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15,560 results for “How long”
Translated Emission Pathways (TEPs): Long-Term Simulations of COVID-19 CO2 Emissions and Thermosteric Sea Level Rise Projections - Supplementary Materials
<p>Supplementary materials for Gonzalez, A. R., & Lin, T. (2022). Translated Emission Pathways (TEPs): Long-Term Simulations of COVID-19 CO<sub>2</sub> Emissions and Thermosteric Sea Level Rise Projections. <em>Earth's Future</em>. In Press.</p> <p><strong>Summary: This study introduces climate science to a broader audience by presenting an accessible research framework and environmental data related to the ongoing COVID-19 pandemic. A series of translated emission pathways (TEPs) were constructed based on the CO<sub>2</sub> emission patterns from the various phases of COVID-19 response. In addition to resembling the forcing scenarios used within climate research, a thermosteric sea level rise analysis was incorporated to further emphasize the environmental benefits that can be obtained from long-term sustainability. As a promising start for including the general public in climate change discussion, this research promotes collective environmental action that mirrors the recommendations of the scientific community.</strong></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-Driven Study of Long-Term Gaming Experience (Tribalwars)
<p>This is the accompanying dataset for our Paper "Data-Driven Study of Long-Term Gaming Experience" at the 14th International Conference on Quality of Multimedia Experience (QoMEX) - Please city this work if you use this dataset.</p> <p>This dataset contains hourly crawled player metadata of the Tribalwars casual game game round "dep13".</p> <p>With this dataset and a set of possible tooling, we hope to provide a foundation into further data-driven QoE studies.</p> <p>You will find further information about prerequisites and the dataset within the <strong>eval.ipynb</strong>.</p>
Continuous Long-term Wi-Fi Fingerprinting Dataset for Indoor Positioning (full version)
<p>Database with Wi-Fi samples (RSSI measurements) collected from several Raspberry Pi (RPi) 3B+ devices continuously over 2+ years. The database includes the long-term dataset from the RPi devices (with 7,435,398 Wi-Fi samples), as well as 12 site-survey datasets (with 11,140 Wi-Fi samples) conducted in this period. The site-surveys were also conducted with a RPi 3B+.</p> <p>The measurements obtained from the RPi 3B+ Wi-Fi interface include the list of detected APs, their signal strength (RSSI) and transmission channel. The list has APs from the 2.4GHz and 5GHz bands because it supports IEEE 802.11.b/g/n/ac wireless LAN. </p> <p>These data were collected at a university building, between 19 Feb. 2019 and 25 Mar. 2021.</p> <p>The supporting material includes the Python scripts to parse and analyse the data by generating various plots. It also includes the locations of the monitoring devices and the list of reference points considered in the site-surveys.</p> <p> </p> <p>A detailed description of this dataset and the data collection process can be found here:</p> <p>Silva I, Pendão C, Moreira A. Collection of a Continuous Long-Term Dataset for the Evaluation of Wi-Fi-Fingerprinting-Based Indoor Positioning Systems. <em>Sensors</em>. <strong>2022</strong>; 22(22):8585. <a href="https://doi.org/10.3390/s22228585">https://doi.org/10.3390/s22228585</a></p> <p> </p> <p>When using this dataset, please add a citation to the paper above or this citation:</p> <p>Silva, I., Pendão, C., & Moreira, A. (2022). Continuous Long-term Wi-Fi Fingerprinting Dataset for Indoor Positioning (full version) (1.1.0) [Data set]. Zenodo. <a href="Silva, I., Pendão, C., & Moreira, A. (2022). Continuous Long-term Wi-Fi Fingerprinting Dataset for Indoor Positioning (full version) (1.1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6928554">https://doi.org/10.5281/zenodo.6928554</a> </p> <p> </p> <p>The following papers have used this dataset for quantifying radio map degradation and overcoming radio map degradation in Wi-Fi fingerprinting:</p> <ul> <li>I. Silva, C. Pendão, J. Torres-Sospedra and A. Moreira, "Quantifying the Degradation of Radio Maps in Wi-Fi Fingerprinting," <em>2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN)</em>, Lloret de Mar, Spain, 2021, pp. 1-8, doi: 10.1109/IPIN51156.2021.9662558.</li> <li>I. Silva, C. Pendão, J. Torres-Sospedra and A. Moreira, "Overcoming Radio Map Degradation in Wi-Fi-based Positioning Systems," <em>2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)</em>, Nuremberg, Germany, 2023, pp. 1-6, doi: 10.1109/IPIN57070.2023.10332545.</li> </ul>
Sentinel-3 NDVI ARD and Long Term Statistics (1999-2019) from the Copernicus Global Land Service over Lombardia
<p>Sentinel-3 NDVI Analysis Ready Data (ARD) (C_GLS_NDVI_20220101_20220701_Lombardia_S3_2.nc) product provided by the Copernicus Global Land Service [3]. The file C_GLS_NDVI_20220101_20220701_Lombardia_S3_2_masked.nc is derived from C_GLS_NDVI_20220101_20220701_Lombardia_S3_2.nc but values have been scaled (raw_value * ( 1/250) - 0.08) and values lower then -0.08 and greater than 0.92 have been removed (set to missing values).</p> <p>The original dataset can also be discovered through the OpenEO API[5] from the CGLS distributor VITO [4]. Access is free of charge but an <a href="https://aai.egi.eu/">EGI registration</a> is needed.</p> <p>The file called Italy.geojson has been created using the Global Administrative Unit Layers <a href="https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/9c35ba10-5649-41c8-bdfc-eb78e9e65654">GAUL G2015_2014</a> provided by FAO-UN (see <a href="https://data.apps.fao.org/map/catalog/srv/api/records/9c35ba10-5649-41c8-bdfc-eb78e9e65654/attachments/GAUL2015_Documentation.zip">Documentation</a>). It only contains information related to Italy.</p> <p> </p> <p>Further info about drought indexes can be found in the Integrated Drought Management Programme [5]</p> <p>[1] <a href="https://www.sciencedirect.com/science/article/abs/pii/027311779500079T">Application of vegetation index and brightness temperature for drought detection</a> [2] <a href="https://en.wikipedia.org/wiki/Normalized_difference_vegetation_index">NDVI</a> [3] <a href="https://land.copernicus.eu/global/index.html">Copernicus Global Land Service</a> [4] <a href="https://vito.be/en">Vito</a> [5] <a href="https://openeo.org/">OpenEO</a> [5] <a href="https://www.droughtmanagement.info/indices">Integrated Drought Management</a></p>
Quantitative Representativeness and Constituency of the Long-Term Agroecosystem Research Network
<p><strong>Data Description</strong>:</p> <p>The USDA Long-Term Agroecosystem Research (LTAR) Network coordinates agricultural research across 18 research sites in the conterminous United States (CONUS). However, it is unclear how well these sites represent the totality of agricultural working lands within the CONUS. Therefore, we performed a quantitative analysis of the 18 sites, based on 15 climatic and edaphic characteristics, to produce maps of representativeness and constituency across the CONUS. Representativeness shows how well the combination of environmental drivers at each CONUS location was represented by the LTAR sites’ environments, while constituency shows which LTAR site was the closest match for each location.</p> <p>Files in collection (22):</p> <p>Collection contains 11 geospatial rasters and 11 PNGs visualizing them.</p> <p>TIF files:</p> <p>├── conus_ltar_constituency_workinglands.tif [Constituency of LTAR network]<br> ├── conus_ltar_representativeness_workinglands.tif [Representativeness of LTAR network]<br> ├── conus_ltar_v5.pc1.tif [Principal Component 1]<br> ├── conus_ltar_v5.pc2.tif [Principal Component 2]<br> ├── conus_ltar_v5.pc3.tif [Principal Component 3]<br> ├── conus_ltar_v5.pc4.tif [Principal Component 4]<br> ├── conus_ltar_v5.pc5.tif [Principal Component 5]<br> ├── conus_ltar_v5.pc6.tif [Principal Component 6]<br> ├── conus_ltar_v5.pc7.tif [Principal Component 7]<br> ├── LTAR_NEON_LTER_bestnetwork_workinglands.tif [Raster identifying best network, among LTAR, NEON, LTER, representing the location]<br> └── LTAR_NEON_LTER_representativeness_workinglands.tif [Representativeness of combined LTAR + NEON + LTER networks]</p> <p>PNG files:</p> <p>├── conus_ltar_constituency_workinglands.png [Constituency of LTAR network]<br> ├── conus_ltar_representativeness_workinglands.png [Representativeness of LTAR network]<br> ├── conus_ltar_v5.pc1.png [Principal Component 1]<br> ├── conus_ltar_v5.pc2.png [Principal Component 2]<br> ├── conus_ltar_v5.pc3.png [Principal Component 3]<br> ├── conus_ltar_v5.pc4.png [Principal Component 4]<br> ├── conus_ltar_v5.pc5.png [Principal Component 5]<br> ├── conus_ltar_v5.pc6.png [Principal Component 6]<br> ├── conus_ltar_v5.pc7.png [Principal Component 7]<br> ├── LTAR_NEON_LTER_bestnetwork_workinglands.png [Raster identifying best network, among LTAR, NEON, LTER, representing the location]<br> └── LTAR_NEON_LTER_representativeness_workinglands.png [Representativeness of combined LTAR + NEON + LTER networks]</p> <p><strong>Data format</strong>:</p> <p>Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution, while the geospatial visualizations are provided in PNG format.</p> <p><strong>Geospatial projection</strong>: </p> <pre><code class="language-bash">GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS["wgs84", DATUM["WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]] </code></pre> <p><strong>Category labels for Constituency data</strong>:</p> <table> <caption> </caption> <thead> <tr> <th scope="col">Cat</th> <th scope="col">LTAR Siite</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>Archbold-University of Florida</td> </tr> <tr> <td>2</td> <td>Central Mississippi River Basin</td> </tr> <tr> <td>3</td> <td>Central Plains Experimental Range</td> </tr> <tr> <td>4</td> <td>Eastern Corn Belt</td> </tr> <tr> <td>5</td> <td>Great Basin</td> </tr> <tr> <td>6</td> <td>Gulf Atlantic Coastal Plain</td> </tr> <tr> <td>7</td> <td>Jornada Experimental Range</td> </tr> <tr> <td>8</td> <td>Kellogg Biological Station</td> </tr> <tr> <td>9</td> <td>Lower Chesapeake Bay</td> </tr> <tr> <td>10</td> <td>Lower Mississippi River Basin</td> </tr> <tr> <td>11</td> <td>Northern Plains</td> </tr> <tr> <td>12</td> <td>Platte River High Plains Aquifer</td> </tr> <tr> <td>13</td> <td>R.J. Cook Agronomy Farm</td> </tr> <tr> <td>14</td> <td>Southern Plains</td> </tr> <tr> <td>15</td> <td>Texas Gulf</td> </tr> <tr> <td>16</td> <td>Upper Chesapeake Bay</td> </tr> <tr> <td>17</td> <td>Upper Mississippi River Basin</td> </tr> <tr> <td>18</td> <td>Walnut Gulch Experimental Watershed</td> </tr> </tbody> </table> <p><strong>Paper describing data and methods</strong>:</p> <p>Kumar, J., Coffin, A. W., Baffaut, C., Ponce-Campos, G. E., Witthaus, L., & Hargrove, W. W. (2023). Quantitative Representativeness and Constituency of the Long-Term Agroecosystem Research Network and Analysis of Complementarity with Existing Ecological Networks. In Environmental Management. Springer Science and Business Media LLC. https://doi.org/10.1007/s00267-023-01834-9</p> <p> </p>
Data to support the publication "Impact of agricultural management on soil aggregates and associated organic carbon fractions: Analysis of long-term experiments in Europe"
<p><strong>Raw data:</strong> Experimental plot ids and information, mass distribution of all aggregate fractions after wet sieving, Sand content of each fraction to conduct the sand correction, mass distribution of all fractions after isolating the micro-aggregates held within the macroaggregates, yields per treatment, carbon content per fraction (raw data)</p> <p><strong>All data per plot: </strong>SOC content, MAOM and POM content of each fraction presented in the fractionation scheme included in the manuscript, together with the mass of the relative fractions. </p> <p> </p>
Effects of audio-motor training on spatial representations in long-term late blindness
<p>Datasets for behavioural data:</p> <p>-<em>Auditory horizontal localization task</em></p> <p>- <em>Auditory vertical localization task</em></p> <p>- <em>Position matching task</em></p> <p>- <em>Proprioceptive midline task</em></p> <p>Dataset for EEG data:</p> <p>- Spatial bisection task: mean ERP amplitude for 50-90ms timew window for each trial, separately for condition, session and roi</p> <p> </p>
Data from: Male long-distance migrant turned sedentary; The West European pond bat (Myotis dasycneme) alters their migration and hibernation behaviour
<p>Winter survey data, temperature data and mark recapture data of <em>Myotis dasycneme</em>. This study aimed to better understand the migration, mating and hibernation choices of the pond bat.</p> <p> </p> <p>The study area covered the whole of the Netherlands, Belgium and East Frisia (northwest Germany). We defined two study periods, data collected between 1930 and 1980 (Sluiter and van Heerdt) and data between 1980 and 2015 (Haarsma). All available mark and recovery data (ringing) of both the historical and recent migration research were digitized. Observations include location and date of capture, species, sex and ring number. The latest observations in the recent dataset (Haarsma) also include biometric measurements (forearm length, body mass) and information about age and reproductive status. These biometric measurements show that male pond bats are on average smaller and lighter than females (body mass (g)/ forearm length (mm) females: 18.9/47.1, males: 16.4/46.4). The dataset shows changes in the fat mass of both sexes during a year.</p> <p>This study also compares migration data with winter monitoring survey data. We selected winter roosts with three or more records of three or more pond bats in one or both of the study periods. Only data from sites with long-term data series (from the hibernacula in the Dutch provinces of Zuid-Holland, Gelderland and Limburg) were used to analyse trends and annual abundance. Our selection included 59 limestone mines in the province of Limburg and 16 WOII bunkers in Gelderland and 38 in Zuid-Holland. We divided the sites into 'core' and 'satellite' sites depending on the timing of first colonization.</p> <p> </p> <p><strong>Bunker limestone mine microclimate</strong></p> <p> </p> <p>Radiation temperature: radiation temperature of the wall, measured with a non-contact infrared thermometer</p> <p>How many bats: the group size of each bat/ group of bats observed, categorized as alone and group.</p> <p>Where: the hanging location of the observed bat, categorized as hidden (in crevice) or free (free on ceiling or wall)</p> <p>Date: date of the observation</p> <p>Xy-coord: The coordinates of the entrance of the bunker or limestone mine. The RD (Rijks-Driehoek) system is the coordinate system used by the Dutch geographical service.</p> <p>Type: Bunker or limestone</p> <p>Location description: description of the name of the site</p> <p> </p> <p><strong>Bunker monitoring core and satellite</strong></p> <p> </p> <p>Date: date</p> <p>Winter: the period between September and April is defined as the winter of the year starting in January.</p> <p>Location description: description of the name of the site</p> <p>N of pond bats: total number of observed pond bats</p> <p>Province: the province</p> <p>Type: hibernacula categorized as a core or satellite site, sites occupied by pond bats since 1977 and 1997 respectively.</p> <p>XY-coord: The coordinates of the entrance of the bunker or limestone mine. The RD (Rijks-Driehoek) system is the coordinate system used by the Dutch geographical service.</p> <p> </p> <p> </p> <p><strong>Supporting information (as referenced in the published paper, hence also available with plos one)</strong></p> <p><br> <strong>S1 Fig. The range of the West European pond bat population (TIF).</strong> The shaded areas indicate the<br> areas where the bulk of the surveys were carried out.</p> <p><br> <strong>S2 Fig. The distribution of the pond bat in Europe (country boundaries are only indicative) (JPG).</strong> Within the whole range of the species distribution seven groups can be separated.<br> A The Netherlands, Belgium and Northwest Germany (~the West European population),<br> B Jutland Peninsula,<br> C Central European lakelands,<br> D The Baltic States,<br> E Ural Mountains (hibernacula),<br> F Volga Valley (summer nurseries),<br> G Hungary and Romania.<br> <br> <strong>S3 Fig. The distribution of hibernacula used by the western pond bat population (TIF). </strong>These are<br> sites with three or more records of pond bats in one or both study periods. We identified four<br> roost categories: Roosts which have been used ever since 1900 (= green squares), roosts used<br> only between 1900–1980 (= open black squares), roosts occupied after 1980 (= purple circles),<br> roosts occupied after 1997 (= blue asterisks). Detailed maps, all with the same enlargement, of<br> the clusters in the provinces of Zuid-Holland (1), Gelderland (1) and Limburg (3) are provided.<br> <br> </p> <p><strong>S1 Table. Summary of the average weight of pond bats over the study period.</strong> The weight is averaged per week. The table gives average weight of females, males both adults and juveniles.</p> <p> </p> <p>Avg weight: average weight of pond bats of each sex, in a certain week</p> <p>Sex: male of female</p> <p>Week number: number of the week</p> <p>Age: juvenile (or young of the year). Defined as the from birth until the onset of first hibernation. Subadult or sexual immature, defined as individuals with no signs of (past) reproductive activity. Adult or sexual mature, defined as all individuals with signs of (previous) reproductive activity.</p> <p>N observations: number of observations within each subset.<br> </p> <p><strong>S2 Table. Mark and recapture data from the historical dataset.</strong><br> </p> <p>Ringnumber: the label of the ring</p> <p> Sex: male or female</p> <p>capture date: date of capture</p> <p>capture location: description of capture location</p> <p>x y coordinate: The coordinates of the capture location in RD. The RD (Rijks-Driehoek) system is the coordinate system used by the Dutch geographical service.</p> <p>recapture date: date of recapture</p> <p>recapture location: description of recapture location</p> <p>x y coordinate: The coordinates of the recapture location in RD. The RD (Rijks-Driehoek) system is the coordinate system used by the Dutch geographical service.</p> <p> </p> <p><strong>S3 Table. Mark and recapture data from the recent dataset.</strong></p> <p> </p> <p>Same dataset as the historical set, but now including age (see definition used in S1)<br> <br> </p>
Residual emissions in long-term national climate strategies show limited climate ambition - Supplementary Data
<p>This supplementary data file contains the strategy data required to produce all figures in 'Residual emissions in long-term national climate strategies show limited climate ambition', in addition to tables presented in Supplemental Information.</p> <p>See 'Title' tab for contents. </p>
The Human Developing Cerebral Cortex Is Characterized by an Elevated De Novo Expression of Long Noncoding RNAs in Excitatory Neurons
<p>This project contains the annotated transcriptomes in GTF format used in the manuscript "The Human Developing Cerebral Cortex Is Characterized by an Elevated De Novo Expression of Long Noncoding RNAs in Excitatory Neurons" DOI: <a href="https://doi.org/10.1093/molbev/msae123">https://doi.org/10.1093/molbev/msae123</a></p>
Infaunal mollusca in 4 fjord along a long latitudinal gradient
<p>5 files are included in the object:</p> <ol> <li>Abundance data of the infaunal communities collected from two depth zones in four fjords along a long latitudinal gradient</li> <li>Biomass data of the infaunal communities collected from two depth zones in four fjords along a long latitudinal gradient</li> <li>Biological trait data for the collected taxa. Modalities for three biological traits are included (feeding mode, maximum size, life span)</li> <li>Habitat clasification for the studied locations</li> <li>R code for conducting RLQ analyses using the provided data</li> </ol>
PhytoNode Upgraded: Energy-Efficient Long-Term Environmental Monitoring Using Phytosensing
<p>The urban population continues to grow despite health risks associated with densely populated cities, such as traffic congestion and air pollution. At the same time cities are also further heating up due to climate change. Environmental monitoring is increasingly critical to react quickly to temporarily increased concentrations of, for example, carbon monoxide, nitrogen oxides, ozone, and particulate matter. <br>We introduce a significantly improved version of our PhytoNode, an energy-efficient sensor node designed for phytosensing, that is, using of plants as environmental sensors. We aim for a scalable and sustainable real-time monitoring solution following our vision of an `intelligent plant' as an inexpensive and accurate sensor node. <br>We measure electrical potentials and leaf temperatures of plants to assess their well-being and, in turn, environmental conditions. <br>The PhytoNode achieves long-term energy autonomy by harvesting energy via solar cells and shares data via Bluetooth Low Energy (BLE) communication. We process the gathered time series plant data onboard in real-time using methods of Machine Learning (ML) to analyze the plant's activity and to detect dangerous concentrations of gases. In a few showcasing experiments, we demonstrate the feasibility of both our hardware and software approach for continuous, long-term environmental monitoring based on phytosensing. By embedding engineered devices in living plants as a `plant wearable' that listens to plant responses, we hope to help pushing towards smarter future cities and healthier urban environments. </p> <p> </p> <p>Data repository for our paper "PhytoNode Upgraded: Energy-Efficient Long-Term Environmental Monitoring Using Phytosensing", submitted to the 8th Future of Information and Communication Conference 2025 (FICC 2025). Please refer to the paper for more information.</p>
Data from: Long-term effects of meadow management on seed bank diversity and composition
<p>Aims: Oligotrophic grasslands are habitats that host among the most diverse plant communities in Europe. Altering management regimes by either intensifying or ceasing management is known to decrease plant diversity. Yet, despite its importance for the recovery of plant communities after disturbances, little is known about whether seed banks are also affected by changes in management. Here, we investigate the effect of management practices on a meadow seed bank using a long-term manipulative experiment. We focus on the response of the seed bank to the treatments, and the relationship between the seed bank and the vegetation response.</p> <p>Methods: The study was conducted in a species-rich wet meadow. The experiment consists of a factorial combination of fertilization, mowing, and removal of the dominant species. After 20 years of management, the seed bank was sampled seasonally at two soil layer depths. Standing vegetation was recorded in June at the peak of vegetation.</p> <p>Results: All seed bank characteristics varied between soil layers. Mowing decreased seed density and diversity, while fertilization significantly affected the species composition. Dominant removal had no effect on the seed bank. While seed bank diversity was not correlated to vegetation diversity, individual species’ responses to mowing and fertilization were positively correlated in the seed bank and the vegetation.</p> <p>Conclusions: Our results show that long-term management influences the seed bank down to 10 cm of soil depth. Whereas mowing apparently reduced seed density and diversity, the effects of fertilization on these characteristics were harder to interpret. After 20 years, most species had concordant responses to both mowing and fertilization, indicating a low legacy of previous management regimes on the seed bank. Our study reveals that the intensification of grassland management has a profound effect on plant diversity by directly affecting plant communities and their seed bank-driven recovery potential.</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>
Movement of plastic balls in a long-vibrating cylinder: from disorder to structure formation with examples of its instability
<p>Many plastic balls, made from PolyOxyMethylene (POM) and of three different diameters 2, 3 and 4 mm, were used to observe the formation and stability of the structure in ball beds when, after pouring into a plexiglass cylinder, they were subjected to long-term vertical vibrations with frequency 100 Hz. The vibration table Vibrax (Renfert GmbH, Germany), used in the experiment and shown in Fig. 1, can function in two modes: sinusoidal (s) and nonsinusoidal (ns). The vibration table can act at four power levels of the vibrations, selected by an operator using the right knob of the table (see Fig. 1). In the presented series of 43 vibration experiments, always the highest power level was used. </p> <p>Locations of surface balls on all sides of a vibrated cylindrical bed were simultaneously recorded on one video frame thanks to the use of two perpendicular mirrors (see Fig. 1), which enables observation of four images: one of the real cylinder and three of its mirror reflections. An explanation of the scene, as seen by the recording camera, is given in the scheme in Fig. 2. Video names were given in a standard form to inform the user about the most important parameters (more in README.txt).</p> <p> </p>
BetaEddyOne: A long-lived 1.5 layer quasigeostrophic eddy on a beta plane, with Lagrangian particles
<p>BetaEddyOne is a yearlong simulation of a large, nonlinear oceanic eddy under 1.5 layer quasigeostrophic dynamics on a beta plane, initially located at 24˚N. The simulation is run at 512 x 256 resolution and is seeded with Lagrangian particles with one particle per grid point. </p> <p>This simulation is indended for use as a common test case for eddy-related diagnostics and analysis methods. It approximately replicates the eddy analyzed in detail in Early, Samelson, and Chelton (2011), <a href="https://doi.org/10.1175/2011JPO4601.1">https://doi.org/10.1175/2011JPO4601.1</a>. The simulation was created using the WaveVortexModel (Early, Lelong, and Sundermeyer, 2021, <a href="https://doi.org/10.1017/jfm.2020.995">https://doi.org/10.1017/jfm.2020.995</a>), the code for which is available on GitHub at <a href="https://github.com/Energy-Pathways-Group/GLOceanKit">https://github.com/Energy-Pathways-Group/GLOceanKit</a>. This particular simulation was created for use in the paper</p> <p>Lilly, J. M., J. Feske, B. Fox-Kemper, and J. J. Early (2024). Integral theorems for the gradient of a vector field, with a fluid dynamical application. <em>Proceedings of the Royal Society of London, Series A</em>. <strong>480</strong> (2293): 20230550, 1–30. <a href="https://doi.org/10.1098/rspa.2023.0550">doi 10.1098/rspa.2023.0550</a>.</p> <p>The figure shows a snapshot of the model's vertical vorticity. </p> <p> </p>
Precomputed data accompanying the paper "Reduced Data-Driven Turbulence Closure for Capturing Long-Term Statistics"
<p>Together with the python code in <a href="https://github.com/rik-stra/tau_orthogonal_method_for_2D_turbulence" target="_blank" rel="noopener">https://github.com/rik-stra/tau_orthogonal_method_for_2D_turbulence</a>, this dataset lets the user interact with and reproduce the results in the paper "Reduced Data-Driven Turbulence Closure for Capturing Long-Term Statistics".</p> <p> All data in this dataset is computed with the provided code.</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>
Data of publication All-optical control of long-lived nuclear spins in rare-earth doped nanoparticles
<p>Data corresponding to the figures of the publication "All-optical control of long-lived nuclear spins in rare-earth doped nanoparticles" by D. Serrano et al. (https://www.nature.com/articles/s41467-018-04509-w). A text file describes data in each compressed folder, please refer to the publication for more details. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.