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1,891 results for “Field data”
GATEMAN project -- D8.2 -- Field experiments raw data
<p>This dataset contains a .zip file (RawData.zip) with some of the raw files recorded during the open-field experimentation campaign in the frame of the GATEMAN project. These files are grouped for each type of validation scenario defined. The scenarios are separated in jamming and spoofing cases (JAM:Jamming, SPO:Spoofing) and classified according to the zone (Z1: Zone 1, Z2: Zone 2) and the vehicle motion (S:Static, D:Dynamic).</p> <p>Also the reference document of the deliverable D8.2 is included here.</p> <p><strong>Dataset Structure:</strong></p> <p>├── <strong>Jamming</strong><br> ├── OF-JAM-Z2-D-Test 11<br> └── Test_11.bin<br> ├── OF-JAM-Z2-D-Test 12<br> └── Test_12.bin</p> <p> ├--─ OF-JAM-Z2-D-Test 13<br> └── Test_13.bin<br> ├── GNURadio<br> └── SingleTone.grc</p> <p>├── <strong>Spoofing</strong><br> ├── OF-SPO-Z2-S-Test 1<br> └── Master_Test1.sbf<br> └── Slave_Test1.sbf</p> <p> ├── OF-SPO-Z2-S-Test 5<br> └── Master_Test5.sbf<br> └── Slave_Test5.sbf</p> <p> ├── OF-SPO-Z2-D-Test 2<br> └── Master_Test2.sbf<br> └── Slave_Test2.sbf</p> <p> ├── OF-SPO-Z2-D-Test 4<br> └── Master_Test4.sbf<br> └── Slave_Test4.sbf</p> <p> ├── OF-SPO-Z2-D-Test 6<br> └── Master_Test6.sbf<br> └── Slave_Test6.sbf</p> <p> ├── OF-SPO-Z1-D-Test 29<br> └── Master_Test29.sbf<br> └── Slave_Test29.sbf</p> <p> ├── GNURadio<br> └── Scenario1_decimated.iq<br> └── PreProcessing.grc<br> └── Signal_to_Usrp.grc</p> <p> </p>
Data from "Predicting Global Ground Geoelectric Field With Coupled Geospace and Three‐Dimensional Geomagnetic Induction Models"
<p>Data presented in http://dx.doi.org/10.1029/2018SW001859 excluding the first and last hours which were determined to contain partially unphysical results and probably should not be used.</p> <p>Each file contains the external ground magnetic field components calculated on 5x5 degree geographic grid and the results of induction modeling using 1d and 3d ground conductivity models: total ground magnetic field components, horizontal ground electric field components. Times are given in UTC.</p> <p>To reproduce Figure 8 in above reference use:</p> <p> </p> <p>import numpy<br> import matplotlib.pyplot<br> data = numpy.load('2006-12-14T22:58:00.npz') # or 2006-12-14T22_58_00.npz<br> matplotlib.pyplot.colorbar(<br> matplotlib.pyplot.imshow(<br> data['B_3D_north'],<br> cmap = matplotlib.pyplot.get_cmap('bwr'),<br> vmin = -800, vmax = 800,<br> extent = (-180, 180, -90, 90)<br> ),<br> format = '%.0f', fraction = 0.02, pad = 0.03<br> )<br> matplotlib.pyplot.show()</p> <p> </p> <p>and substitute B_3D_east, E_3D_north, etc. for the different panels.</p>
Source data for "Synthetic gauge fields for phonon transport in a nano-optomechanical system"
<ul> <li>Experimental raw data for density plots in Fig 2. Each .csv contains an array, where 1st row corresponds to x_axis (mechanical frequency in MHz for panels 1,2,3,4) and first column the y_axis (optical frequency in THz for panel 1, modulation frequency in MHz for panels 2,3,4). First nonzero component is the 2nd for each array. Remaining array elements contain the z values (Thermomechanical noise spectral for panel 1, Amplitude of driven responses for panels 2,3,4). An illustrative example of plotting in an ipython notebook follows:</li> </ul> <p> %pylab inline</p> <p> A= genfromtxt('Fig2_data_modVolt=0mV_experiment.csv', delimiter=',') </p> <p> x = A[0,1:]<br> y = A[1:,0]<br> z = A[1:,1:]<br> imshow(z,aspect='auto',vmin=z.min(),vmax=z.max(),extent=[x.min(),x.max(),y.min(),y.max()],cmap='magma') </p> <ul> <li> Theoretical data for panel 4 in Fig 2, stored in a .csv with the same structure as previous.</li> <li> Raw experimental data for upper panels in Fig 3. Each .csv contains an array where 1st row corresponds to x_axis (modulation phase) and first column the y_axis (optical frequency in THz). Z values contain the experimental signal proportional to the Y optical quadrature of the transferred mode.</li> <li>Theoretical data for lower panels in Fig 3, stored in a .csv with the same structure as previous.</li> <li>Jupyter notebook to produce and plot typical data for Fig 4: phononic amplitude averaged over 100 disorder realizations, normalized to the maximum value (*extra_dependencies: Kwant Python library: <a href="https://kwant-project.org/">https://kwant-project.org/</a>).</li> </ul>
Data supporting publication: Revealing Mode Formation in Quasi-Bound States in the Continuum Metasurfaces via Near-Field Optical Microscopy
<p>This repository includes the data corresponding to the figures shown in the journal article entitledRevealing Mode Formation in Quasi-Bound States in the Continuum Metasurfaces via Near-Field Optical Microscopy, published in Advanced Materials on 02.08.2024</p>
On the mixing between flavor singlets in lattice gauge theories coupled to matter fields in multiple representations - data release
<p>This release contains all data and metadata used to prepare the publication <a href="https://arxiv.org/abs/2405.05765"><em>On the mixing between flavor singlets in lattice gauge theories coupled to matter fields in multiple representations</em> [2405.05765].</a> </p> <p>If you encounter difficulties downloading the large files, we recommend using <a href="../records/11142962">zenodo-get</a>. This provides a command-line downloader for any Zenodo record. For unstable connections we recommend using it with the -w flag to generate a list all files in this Zenodo record. This can then be used with tools such as <a href="https://www.gnu.org/software/wget/">wget</a> to resume partial downloads as</p> <p><code>zenodo_get RECORD_ID_OR_DOI -w - | xargs wget -c<br>zenodo_get RECORD_ID_OR_DOI </code></p> <p>(The second line ensures that the downloads completed correctly, and that the md5 hashes match)<br><br>Further details are given in the file README.md.</p> <p>The work of EB and BL is supported in part by the EPSRC ExCALIBUR programme ExaTEPP (project EP/X017168/1). The work of EB, BL, MP, and FZ has been supported by the STFC Consolidated Grant No. ST/X000648. The work of EB has also been supported by the UKRI Science and Technology Facilities Council (STFC) Research Software Engineering Fellowship EP/V052489/1. The work of NF has been supported by the STFC Consolidated Grant No. ST/X508834/1. The work of DKH was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B06033701). The work of DKH was further supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1A4A5031460). The work of JWL is supported by IBS under the project code, IBS-R018-D1. The work of HH and CJDL is supported by the Taiwanese MoST grant 109-2112-M-009-006-MY3 and NSTC grant 112-2112-M-A49-021-MY3. The work of CJDL is also supported by Grants No. 112-2639-M-002-006-ASP and No. 113-2119-M-007-013. The work of BL and MP has been further supported in part by the STFC Consolidated Grant No. ST/T000813/1.<br>BL and MP received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program under Grant Agreement No.~813942. The work of DV is supported by STFC under Consolidated Grant No. ST/X000680/1.</p> <p>Numerical simulations have been performed on the DiRAC Extreme Scaling service at the University of Edinburgh, and on the DiRAC Data Intensive service at Leicester.<br>The DiRAC Extreme Scaling service is operated by the Edinburgh Parallel Computing Centre on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk). This equipment was funded by BEIS capital funding via STFC capital grant ST/R00238X/1 and STFC DiRAC Operations grant ST/R001006/1. DiRAC is part of the National e-Infrastructure</p>
Data supporting publication: Nanoscale Mechanical Manipulation of Ultrathin SiN Membranes Enabling Infrared Near-Field Microscopy of Liquid-Immersed samples
<p>This repository includes the data corresponding to the figures shown in the journal article entitled Nanoscale Mechanical Manipulation of Ultrathin SiN Membranes Enabling Infrared Near-Field Microscopy of Liquid-Immersed samples, published in small. </p>
Data for: Multi-year field measurements of home storage systems and their use in capacity estimation
<p>The dataset accompanies the Nature Energy publication by Figgener et al. (2024), Multi-year field measurements of home storage systems and their use in capacity estimation, <a href="https://doi.org/10.1038/s41560-024-01620-9">DOI 10.1038/s41560-024-01620-9</a>. <br><br>In addition, we use the dataset in Figgener et al. (2024), Degradation mode estimation using reconstructed open circuit voltage curves from multi-year home storage field data, <a href="https://doi.org/10.48550/arXiv.2411.08025">DOI 10.48550/arXiv.2411.08025</a></p> <p>The ISEA / CARL of RWTH Aachen University measured 21 private home storage systems in Germany over up to eight years from 2015 to 2022. All these storage systems are combined with residential photovoltaic systems to increase self-consumption. The measured quantities published are system-level battery current, voltage, power, battery pack housing temperature, and room temperature. The sample rate is one second. The dataset consists of 106 system years, 14 billion data points, and 1,270 monthly files stored in 21 system folders. </p> <p>Use the data as follows:<br><br>1. Download the data (Data_ID_01.zip to Data_ID_21.zip) and the belonging repository (Metadata_and_Code.zip)</p> <p>2. Uncompress the files so that the uncompressed folders have the same name as the .zip files.</p> <p>3. Copy all data folders in folder "Metadata_and_Code/00_Data/01_Operational_Data". Read and execute the file "StartUp_Read_and_Execute.m" and stay in this folder for any script you execute. </p> <p>In addition, a detailed description of the dataset and how to use it can be found in the supplementary information of the publication.</p>
BepiColombo magnetic field data (MPO-MAG) from the two Venus flybys
<p> </p> <p>Vector magnetic field data from the first two Venus flybys (highres and 1-sec res).</p> <p>*Data will be properly archived on ESA's PSA, once the data has been finalized and/or cleaned*</p> <p>Reference frame: VSO</p> <p>Trajectory information is also included. </p>
JGR_biogeosciences_Supplementary_Information_Field_Data
<p>The dataset is the supplementary material as an Excel file, which includes the field measurements of DOC concentration, CDOM absorption coefficient, remote-sensing reflectance, along with the corresponding coordinates, dates, and times of collection. The study area is the Plum Island Estuary in Massachusetts and the Gulf of Maine (USA).</p>
Field data for: Enterovirus sequence data obtained from primate samples in Central Africa suggest a high prevalence of enteroviruses with possible zoonotic potential
<p>Enteroviruses infect humans and animals, can cause disease, and some may be transmitted across species barriers. We collected different types of samples from various species of Central African wildlife, including data on sampling location and tested the samples for the presence of Enterovirus RNA using a family level PCR. Specimen collection was approved by an Institutional Animal Care and Use Committee (IACUC) of the University of California Davis, and the Governments of Cameroon and the Democratic Republic of the Congo. Enterovirus RNA was detected in samples from 17 primates and 2 rodents. Some sequences were very similar while others were dissimilar to known species, highlighting the unexplored enterovirus diversity in wildlife.</p> <p>The samples and filed data were collected by field ecologists as part of the USAID funded PREDICT project (https://ohi.vetmed.ucdavis.edu/programs-projects/predict-project) and screened for enterovirus RNA using consensus PCR. Maps were generated using basic maps from Paintmaps (http://www.paintmaps.com), a free tool for educational and academic use. The dataset contains the metadata on enterovirus screening among wildlife in Cameroon and the Democratic Republic of the Congo from 2003-2014 as part of the USAID funded PREDICT project. Please refer to the article for more information on methods and references.</p>
Data for Rasch analysis of the three Field Practice Experiences Scales
<p>Data for Rasch analysis of the three Field Practice Experiences Scales. Data rare from Danish teacher education program students. Contains the following variables:</p> <p>Variables O1 to O12 are the items for the Observed scale</p> <p>Variables P1 to P12 are the items for the Practised scale</p> <p>Variables F1 to F12 are the items to the Received feedback scale</p> <p>Gender: 1 = female, 2 = male</p> <p>Campus: 1 = campus A, 2 = campus B</p> <p>T_Progr (teacher education program): 1 = regular, 2 = other</p> <p>P_level (level of latest field practice placement): 1 = 3rd level, 2 = 2nd level, 3 = 1st level</p> <p>Age: 1 = 24 years and younger, 2 = 25 years and older</p>
Hydrothermally altered landslide deposits at Askja, Iceland - field data
<p>We analyze the proximal deposits of the July 21, 2014 landslide at Askja (Iceland), by combining high-resolution imagery from Unoccupied Aircraft Systems (UAS) and in-situ hyperspectral field data. Results underline that the northern part of the landslide source region is a hydrothermally altered material class, which bifurcates halfway downslope and then extends to the lake. Here we provide the field data, such as the drone derived orthomosaic and digital elevation model (DEM), as well as hyperspectral field data. For locations of the data and measurements made we refer to the publication </p> <p>Pouria Marzban, Stefan Bredemeyer, Thomas R. Walter, Friederike Kaestner, Daniel Mueller and Sabine Chabrillat (2023) Hydrothermally altered landslide deposits at Askja, Iceland, identified by high resolution satellite and UAS imagery, spectral classification and hyperspectral field data.Frontiers in Earth Science Volcanology. Manuscript ID: 1083043</p>
Experimental data of "High-field 1/f noise in hBN-encapsulated graphene transistors"
<p>Current-to-voltage characteristics along with flicker noise amplitude (A factor, description given in the paper) of the devices studied in the main and supplementary text of the article " by A. Schmitt et al. Dimensions of devices are provided in the article</p>
Magnetic field data for "Cosmic Rays in Intermittent Magnetic Fields" (magnetic fields produced by the small-scale dynamo)
<p>Magnetic field data for the kinematic dynamo generated magnetic fields (KS) employed in the cosmic ray test particle simulations of Shukurov et al. 2017, <em>ApJL</em>, <strong>839</strong>, L16 [<a href="https://doi.org/10.3847/2041-8213/aa6aa6">https://doi.org/10.3847/2041-8213/aa6aa6</a>]. One dataset was also used in "Relative distribution of cosmic rays and magnetic fields", Seta et al. 2018, <em>MNRAS</em>, <strong>473 </strong>(4), 4544-4557 [<a href="https://doi.org/10.1093/mnras/stx2606">https://doi.org/10.1093/mnras/stx2606</a>]. These studies investigated the effects of magnetic field structure on charged test particle transport and trapping. See the README file for more details.</p> <p> </p>
Data on eye movements of glaucoma patients with asymmetrical visual field loss during free viewing.
<p>Raw eye tracking data and processed eye movement data were recorded from fifteen participants with assymmetrical visual field loss (visual field worse in one eye) while they freely viewed 270 images of nature with each eye monocularly.</p>
Hubble and Webb Space Telescope data for the cluster field RXJ-2129
<p>This repository contains reduced image mosaics from the Hubble and Webb Space Telescopes of the galaxy cluster RXJ-2129.</p> <p>Data were processed with the <a href="https://github.com/gbrammer/grizli">grizli</a> software pipeline, as described by <a href="https://arxiv.org/abs/2302.10936">Valentino et al. (2023, ApJ, in press)</a>, with the software and observatory calibrations available as of October 2022.</p> <p>For a given broad-band filter, the science data are given in the "sci.fits" files. Inverse variance weight images are provided in the "wht.fits" files. </p> <p>Aperture photometry following the methodology described by Valentino et al. is provided in "rxj2129-grizli-v4.0-fix_phot_apcorr.fits", and photometric redshift estimates derived with the <a href="https://github.com/gbrammer/eazy-py/">eazy-py </a>software are tabulated in "rxj2129-grizli-v4.0-fix.zout.fits".</p>
Data, scripts, and figures of the article: The effect of oregano essential oils on Feed Passage Syndrome in broilers: 1. Assessment under field conditions
<p>This data set contains the data, JMP scripts, and figures of the article titled "The effect of oregano essential oils on Feed Passage Syndrome in broilers: 1. Assessment under field conditions" to be published in the journal Animal - Open Space.</p>
Data to: "Despite impaired binocular function, binocular disparity integration across the visual field is spared in normal aging and glaucoma"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Maiello G., & Kwon, M. (in press) Despite impaired binocular function, binocular disparity integration across the visual field is spared in normal aging and glaucoma. IOVS</p> <p>A preprint version of the manuscript is available at: https://doi.org/10.1101/2022.11.28.518250</p>
Data presented in González-Flórez et al. 2023 "Insights into the size-resolved dust emission from field measurements in the Moroccan Sahara", Atmos. Chem. Phys.
<p>Meteorological, dust and saltation data used in González-Flórez et al., 2023. Data are based on measurements taken during an intensive dust field campaign conducted in the context of the FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe (FRAGMENT) project. The campaign took place in September 2019 in a small ephemeral lake, locally named "L'Bour", located in the Lower Drâa Valley in Morocco. The description of the data is provided below:</p> <p>- t.nc: time series of temperature measured with four aspirated shield temperature sensors (Campbell Scientific 43502 fan-aspirated shield with 43347 RTD Temperature probe) placed at heights of 1m, 2m, 4m and 8m.</p> <p>- t005.nc time series of temperature measured with a temperature and relative humidity probe (Campbell Scientific HC2A-S3) at 0.5m height.</p> <p>- rh005.nc: time series relative humidity measured with a temperature and relative humidity probe (Campbell Scientific HC2A-S3) at 0.5m height.</p> <p>- wspd.nc: time series of wind speed measured with five 2-D sonic anemometers (Campbell Scientific WINDSONIC4-L) placed at heights of 0.4m, 0.8m, 2m, 5m and 10m.</p> <p>- sdir.nc: time series of wind direction measured with five 2-D sonic anemometers (Campbell Scientific WINDSONIC4-L) placed at heights of 0.4m, 0.8m, 2m, 5m and 10m.</p> <p>- radout.nc: time series of outgoing long wave radiation measured with a four-component net radiometer (Campbell Scientific NR01-L radiometer) placed at 1.5m height.</p> <p>- p015.nc: times series barometric pressure measured with a barometer (Campbell Scientific CS106) at around 1.5m height.</p> <p>- u_star_law.nc: time series friction velocity calculated through the law of the wall method.</p> <p>- z0_law.nc: time series of roughness length calculated through the law of the wall method.</p> <p>- zeta_law.nc: time series of dimensionless height, zref/L, where zref is the reference height (zref=2m) and L is the Obukhov length calculated through the law of the wall method.</p> <p>- psd_lower_15avg_20190904_000000_integrated_bins.nc: time series of 15-min average number concentrations in integrated size bin resolution measured with an optical particle counter (Fidas 200S, Palas GmbH) at ~1.8m height.</p> <p>- psd_upper_15avg_20190904_000000_integrated_bins.nc: time series of 15-min average number concentrations in integrated size bin resolution measured with an optical particle counter (Fidas 200S, Palas GmbH) at ~3.5m height and corrected for systematic bias based on an intercomparison between the two Fidas at the end of the campaign.</p> <p>- diff_flux_nb_15avg_20190904_000000_integrated_bins.nc: time series of 15-min average number diffusive flux calculated using the flux-gradient method.</p> <p>- q_15avg.nc: time series of 15-min average saltation flux calculated based on measurements with optical gate devices at heights of 0.05m, 0.15m and 0.3m as part of the Standalone AeoliaN Transport Real-time Instrument (SANTRI, Desert Research Institute).</p> <p>- geometric_diameters_integrated_size_bins.csv: containing the minimum, maximum and mean logarithmic optical diameter of the integrated size bins.</p> <p>- optical_diameters_integrated_size_bins: containing the minimum, maximum and mean logarithmic geometric diameter of the integrated size bins.</p> <p>SANTRI data were processed by Martina Klose (<a href="mailto:martina.klose@kit.edu">martina.klose@kit.edu</a>) and the rest by Cristina González Flórez (<a href="mailto:cristina.gonzalez@bsc.es">cristina.gonzalez@bsc.es</a>). Please, cite González-Flórez et al. (2023, ACP) if you use these data. If the data become the key main component of a paper then co-authorship may be offered. Contact Carlos Pérez García-Pando (<a href="mailto:carlos.perez@bsc.es">carlos.perez@bsc.es</a>) if more details are needed.</p> <p>This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 773051, FRAGMENT).</p>
'Danum Revised' forest field plot data for thirty-five 0.28 hectare field plots, measured in 2020
<p>Permanent forest plot data from Sabah, Malaysia. Danum Valley Conservation Area. Here we provide a brief description of the permanent plot data collected following the same protocols utilized within the Kuamut forest reserve.</p><p>Twenty circular field plots with a 30 m radius that were established across the protected area as part of a collaboration between SEARRP and the Carnegie Airborne Observatory (CAO) in 2017. See <a href="https://doi.org/10.5281/zenodo.8042050">'Danum Revised' forest field plot data for twenty 0.28 hectare field plots, measured in 2017</a></p><p>In 2020 we added an additional 15 plots as part of work for Keller <i>et al. '</i>Biodiversity consequences of long-term active forest restoration in selectively-logged tropical rainforests', and so this dataset is the full 35 field plots measured in 2020.</p><p>These plots were surveyed following the same protocols as those described by the Kuamut conservation project. The field protocol is provided. The original 20 Field plot locations where selected using airborne LiDAR to specifically target both high Aboveground Carbon Density (ACD) areas, and also areas with different ACD predictions based on a draft set of carbon mapping models. See Asner et al. (2017) and Jucker et al. (2018) for further details.</p><p>In 2020 we selected existing these additional 15 plot points using a high resolution biomass map (see: Asner et al. 2017; Asner et al. 2021). In order to represent the entirety of the area sufficiently, we chose plot locations to represent the average biomass value and variation around it of the majority of the managed area (<a href="https://doi.org/10.1126/science.aay4490">cf. Philipson et al. (2020) for regional values</a>). The plot location further must have fulfilled the following criteria: (1) plots had to be at least 100 m away from noticeable features such as roads to avoid edge effects (but for logistic reasons they nevertheless had to be in the vicinity of roads); (2) plots had to be spread out as much as logistically feasible; (see Keller <i>et al.</i> (submitted), for full details). Note Keller <i>et al.</i> (submitted), used 30 plots for a balanced design between naturally regenerating and actively restored areas. Here we present all 35 plots measured in 2020. Plot locations where additionally updated using an SXblue GPS differential with SBAS augmentation. These locations are provided.</p><p>Use of these data require citation of this dataset, the associated publication (Keller <i>et al. submitted), </i>as well as the <a href="https://doi.org/10.5281/zenodo.8042050">2017 dataset</a> (which describes the full set up process),. We also require that you inform us of the use of the data would also appreciate the opportunity to be involved in work making use of this data. The required citations are follows:</p><p>Philipson, Christopher D., Brodrick, Philip G., Milne, Sol, Murus, Remmy, Rasion, Joulu, Reynolds, Glen, Siew Yee, Hii, Ulok, Philip Ak, & Asner, Gregory P. (2023). 'Danum Revised' forest field plot data for twenty 0.28 hectare field plots, measured in 2017 (1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8042050">https://doi.org/10.5281/zenodo.8042050</a></p><p>Keller <i>et al. (submitted). '</i>Biodiversity consequences of long-term active forest restoration in selectively-logged tropical rainforests',</p><p>This work would not be possible without the incredibly hard work and dedication of the entire SEARRP field team, including Philip Ak Ulok; Hii Siew Yee; Remmy Bin Murus; Alexander Karolus; Andy Brian Karolus; Frederica Karolus; Zidey Fulgentius; Welday Bin Girang; Mohamad Taufiq Bin Sumin, Mohd Fadil Bin Abd Karim, Joulu Rasion, and Japin Bin Rasion. We are very grateful to the help of all the field staff not specifically mentioned here.</p><p> </p><p><strong>References</strong></p><p>Asner, Gregory P., Philip G. Brodrick, Christopher Philipson, Nicolas R. Vaughn, Roberta E. Martin, David E. Knapp, Joseph Heckler, et al. 2017. "Mapped Aboveground Carbon Stocks to Advance Forest Conservation and Recovery in Malaysian Borneo." Biological Conservation 217 (June 2017): 289–310. <a href="https://doi.org/10.1016/j.biocon.2017.10.020">https://doi.org/10.1016/j.biocon.2017.10.020</a></p><p>Jucker, Tommaso, Gregory P. Asner, Michele Dalponte, Philip G. Brodrick, Christopher D. Philipson, Nicholas R. Vaughn, Yit Arn Teh, et al. 2018. "Estimating Aboveground Carbon Density and Its Uncertainty in Borneo's Structurally Complex Tropical Forests Using Airborne Laser Scanning." Biogeosciences 15 (12): 3811–30. <a href="https://doi.org/10.5194/bg-15-3811-2018">https://doi.org/10.5194/bg-15-3811-2018</a></p><p>Keller, Nadine, Pascal A Niklaus, Jaboury Ghazoul, Tobias Marfil, Elia Godoong, and Christopher D Philipson. 2023. "Biodiversity Consequences of Long-Term Active Forest Restoration in Selectively-Logged Tropical Rainforests." <i>Forest Ecology and Management</i> 549: 4. <a href="https://linkprotect.cudasvc.com/url?a=https%3a%2f%2fdoi.org%2f10.1016%2fj.foreco.2023.121414&c=E,1,CfW3xjatfMP0AonRryjLaKUmg50KQQtF2-n0UPlyW_NATChUbrP9yczm8IQKqWaiNSMtm0g3HuVHyW8N7CKr84VGIQokuIogx-7OG-AAY5epGc0i&typo=1">https://doi.org/10.1016/j.foreco.2023.121414</a>.</p><p>Philipson, Christopher D., Mark E.J. Cutler, Philip G. Brodric, Gregory P. Asne, Doreen S. Boy, Pedro Moura Costa, Joel Fiddes, et al. 2020. "Active Restoration Accelerates the Carbon Recovery of Human-Modified Tropical Forests." <i>Science</i> 369 (6505): 838–41. <a href="https://doi.org/10.1126/science.aay4490">https://doi.org/10.1126/science.aay4490</a></p><p>Philipson, Christopher D., Brodrick, Philip G., Milne, Sol, Murus, Remmy, Rasion, Joulu, Reynolds, Glen, Siew Yee, Hii, Ulok, Philip Ak, & Asner, Gregory P. (2023). 'Danum Revised' forest field plot data for twenty 0.28 hectare field plots, measured in 2017 (1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8042050">https://doi.org/10.5281/zenodo.8042050</a></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.