td> </tr> <tr> <td>+3j</td> <td>20</td> <td>1,179,648</td> <td>7.7e9</td> <td>1.6e10</td> </tr> <tr> <td>+4j</td> <td>20</td> <td>1,179,648</td> <td>2.2e9</td> <td>3.1e9</td> </tr> <tr> <td>+5j</td> <td>20</td> <td>1,179,648</td> <td>3.4e8</td> <td>4.2e8</td> </tr> </tbody> </table> <p>The number of batches has been chosen such that the most important sub processes are likely to be sampled and such that the overall runtime is at least a few seconds. The batch size has been chosen by scanning over factors of two and picking the best-performing batch size with the native variant. The event rate is the number given in the Pepper output. It does not include the closing time of the generated HDF5 file, which can be significant for the two lowest multiplicities. This can be checked by inspecting the corresponding relative and absolute timing results in the `*timing.csv` files included in the dataset.</p>
Profiling results for top pair plus jets production with Pepper
<h2>Profiling results for top pair plus jets production with Pepper v1.1.1</h2> <h3>Contents</h3> <p>This includes data/output for the following GPU accelerated unweighted event generation runs:</p> <ul> <li>A run over a few seconds to produce pepper-internal timing results (`*timers.csv` files)</li> <li>The same run, but with `nvprof` (`*.nvprof` files)</li> <li>A run with a single event batch, with `ncu --import-source on --set full` (`*.ncu-rep` files)</li> </ul> <p>The simulated process is top pair production with n jets, with n = 0...4, at 13 TeV. The configuration files (`pepper.ini`) are included. Also the `pepper_cache` directories are included.</p> <p>All results are for the "main" pepper variant (which uses Kokkos to utilise the GPU) and for the "native" pepper variant (which uses CUDA directly).</p> <h3>Software stack and CPU/GPU hardware details</h3> <p>The software/hardware used for the profiling are as follows:</p> <ul> <li>GPU Driver: NVIDIA-SMI: 550.100, Driver Version: 550.100, CUDA Version: 12.4</li> <li>GPU: Tesla V100S-PCIE-32GB</li> <li>Cuda compilation tools: release 11.6, V11.6.124</li> <li>Kokkos 4.3.01</li> <li>gcc (GCC) 11.4.1 20231218 (Red Hat 11.4.1-3)</li> <li>Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz</li> </ul> <p>Note that the event generation includes write-out of event files. The LHEH5 files are written to local SSD storage. The event files are not included in this dataset.</p> <h3>Additional run parameters</h3> <table> <tbody> <tr> <td>TTBar</td> <td>number of batches</td> <td>batch size</td> <td>rate (main variant)</td> <td>rate (native variant)</td> </tr> <tr> <td>+0j</td> <td>20</td> <td>1,179,648</td> <td>1.4e10</td> <td>2.0e10</td> </tr> <tr> <td>+1j</td> <td>20</td> <td>1,179,648</td> <td>1.5e10</td> <td>2.4e10</td> </tr> <tr> <td>+2j</td> <td>20</td> <td>1,179,648 * 2</td> <td>1.3e10</td> <td>2.7e10</td> </tr> <tr> <td>+3j</td> <td>20</td> <td>1,179,648 / 2</td> <td>6.4e9</td> <td>1.4e10</td> </tr> <tr> <td>+4j</td> <td>20</td> <td>1,179,648</td> <td>2.2e9</td> <td>2.2e9</td> </tr> </tbody> </table> <p>The number of batches has been chosen such that the most important sub processes are samples and such that the overall runtime is at least a few seconds. The batch size has been chosen by scanning over factors of two and picking the best-performing batch size with the native variant. The event rate is the number given in the Pepper output. It does not include the closing time of the generated HDF5 file, which can be significant for the two lowest multiplicities. This can be checked by inspecting the corresponding relative and absolute timing results in the `*timing.csv` files included in the dataset.</p>
Thomas_et_al_2023_[updated]_The_potential_of_hay_for_graminoid_introduction_in_the_restoration_of_subtropical_grasslands_results_from_a_greenhouse_experiment
<p>Data set from a greenhouse experiment where we aimed to assess the effect of three harvest dates of hay and two amounts of dry hay in the emergency of seedlings, to application in ecological restoration of Campos Sulinos grasslands, South Brazil.</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.