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8,729 results for “ranging”
Datasets: Performance Characterization of Lithium-Ion Battery Cells Within Restricted Operating Range Using an Extended Ragone Plot
<h1>Documentation</h1> <p>This repository contains the measurement data presented in <strong>"Performance Characterization of Lithium-Ion Battery Cells Within Restricted Operating Range Using an Extended Ragone Plot."</strong></p> <p>The performance characterization was conducted on three lithium-ion battery cell types, each with two samples. In the publication only datasets from the following cells are included: #1: SCiB-23Ah-01, #2: SLPB8644143-353, #3: M1B-1223-01. The measurements were performed using a Scienlab SL60/300/18BT2C battery test system in combination with a BINDER type MK 720 temperature chamber. Further details about the battery cells, the experimental setup and procedures are available in the publication. An uncertainty analysis for these measurements is included in the supplementary material of the publication.</p> <blockquote> <p><strong>Note:</strong> Please cite the referenced publication when using these datasets in your work.</p> </blockquote> <h2>Datasets Overview</h2> <p><strong>Toshiba SCiB™ 23 Ah (prismatic)<br></strong></p> <ul> <li>SCiB-23Ah-01<br> <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1<br> <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (2.7 V); 2 (2.55 V); 3 (2.4 V); 4 (2.25 V); 5 (2.1 V).</em></li> </ul> </li> </ul> </li> <li>SCiB-23Ah-02 <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (2.7 V); 2 (2.55 V); 3 (2.4 V); 4 (2.25 V); 5 (2.1 V).</em></li> </ul> </li> </ul> </li> </ul> <p><strong>Shenzen Melasta Battery SLPB8644143 (pouch)<br></strong></p> <ul> <li>SLPB8644143-353<br> <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (4.2 V).</em></li> </ul> </li> <li>PowerTemperatureTest_dis-Umax#2 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 2 (4.1 V); 3 (4.0 V); 4 (3.9 V); 5 (3.8 V).</em></li> </ul> </li> </ul> </li> <li>SLPB8644143-45 <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (4.2 V).</em></li> </ul> </li> <li>PowerTemperatureTest_dis-Umax#2 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 2 (4.1 V); 3 (4.0 V); 4 (3.9 V); 5 (3.8 V).</em></li> </ul> </li> </ul> </li> </ul> <p><strong>LithiumWerks (A123) ANR26650m1B (cylindrical)<br></strong></p> <ul> <li>M1B-1223-01<br> <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (3.6 V); 2 (3.45 V); 5 (3.3 V).</em></li> </ul> </li> <li>PowerTemperatureTest_dis-Umax#2<br> <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 3 (3.4 V); 4 (3.35 V).</em></li> </ul> </li> </ul> </li> <li> M1B-1223-02 <ul> <li>PowerTemperatureTest_dis-Umax#2 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 3 (3.4 V); 4 (3.35 V).</em></li> </ul> </li> </ul> </li> </ul> <h2>Usage instructions</h2> <ol> <li>Raw test reports are stored in the <code>test reports</code> directory as <code>*.csv</code> and <code>*.info.txt</code> files. These files contain the following measurement signals: <ul> <li>Time, Test step, ind_T, ind_U, ind_xP, E [J], Eneg [J], Epos [J], I [A], P [W], Q [As], Qneg [As], Qpos [As], T_1 [°C], T_2 [°C], T_3 [°C], T_Clima [°C], U [V]</li> </ul> </li> <li>The test reports are structured and consolidated into an HDF5 file (<code>Scienlab.h5</code>) for efficient storage and analysis. This HDF5 file can be processed using the <strong>HDF5 Data Analysis and Visualization Toolkit</strong>, provided in this repository. <ul> <li>The Python script <code>hdf5_main.py</code> is included for processing and visualizing the datasets.</li> <li>PowerTemperatureTest_dis-Umax datasets contain cyclization data from multiple constant power (CP) discharges and standardized constant current constant voltage (CCCV) charges, with varying end-of-charge voltages.</li> <li>OCVTest datasets contain low-current galvanostatic data required for performance characterization via reconstruction-based approaches. The test protocol is extensively described in the publication.</li> </ul> </li> </ol>
Dataset for "A 21 m Operation Range RFID Tag for "Pick to Light" Applications with a Photovoltaic Harvester"
<p>In the paper, a novel Radio-Frequency Identification (RFID) tag for “pick to light” applications is presented. The proposed tag architecture shows the implementation of a novel voltage limiter and a supply voltage (VDD) monitoring circuit to guarantee a correct operation between the tag and the reader for the “pick to light” application. The feasibility to power the tag with different photovoltaic cells is also analyzed, showing the influence of the illuminance level (lx), type of source light (fluorescent, LED or halogen) and type of photovoltaic cell (photodiode or solar cell) on the amount of harvested energy. Measurements show that the photodiodes present a power per unit package area for low illuminance levels (500 lx) of around 0.08 μW/mm<sup>2</sup>, which is slightly higher than the measured one for a solar cell of 0.06 μW/mm<sup>2</sup>. However, solar cells present a more compact design for the same absolute harvested power due to the large number of required photodiodes in parallel. Finally, an RFID tag prototype for “pick to light” applications is implemented, showing an operation range of 3.7 m in fully passive mode. This operation range can be significantly increased to 21 m when the tag is powered by a solar cell with an illuminance level as low as 100 lx and a halogen bulb as source light.</p> <p>This dataset contains some of the data gathered during the experimental work developed and used in the paper.</p>
Dust mass fractions for dust bins covering the 0.1 to 100 µm size range
<p>This is a file containing the dust mass fractions for dust bins covering the 0.1 to 100 µm size range calculated from the BFT-supercoarse emitted dust PSD parameterization (Meng et al., 2021 GRL submitted). </p>
Data associated with the Tectonics manuscript "Building a Young Mountain Range: Insight into the Growth of the Greater Caucasus Mountains from Detrital Zircon (U-Th)/He Thermochronology and 10Be Erosion Rates"
<p>U-Pb and U-Th/He ages of zircons from a suite of detrital catchments reported in the manuscript "Building a Young Mountain Range: Insight into the Growth of the Greater Caucasus Mountains from Detrital Zircon (U-Th)/He Thermochronology and 10Be Erosion Rates" submitted to Tectonics. Repository includes sample locations and DEMs of each sampled catchment.</p>
Range shifts of overwintering birds depend on habitat type, snow conditions and habitat specialization
<p>Data and R code accompanying the publication "Range shifts of overwintering birds depend on habitat type, snow conditions and habitat specialization"</p> <p>Bosco L, Xu Y, Deshpande P, Lehikoinen A</p> <p>2022</p> <p>---------</p> <p>The data and code to calculate range shifts based on the center of gravity are provided here.</p> <p>The RData files contains raw data from the winter bird counts with added average snow depth values downloaded from open source databases (described in the paper), 100x100km grid info (grid ID, centroid coordinates and average (geographical) coordinates).</p> <p>The csv file contains the route lengths from the winter bird count transects per habitat type.</p> <p>The R file contains the R code used to clean the data (see methods in the publication) and calculate the habitat specific center of gravity (based on bird densities) which were used to calculate shift direction and distance.</p>
Human-mouse syntenic Long Range Interactions in Neural Stem Cells
<p>This repository contains the list of human DNA regions corresponding to long-range interactions in RNA polII-mediated long-range interactions in mouse. They are named human-mouse syntenic Long Range Interactions (hmsLRI) and were obtained via synteny from mouse neonatal forebrain stem cells. They were also annotated for their overlap with DNA sequence variants (SNPs; CNVs) associated with human neurodevelopmental disease (NDD). The lists of genes identified via inferred that are potentially involved in NDD, eye-development, and traits (schizophrenia, bipolar disorder, intelligence).</p> <p>This is a resource for exploring the potential of the non-coding regions of DNA, the largest part of the genome (~98%), in NDD. Indeed, despite the numerous NDD-causative genes identified, only 42% of patients with severe developmental disorders carry pathogenic <em>de novo</em> mutations within coding sequences. More than a half of patient could be diagnosed and treated with further research and technologies, one option is studying the non-coding genome and how alterations affect genes. </p> <p>Tracks for visualization onto UCSC Genome Browser and WashU, are also provided.<br> See README for more details. </p> <p>The peer-reviewed publication for this dataset has now been published in International Journal of Molecular Science, available OA at: <a href="https://www.mdpi.com/1422-0067/23/14/7964">https://www.mdpi.com/1422-0067/23/14/7964</a>. Please cite this when using the dataset.</p>
Data used in the article: "Climate change impacts the vertical structure of marine ecosystem thermal ranges"
<p>This dataset is used in the manuscript "Climate change impacts the vertical structure of marine ecosystem thermal ranges" accepted in Nature Climate Change 2022.</p>
Data and Code for the paper 'Indication of long-range correlations governing city size'
<div> <h1>Summary</h1> <p>This repository contains the preprocessed data necessary for constructing the city network as described in the related paper, as well as the code to do the Shortest-path Fluctuation Analysis (SFA) on those networks.</p> <div> <h2>Under the <code>data</code> folder</h2> <ul> <li> <p>In the <code>Node_list</code> folder, each <em><code>CSV</code></em> file has the name convention like <code>AL_1000_node_list.csv</code>, for example, this means the table contains the list of <strong>nodes</strong> that make up the network for <strong>Austria</strong> (as country code, <code>AL</code>) at the spatial clustering distance threshold of <strong>1000</strong>m. The table has 3 columns with column names, and without row names.</p> <ul> <li> <p>Each row in the table is a record of one node, the <strong><em>node ID</em></strong> is the <code>row_id</code> of the record in the table, the first record has a row_id of <strong>0</strong>.</p> </li> <li> <p>The <strong>first</strong> column in the table is the <em><code>X</code></em> coordinate of the mass center of the node.</p> </li> <li> <p>The <strong>second</strong> column in the table is the <em><code>Y</code></em> coordinate of the mass center of the node.</p> </li> <li> <p>The <strong>third</strong> column in the table is the <strong>Size</strong> of the node, i.e., the number of pixels of this city.</p> </li> </ul> </li> <li> <p>In the <code>Edge_list</code> folder, each <em><code>TXT</code></em> file has the name convention like <code>AL_1000_edges.txt</code>, for example, this means the table contains the list of <strong>edges</strong> that make up the network for <strong>Austria</strong> (as country code, <code>AL</code>) at the spatial clustering distance threshold of <strong>1000</strong>m. The table has 2 columns without column name, without row name.</p> <ul> <li> <p>Each row in the table is a record of one edge that is composed of a pair of nodes, the <strong><em>node ID</em></strong> is the <code>row_id</code> of that node in the <strong>node_list</strong> table.</p> </li> <li> <p>The <strong>first</strong> column in the table is the <strong><em>ID</em></strong> of the node on one side of an edge.</p> </li> <li> <p>The <strong>second</strong> column in the table is the <strong><em>ID</em></strong> of the node on the other side of an edge.</p> </li> </ul> </li> </ul> </div> <div> <h2>Under the <code>code</code> folder</h2> <ul> <li> <p>The <code>SFA_args_LSPT_out.cpp</code> file contains the <strong>C++</strong> code to do the SFA calculation, detailed information can be found in the documentation of the code file.</p> </li> <li> <p>The <code>SFA_config.ini</code> file contains some configuration parameters for running the compiled program, details can also be found in the file. This file has to be put in the same folder as the compiled executable file.</p> </li> </ul> </div> </div>
TreeGOER: Tree Globally Observed Environmental Ranges
<p><strong>TreeGOER (Tree Globally Observed Environmental Ranges)</strong> is a database that documents the environmental ranges (minimum, maximum, median, mean and 5%, 25%, 75% and 95% quantiles) for 48,129 tree species and for 51 environmental variables, including 38 bioclimatic variables, 8 soil variables and 3 topographic variables. These ranges were calculated after cleaning occurrence records and standardizing species names with the <a href="https://bsapubs.onlinelibrary.wiley.com/doi/10.1002/aps3.11388">WorldFlora</a> R package to <a href="https://onlinelibrary.wiley.com/doi/10.1002/tax.12373">World Flora Online</a> or the <a href="https://www.nature.com/articles/s41597-021-00997-6">World Checklist of Vascular Plants</a> for a global GBIF occurrence download of 44,267,164 occurrences (GBIF.org 2021 <strong>GBIF Occurrence Download</strong> <a href="https://doi.org/10.15468/dl.77gcvq">https://doi.org/10.15468/dl.77gcvq</a>). The 5% and 95% quantiles were calculated separately for two methods of outlier detection and for the full data set. The process of compilation of TreeGOER with 30 arc-seconds global grid layers, two examples of BIOCLIM applications that investigated the effects of climate change on global tree diversity patterns and R scripts to repeat these analyses have been described by Kindt, R. (2023). <strong>TreeGOER: A database with globally observed environmental ranges for 48,129 tree species</strong>. Global Change Biology 29: 6303–6318. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</p> <p><strong>TreeGOER </strong>can be used in combination with the <strong>CitiesGOER</strong> database (<a href="https://doi.org/10.5281/zenodo.8175429">https://doi.org/10.5281/zenodo.8175429</a>) that documents the conditions for the same environmental variables (except elevation) for 52,602 cities with a human population ≥ 5000. An alternative to CitiesGOER is the <strong>ClimateForecasts</strong> database (<a href="../records/10776414">https://zenodo.org/records/10776414</a>), a database documenting the environmental conditions at the locations of 15,504 weather stations that was also integrated in the <strong><a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees database</a></strong> (see <a href="https://doi.org/10.1038/s41598-023-39552-1">Kindt et al. 2023</a>). <strong>TreeGOER</strong> could also be used with the <strong>TreeGOER Global Zones</strong> atlas that can be obtained from <a href="https://doi.org/10.5281/zenodo.8252756">https://doi.org/10.5281/zenodo.8252756</a>. This high-resolution atlas includes sheets with global zones for the Climatic Moisture Index (CMI) and the number of months with average temperature > 10 degrees C (Tmo10); these are zones for which presence of the 48,129 species was documented by TreeGOER.</p> <p>Changes between different versions of the databases are documented in a specific sheet in the metadata file.</p> <p><strong>The distribution of the same 48,129 species across historical and contemporary Holdridge Life Zones is available via this Zenodo archive: <a href="https://zenodo.org/records/14020914">https://zenodo.org/records/14020914</a>. The distribution of the same 48,129 species across 1931-1960, 1961-1990 and 1991-2020 Köppen-Geiger climate zones is available via this Zenodo archive: <a href="https://zenodo.org/records/14211619">https://zenodo.org/records/14211619</a>. The distribution of the same 48,129 species for Whittaker Biome Types are available via this Zenodo archive: <a href="https://zenodo.org/records/14908943">https://zenodo.org/records/14908943</a>. Globally observed environmental ranges as in TreeGOER are available since 14<sup>th</sup> June 2024 for another set of species (including several bamboo and hybrid tree species not included in GlobalTreeSearch) via this Zenodo archive: <a href="../records/11652972">https://zenodo.org/records/11652972</a>. <br></strong></p> <p> </p> <p>The development of TreeGOER was supported by the <strong>Darwin Initiative</strong> to project DAREX001 of <em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em>, by Norway’s <strong>International Climate and Forest Initiative</strong> through the Royal Norwegian Embassy in Ethiopia to the <em>Provision of Adequate Tree Seed Portfolio</em> project in Ethiopia, and by the <strong>Green Climate Fund</strong> through the IUCN-led <em>Transforming the Eastern Province of Rwanda through Adaptation</em> project. When using TreeGOER in your work, cite the publication (Kindt <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">2023</a>) as well as this repository using the DOI (<a href="https://doi.org/10.5281/zenodo.7922927">https://doi.org/10.5281/zenodo.7922927</a>).</p> <p> </p>
Data set for "Diverse long-range axonal projections of excitatory layer 2/3 neurons in mouse barrel cortex"
<p>Data set for: Yamashita T, Vavladeli A, Pala A, Galan K, Crochet S, Petersen SSA, Petersen CCH (2018) Diverse long-range axonal projections of excitatory layer 2/3 neurons in mouse barrel cortex. Front Neuroanat 12: 33. https://doi.org/10.3389/fnana.2018.00033</p> <p>There are 25 files in this data upload:</p> <p>1. '2018_Yamashita_FrontNeuroanat.pdf' - this a pdf version of the online publication.</p> <p>2. 'Yamashita_Figure2_Quantification.xlsx' - this is a Microsoft Excel file giving the locations of high density axonal projections from layer 2/3 pyramidal neurons in the mouse C2 barrel column in the coordinate frame of Paxinos & Franklin (2001) The mouse brain in stereotaxic coordinates. Academic Press. The data are plotted in Figure 2 of Yamashita et al., 2018.</p> <p>3. 'Yamashita_Figure7_Quantification.xlsx' - this is a Microsoft Excel file giving the dendritic length, number of dendrites, number of dendritic nodes and total axonal length, as well as the axonal length in the different projection zones for each reconstructed neuron. The data are plotted in Figure 7 of Yamashita et al., 2018.</p> <p>4. 'Yamashita_SupMov1_S2P_AP049.mov' - this is a QuickTime video file, showing the 3D structure of neuron AP049 featured in Figure 3 of Yamashita et al., 2018.</p> <p>5. 'Yamashita_SupMov2_M1P_TY308.mov' - this is a QuickTime video file, showing the 3D structure of neuron TY308 featured in Figure 5 of Yamashita et al., 2018.</p> <p>6. 'AV198.zip' - this zipped folder contains data relating to mouse AV198: a) 'AV198_stack.tif' the z-stack of whole-brain fluorescence images from expression of tdTomato in layer 2/3 neurons of the C2 barrel column of mouse AV198. b) 'AV198_ROI_Box.zip' can be loaded into FIJI (https://fiji.sc) and indicates projection regions by a box. c) 'AV198_ROI_Point.zip' can be loaded into FIJI (https://fiji.sc) and indicates projection regions by a point. d) 'AV198_Paxinos' is a folder showing the coronal fluorescent brain sections in pdf format overlaid on the equivalent drawing from Paxinos & Franklin (2001) The mouse brain in stereotaxic coordinates. Academic Press.</p> <p>7. 'AV199.zip' - same as 'AV198.zip' but for mouse AV199.</p> <p>8. 'AV201.zip' - same as 'AV198.zip' but for mouse AV201.</p> <p>9. 'AV202.zip' - same as 'AV198.zip' but for mouse AV202.</p> <p>10. 'AV203.zip' - same as 'AV198.zip' but for mouse AV203.</p> <p>11. 'AP042.ASC' - Neurolucida (http://www.mbfbioscience.com/neurolucida) data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP042. Brain contours are also traced.</p> <p>12. 'AP044.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP044. Brain contours are also traced.</p> <p>13. 'AP046.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP046. Brain contours are also traced.</p> <p>14. 'AP047.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP047. Brain contours are also traced.</p> <p>15. 'AP049.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP049. Brain contours are also traced.</p> <p>16. 'TY220.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY220. Brain contours are also traced.</p> <p>17. 'TY288.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY288. Brain contours are also traced.</p> <p>18. 'TY300.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY300. Brain contours are also traced.</p> <p>19. 'TY302.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY302. Brain contours are also traced.</p> <p>20. 'TY308.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY308. Brain contours are also traced.</p> <p>21. 'TY310.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY310. Brain contours are also traced.</p> <p>22. 'TY337.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY337. Brain contours are also traced.</p> <p>23. 'TY345.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY345. Brain contours are also traced.</p> <p>24. 'TY367.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY367. Brain contours are also traced.</p> <p>25. 'TY369.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY369. Brain contours are also traced.</p>
MengxiaoZhao_Using sky-wave echoes information to extend HFSWR's maximum detection range
<p>1. for Figure 2,3,4</p> <p>a. 'Ground Attention.mat' —— The data of ground wave attenuation<br> [<br> f0 —— four frequency<br> L —— the ground distance<br> Attenuation: 501*4 —— the ground wave attenuation for four different frequency<br> ]</p> <p>b. 'Skywave Attention.mat' —— The data of skywave attenuation<br> [<br> f0 —— four frequency<br> L —— the ground distance<br> attenuation: 34*4 —— the skywave attenuation for four different frequency<br> ]</p> <p>c. 'Path attenuation of 5Mhz.mat' —— four paths' attenuation of 5Mhz<br> [<br> L —— Ground distance<br> path1 —— the attenuation of path 1<br> path23 —— the attenuation of path 2&3<br> path4 —— the attenuation of path4<br> ]</p> <p><br> 2. for Figure 7 - simulation result</p> <p>a. 'simulation_echoes data.mat' —— the data of simulation targets' echoes</p> <p>b. 'Figure7_data.mat' —— the simulation results<br> [<br> data —— Doppler*Range<br> Doppler —— the axis of Doppler <br> Range —— the axisof Range <br> ]</p> <p>3. for Figure 8,9,10 - actual data processing results. We give 5 batches of echoes' data and the final result of Figure 9.</p> <p>a. 'TCDat1.mat','TCDat2.mat','TCDat3.mat','TCDat4.mat','TCDat5.mat',<br> —— the echoes' data</p> <p>b. 'Figure9_data.mat' —— the final result of Figure 9. <br> [<br> data ——Doppler*Range<br> axist_Doppler —— the axisof Doppler<br> axist_Range —— the axisof Range<br> counT —— the number of detections<br> mTgt —— the parameters of detections<br> ]</p> <p>4. 'parameters.txt' —— the parameters of simulation data and actual data</p> <p><br> </p>
Supporting Information for "An empirical modification of the force field approach to describe the modulation of galactic cosmic rays close to Earth in a broad range of rigidities"
<p>This supporting information provides the Data Set S1 used to produce Fig. 6 in <strong>"An empirical modification of the force field approach to describe the modulation of galactic cosmic rays close to Earth in a broad range of rigidities"</strong> (Gieseler et al., 2017). It can be used to calculate the rigidity-dependent solar modulation potential <span class="math-tex">\(\phi(P)\)</span> for monthly intervals from 1973-2017 following Eq. 10 in Gieseler et al. (2017).</p> <p>If you use this data, please refer to and cite <strong>BOTH</strong> following publications:</p> <ul> <li>Gieseler, J., B. Heber, and K. Herbst, <em>An empirical modification of the force field approach to describe the modulation of galactic cosmic rays close to Earth in a broad range of rigidities</em>, J. Geophys. Res., 2017 (doi:10.1002/2017JA024763).</li> <li>Usoskin, I. G., G. A. Bazilevskaya, and G. A. Kovaltsov, <em>Solar modulation parameter for cosmic rays since 1936 reconstructed from ground-based neutron monitors and ionization chambers</em>, J. Geophys. Res., 2011 (doi:10.1029/2010JA016105).</li> </ul> <p>This data set contains the solar modulation potential values in MV for monthly intervals from 1973-2017 derived from the proton proxies IMP-8 He and ACE/CRIS C (Phi_pp), and from Usoskin et al. (2011) as provided by http://cosmicrays.oulu.fi/phi/phi.html (Phi_Uso11). The uncertainties of Phi_pp are given in column 4, those of Phi_Uso11 are 26 MV for the observed period. The LIS used to calculate the modulation potentials is that from Burger et al. (2000) as given by Usoskin et al. (2005).</p> <p>Column 1: Fractional year (start of interval)<br> Column 2: Month<br> Column 3: Phi_pp /MV<br> Column 4: Uncertainty of Phi_pp /MV<br> Column 5: Phi_Uso11 /MV</p> <p>Data also available at http://www.ieap.uni-kiel.de/et/ag-heber/cosmicrays</p>
Tulajja, Salt Range, Pakistan.
<p>Tulajja, Salt Range, Pakistan. Collection of documentary images of the ruins located at 32°29'17"N 72°15'48"E.</p>
Datasets of pushing complexometric titrations in the nanomolar range thanks to SERS
<p>These datasets contains the spectral information used in a publication regarding the implementation of complexometric titrations of copper monitored using Surface Enhanced Raman Spectroscopy (SERS). In this study the sensitivity of a classic complexometric titration system for Cu2+ is pushed into the nanomolar regime thanks to SERS monitoring of the endpoint.</p> <p>All the data are organised as column vectors. </p> <p>The data consists of three csv files with semi-column separators.</p> <p>“AN334_and_AN338_laser_532nm_1microM_copper_variation_of_PAN_all_data_corrected_serie_11_points” correspond to spectral data acquired under 532 nm irradiation with variation of the PAN (100, 50 and 25 nM) concentration for 1 µM of copper.</p> <p>“AN335_and_AN341_laser_532nm_500nM_copper_variation_of_PAN_all_data_corrected_serie_11_points” correspond to spectral data acquired under 532 nm irradiation with variation of the PAN (100, 50 and 25 nM) concentration for 500 nM of copper.</p> <p>“AN336_and_AN344_laser_532nm_250nM_copper_variation_of_PAN_all_data_corrected_serie_11_points” correspond to spectral data acquired under 532 nm irradiation with variation of the PAN (100, 50 and 25 nM) concentration for 250 nM of copper.</p> <p>Each dataset has 90 685 rows corresponding to 1 irradiation used (532 nm), 2 experimental replicate titration series including 1 titration + 1 blank titration for each titration and for 3 differents concentrations of PAN and 11 titration steps per titration series. For the 3 copper concentrations we obtain 396 samples.</p> <p>The columns vectors consist of a first sub-vector of spectral descriptors (identity of spectra, identity and composition of measurement samples and conditions of spectral acquisition), followed by a sub-vector of baseline-subtracted spectral intensities.</p> <p>The meaning of each column is in the .docx file titled "Structure of AN334-AN338 to AN336-AN344 datasets”.</p> <p>The other two csv fils with semi-column separators "AN339_AN342_AN345_laser_638nm_variation_of_copper_and_PAN_all_data_corrected_serie_11_points" and "AN340_AN343_AN346_laser_785nm_variation_of_copper_and_PAN_all_data_corrected_serie_11_points" correspond to spectral data acquired under 638 and 785 nm irradiation respectively. In both files, the data correspond to the 11-point experiments with PAN variation (100, 50 and 25 nM) and copper variation (1 µM, 500 and 250 nM).</p> <p><span>Finally, the data consists of one csv files with semi-column separators.</span></p> <p><span>“AN384_to_AN386_Laser_532_638_785nm_all_data_corrected_titration_serie_20_points” correspond to spectral data acquired under 532, 638 and 785 nm irradiation.</span></p> <p><span>The dataset has 111 241 rows corresponding to 3 irradiations used (532, 638 and 785), 2 replicate titration series + 1 blank titration and 20 titration steps per titration series.</span><span></span></p> <p><span>The columns vectors consist of a first sub-vector of spectral descriptors (identity of spectra, identity and composition of measurement samples and conditions of spectral acquisition), followed by a sub-vector of baseline-subtracted spectral intensities.</span></p> <p>The meaning of each column is in the .docx file titled "Structure of AN384 to AN386 datasets”.</p>
Experimental measurements of the H2-H2 and H2-He binary absorption coefficients in the [2500 ,5900] cm^-1 spectral range from 120 to 500 K
<p>Binary absorption coefficients (BAC) of the Collision-Induced Absorption (CIA) fundamental band of H2 are given in tabular form for both the H2-H2 and H2-He contributions for seven temperatures in the [120, 500] K range and [2500, 5900] cm^-1 spectral range.</p> <p>Tables are composed of three columns. The first column represents the wavenumber in cm^-1, in the second column the binary absorption coefficients in cm^5 molecule^-2 are collected, and the third column contains the accuracy of the binary coefficients in cm^5 molecule^-2. Every column is delimited with a comma. </p> <p>The accuracy can be supposed to be of three significant digits, even if the trailing zeros are not displayed. </p> <p> </p>
Data set for "Cell class-specific long-range axonal projections of neurons in mouse whisker-related somatosensory cortices"
<p>Data set for: Liu Y, Bech P, Tamura K, Délez LT, Crochet S, Petersen CCH (2024) Cell class-specific long-range axonal projections of neurons in mouse whisker-related somatosensory cortices. eLife 13: RP97602. https://doi.org/10.7554/eLife.97602</p> <p>There are 3 files in this upload:</p> <p>1. The file named "2024_Liu_eLife.pdf" is the Open Access pdf of the online publication in eLife.</p> <p>2. The file named "Liu_anatomy_data_code.zip" (~35 GB) is a zipped version of a folder "Liu_anatomy_data_code" (~111 GB), which contains the anatomical data analysed in the study along with the Python codes used to generate the published figures 1-7 and their associated figure supplements. </p> <p>3. The file named "Liu_function_data_code.zip" (~10 GB) is a zipped version of a folder "Liu_function_data_code" (~35 GB), which contains the functional data analysed in the study along with the Python codes used to generate the published figure 8 and its associated figure supplement. </p> <p>After unzipping, the Python codes should run as a Jupyter notebook (anatomy .ipynb code) or Python code (function .py code) in Anaconda.</p>
Supplementary material for "High turn-over rates at the upper range limit and elevational source-sink dynamics in a widespread songbird"
<p><strong>Abstract</strong></p> <p>The formation of an upper distributional range limit for species breeding along mountain slopes is often based on environmental gradients resulting in changing demographic rates towards high elevations. However, we still lack an empirical understanding of how the interplay of demographic parameters forms the upper range limit in highly mobile species. Here, we study apparent survival and within-study area dispersal over a 700 m elevational gradient in barn swallows (<em>Hirundo rustica</em>) by using 15 years of capture-mark-recapture data. Annual apparent survival of adult breeding birds decreased while breeding dispersal probability of adult females, but not males increased towards the upper range limit. Individuals at high elevations dispersed to farms situated at elevations lower than would be expected by random dispersal. These results suggest higher turn-over rates of breeding individuals at high elevations, an elevational increase in immigration and thus, within-population source-sink dynamics between low and high elevations. The formation of the upper range limit therefore is based on preference for low-elevation breeding sites and immigration to high elevations. Thus, shifts of the upper range limit are not only affected by changes in the quality of high-elevation habitats but also by factors affecting the number of immigrants produced at low elevations.</p>
Data from: Moth species richness and diversity decline in a 30-year time series in Norway, irrespective of species' latitudinal range extent and habitat
<p>Data from:</p> <p>Burner, R., V. Selås, S. Kobro, R. Jacobsen, A. Sverdrup-Thygeson. 2021. Moth species richness and abundance decline in a 30-year time series, irrespective of species’ latitudinal range extent and habitat. <em>Journal of Insect Conservation</em><br> </p> <p>Current contact info for corresponding author: Ryan C. Burner, rburner[at]usgs.gov</p> <p> </p> <p>These data consist of a 30-year time series (1984 to 2013) of moth captures from a single site in southeast Norway, along with trait data for many of the species and climate data for the site. The moths were collected and identified by Sverre Kobro for the entire 30-year period and we are grateful for his efforts. </p> <p> </p> <p>Abstract from manuscript:</p> <p><strong>Introduction</strong></p> <p>Insects are reported to be in decline around the globe, but long-term datasets are rare. The causes of these trends are elusive, with land use change and climate change among the top candidates. Yet if species traits can predict rates of population change, this can help identify underlying mechanisms. If climate change is important, for example, northern species may decline as southern species expand. Land use changes, however, may impact species that rely on certain habitats.</p> <p><strong>Aims and Methods</strong></p> <p>We present 30 years of moth captures (comprising 85,149 individuals of 885 species) from a site in southeastern Norway to test for population trends that are correlated with species traits. We use time series analyses and joint species distribution models combined with local climate and habitat data.</p> <p><strong>Results and Discussion</strong></p> <p>Species richness and abundance declined by 10.1% and 13.8% per decade, respectively. Capture rates declined for 19% of species during this time as well, though 6% have increased. Annual summer weather is correlated with annual rates of abundance change for many species. But, opposite to a general expectation, many species in our study responded negatively to increasing summer temperatures. Surprisingly, neither species’ northern range limits nor the habitat in which their primary food plants grow are strong predictors of their rates of change, or their responses to climatic factors. However, species with more southerly distributions are less likely to be declining. Complex and indirect effects of both land use and climate change may play a role in these declines.</p> <p><strong>Implications for insect conservation</strong></p> <p>Our results provide additional evidence for long-term declines in insect abundance. The multifaceted causes of population changes may limit the ability of species traits to reveal which species are most at risk. </p> <p> </p> <p><strong>ACKNOWLEDGEMENTS</strong></p> <p>Thanks to J. Fjelddalen, who helped with geometrid moth identifications. This project was supported by internal funding from the Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences.</p> <p> </p>
A curated database of fungal pathogens and their host range
<p>This database contains a manually curated set of human, animal and plant pathogens, annotated with their confirmed host range and relevant sources. In addition to that, we include additional sets of plant-associated fungi (which may include non-pathogens), as well as fungi with an automatically assigned, putative human, animal or plant host. The labelled fungal species are linked to their representative GenBank genomes wherever possible. Genomes that were screened, but no label was found, are also included.</p> <p><strong>[Last update on: 11 Dec 2022]</strong><br> [Home page: <a href="https://dacs-hpi.gitlab.io/pathogenic-fungi/">https://dacs-hpi.gitlab.io/pathogenic-fungi/</a>]<br> <br> The database is stored in a flat-file format. All metadata are stored in all_data_[date].csv, and all_data_[date].rds contains the same data in a compressed format that can be easily loaded in R. The database was first compiled on 9 Oct 2021 (v1.0), and then updated on 2 Jan 2022 (v1.1) and 11 Dec 2022 (v1.2).</p> <p>The core database is limited to manually confirmed human, animal and plant pathogens with available genomes as of 9 Oct 2021. Those data are a subset of all_data, and are stored in core_fungal_pathogens.csv and core_fungal_pathogens.rds.</p> <p>The temporal-test subset contains confirmed pathogens with genomes added to GenBank between 9 Oct 2021 and 2 Jan 2022.</p> <p>You may also be interested in trained neural network models predicting pathogenic potentials of novel fungi from DNA sequences (<a href="https://zenodo.org/record/5711877">https://zenodo.org/record/5711877</a>) and simulated Illumina read sets used to train them (<a href="https://zenodo.org/record/5846397">https://zenodo.org/record/5846397</a>).<br> <br> See also the preprint: <a href="https://www.biorxiv.org/content/10.1101/2021.11.30.470625">https://www.biorxiv.org/content/10.1101/2021.11.30.470625</a> and <strong>the paper</strong> presented at ECCB '22 and published in <em>Bioinformatics:</em> <a href="https://doi.org/10.1093/bioinformatics/btac495">https://doi.org/10.1093/bioinformatics/btac495.</a></p>
A collection of fully-annotated soundscape recordings from the southern Sierra Nevada mountain range
<p>This collection contains 100 soundscape recordings of 10 minutes duration, which have been annotated with 10,296 bounding box labels for 21 different bird species from the Western United States. The data were recorded in 2015 in the southern end of the Sierra Nevada mountain range in California, USA. This collection has been featured as test data in the 2020 BirdCLEF and Kaggle Birdcall Identification competition and can primarily be used for training and evaluation of machine learning algorithms.</p> <p><strong>Data collection</strong></p> <p>The recordings were made in Sequoia and Kings Canyon National Parks, two contiguous national parks in the southern Sierra Nevada mountain range in California, USA. The focus of the acoustic study was the high-elevation region of the Parks; specifically, the headwater lake basins above 3,000 km in elevation. The original intent of the study was to monitor seasonal activity of birds and bats at lakes containing trout and lakes without trout, because the cascading impacts of trout on the adjacent terrestrial zone remain poorly understood. Soundscapes were recorded for 24 h continuously at 10 lakes (5 fishless, 5 fish-containing) throughout Sequoia and Kings Canyon National Parks during June-September 2015. Song Meter SM2+ units (Wildlife Acoustics, USA) powered by custom-made solar panels were used to obviate the need to swap batteries, due to the recording locations being extremely difficult to access. Song Meters continuously recorded mono-channel, 16-bits uncompressed WAVE files at 48 kHz sampling rate. For this collection, recordings were resampled at 32 kHz and converted to FLAC.</p> <p><strong>Sampling and annotation protocol</strong></p> <p>A total of 100 10-minute segments of audio between July 9 and 12, 2015 from morning hours (06:10-09:10 PDT) from all 10 sites were selected at random. Annotators were asked to box every bird call they could recognize, ignoring those that are too faint or unidentifiable. Every sound that could not be confidently assigned an identity was reviewed with 1-2 other experts in bird identification. To minimize observer bias, all identifying information about the location, date and time of the recordings was hidden from the annotator. Raven Pro software was used to annotate the data. Provided labels contain full bird calls that are boxed in time and frequency. In this collection, we use eBird species codes as labels, following the 2021 eBird taxonomy (Clements list). Unidentifiable calls have been marked with “????” and were added as bounding box labels to the ground truth annotations. Parts of this dataset have previously been used in the 2020 BirdCLEF and Kaggle Birdcall Identification competition.</p> <p><strong>Files in this collection</strong></p> <p>Audio recordings can be accessed by downloading and extracting the “soundscape_data.zip” file. Soundscape recording filenames contain a sequential file ID, recording date and timestamp in PDT (UTC-7). As an example, the file “HSN_001_20150708_061805.flac” has sequential ID 001 and was recorded on July 8th 2015 at 06:18:05 PDT. Ground truth annotations are listed in “annotations.csv” where each line specifies the corresponding filename, start and end time in seconds, low and high frequency in Hertz and an eBird species code. These species codes can be assigned to scientific and common name of a species with the “species.csv” file. The approximate recording location with longitude and latitude can be found in the “recording_location.txt” file.</p> <p><strong>Acknowledgements </strong></p> <p>Compiling this extensive dataset was a major undertaking, and we are very thankful to the domain experts who helped to collect and manually annotate the data for this collection (individual contributors in alphabetic order): Anna Calderón, Thomas Hahn, Ruoshi Huang, Angelly Tovar</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.