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CAMELS-AUS v2: updated hydrometeorological timeseries and landscape attributes for an enlarged set of catchments in Australia
<p>Version 2 of the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. Since publication in 2021, CAMELS-AUS (Australia) has served as a resource for the study of hydrological change, arid-zone hydrology, and hydrological model improvement. In this update, the dataset has been significantly enhanced both temporally and spatially. The new dataset comprises information for over twice as many catchments (561 compared to 222). The streamflow and climatic information are updated a further eight years (2022 compared to 2014). Lastly, the attribute information is improved, particularly with respect to hydrological statistics (signatures) and uncertainty in streamflow. Together, these updates make CAMELS-AUS Version 2 a more comprehensive and current resource for hydrological research and applications. </p>
Data sets and code for "Suprachiasmatic Nucleus-wide Estimation of Oscillatory Temporal Dynamics" (Yao et al, 2024)
<ul> <li>Data from iDISCO clearing and scanning of three adult mouse suprachaismatic nuclei. Brains are labeled as b1, b2, and b3. Each lobe of the SCN is recorded in a separate csv file. Animals were sacrificed at ZT 19. </li> <li>Data for PER2::LUC recordings of ix adult mouse suprachaismatic nuclei. For each slice there are two files: the time series data (labeled "slice-[orientation]-time-series-#" and the coordinates of the pixels represented (labeled "slice-[orientation]-pixel-coords-#."</li> <li>Code in MATLAB to perform phase extraction, linear modeling, phase estimation, and dynamical simulation.</li> </ul>
Data sets, code, figures for Sensing force gradients with cavity optomechanics while evading backaction
<p>The directory contains data sets, code and figures for the published version of the research article Sensing force gradients with cavity optomechanics while evading backaction.</p>
Data set for the study "Assessing the lifetime of anthropogenic CO2 and its sensitivity to different carbon cycle processes"
<p>This repository contains the data necessary to reproduce the results of the paper: <br>"Assessing the lifetime of anthropogenic CO<sub>2</sub> and its sensitivity to different carbon cycle processes" <br><a href="https://doi.org/10.5194/bg-22-2767-2025" target="_blank" rel="noopener">https://doi.org/10.5194/bg-22-2767-2025</a></p> <h3><strong>Data organization:</strong></h3> <p>The Zenodo upload is organized as the following inside of <code>results.zip</code>:</p> <ul> <li>Data analysis and figure generation are given by "*.pynb" and "*.m" files<br><br></li> <li>Data files as NetCDF output are organized with the following structure inside of <code>data</code>:<br><br> <ul> <li><strong>Experiment</strong>: <code>REF</code>, <code>noLAND</code>, <code>noWEATH</code>, <code>ECS2</code>, <code>ECS4</code>, <code>intCH4</code>, <code>PATH1</code>, <code>PATH2</code>, and <code>PULSE</code><br><br> <ul> <li><strong>Emissions scenario</strong>: <code>0_gtc</code>, <code>500_gtc</code>, <code>1000_gtc</code>, <code>2000_gtc</code>, <code>3000_gtc</code>, <code>4000_gtc</code>, and <code>5000_gtc</code><br><br> <ul> <li><strong>Component</strong>: atmosphere (<code>atm</code>), land (<code>lnd</code>), ocean (<code>ocn</code>), biogeochemistry (<code>bgc</code>), and the carbon cycle (<code>co2</code>)<br> <ul> <li>Note: for <code>intCH4</code>, there is another file concerning methane (<code>ch4</code>)</li> <li>Note: surface ocean pH and surface ocean DIC were not part of the standard output in the original CLIMBER-X model. Instead, these variables were calculated during post-processing using 2D spatial data. Since the 2D data was only output every 1 kyr, the first millennium of data was missing. To address this, we re-ran the experiments with surface ocean pH and DIC included in the output for the first 1 kyr. This is why there are additional individual files for pH, DIC, and the Revelle factor (see "fig5_7_8_9.ipynb" for further details).<br><br></li> </ul> </li> <li><strong>File type</strong>: for each component, files are divided into timeseries (<code>*_ts.nc</code>) or 2D data with a 1 kyr output frequency (<code>*.nc</code>)<br> <ul> <li>Note: due to size constraints of the Zenodo repository, only some 2D spatial data presented in the publication (for the <code>REF</code> experiment) is available. However, this is not an exhaustive dataset. For inquiries regarding additional data, please contact the corresponding author to explore potential availability.</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul>
Data set from: Can laboratory-based XAFS compete with XRD and Mössbauer spectroscopy as a tool for quantitative species analysis?
<p><strong>Abstract:</strong> This work investigated the capability of quantitative laboratory X-ray Absorption Fine Structure Spectroscopy (lab-XAFS) via Linear Combination Fitting (LCF) of reference spectra in comparison with quantitative X-ray diffraction (XRD) and Mössbauer spectroscopy. While lab-XAFS already show good results when performing LCF with significant different spectra of the species to be identified, the method is challenging when the reference spectra and possibly species in the sample are very similar as it is the case for α-Fe<sub>2</sub>O<sub>3</sub>, γ- Fe<sub>2</sub>O<sub>3</sub> and Fe<sub>3</sub>O<sub>4</sub>. For this investigation an iron oxide mineral with origin from Mexico (here named Mexican Magnetite) with different iron oxide phases was used and measured using all three methods.</p> <p> </p> <p>This data set contains the raw data of the work “<em>Can laboratory-based XAFS compete with XRD and Mössbauer spectroscopy as a tool for quantitative species analysis? Critical evaluation using the example of a natural iron ore</em>” of XAFS, XRD and Mössbauer measurements. This includes XAFS, Mössbauer and XRD spectra of the reference materials α-Fe<sub>2</sub>O<sub>3</sub>, Fe<sub>3</sub>O<sub>4</sub> and the sample Mexican magnetite, the XAFS spectra of the reference material γ- Fe<sub>2</sub>O<sub>3</sub> and the XAFS, XRD and Mössbauer spectra of three different α-Fe<sub>2</sub>O<sub>3</sub>/Fe<sub>3</sub>O<sub>4</sub> mixtures.</p> <p> </p> <p><u>Sample information/sample list</u></p> <p><strong>sample/references:</strong> The sample and the corresponding short cut name used in the data files is listed. Furthermore the method the sample was measured with is also listed.</p> <table> <tbody> <tr> <td> <p><strong>Short cut name</strong></p> </td> <td> <p><strong> Sample/reference</strong></p> </td> <td> <p><strong>Measured with</strong></p> </td> </tr> <tr> <td> <p>MexicanMagnetite</p> </td> <td> <p> Iron oxide mineral with origin in Mexico</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>Fe2O3</p> </td> <td> <p>Fe2O3-alpha / Hematite</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>Fe3O4</p> </td> <td> <p>Fe3O4 / Magnetite</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>Fe</p> </td> <td> <p>Iron powder</p> </td> <td> <p>XAFS</p> </td> </tr> <tr> <td> <p>Fe2O3-alpha</p> </td> <td> <p>Fe2O3-alpha / Hematite</p> </td> <td> <p>XAFS</p> </td> </tr> <tr> <td> <p>Fe2O3-gamma</p> </td> <td> <p>Fe2O3-gamma / Maghemite</p> </td> <td> <p>XAFS</p> </td> </tr> <tr> <td> <p>30-70</p> </td> <td> <p>Mixture of 30 % Fe2O3-alpha/ 70 %Fe3O4</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>50-50</p> </td> <td> <p>Mixture of 50 % Fe2O3-alpha/ 50 %Fe3O4</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> <tr> <td> <p>70-30</p> </td> <td> <p>Mixture of 70 % Fe2O3-alpha/ 30 %Fe3O4</p> </td> <td> <p>XAFS, XRD, Mössbauer</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Mixtures ratios:</strong> The prepared Fe2O3-Fe3O4 model mixtures with the weight-in ratios and the actual achieved mass percentage ratio between the two iron species, taken impurities of the used materials into account, are listed below. The short cut name is the name used in the data files (see table above).</p> <table> <tbody> <tr> <td> <p><strong>Short cut name</strong></p> </td> <td> <p><strong>Actual achieved weigh-in ratios</strong></p> <p><strong>m(Fe2O3)/m(Fe3O4)*</strong></p> </td> <td> <p><strong>Actual achieved mass percentage ratios ωrel(Fe2O3) / ωrel(Fe3O4)</strong></p> </td> </tr> <tr> <td> <p>30-70</p> </td> <td> <p>0.31380 g / 0.7059 g</p> </td> <td> <p>31.8 / 68.2</p> </td> </tr> <tr> <td> <p>50-50</p> </td> <td> <p>0.5140 g / 0.5174 g</p> </td> <td> <p>50.6 / 49.4</p> </td> </tr> <tr> <td> <p>70-30</p> </td> <td> <p>0.7037 g / 0.3041 g</p> </td> <td> <p>70.5 / 29.5</p> </td> </tr> </tbody> </table> <p>*the given masses here, ar the masses of the materials of the mixtures before sampel prepration. For the sample prepration the mass applied on the tape or mixed with wax is about 5-10 mg.</p> <p><u>Spectrometer Specifications</u></p> <p><strong>XAFS:</strong> The experimental setup for the laboratory XAFS measurement is based on the Highly Annealed Pyrolytic Graphite (HAPG) von Hámos spectrometer with the use of a cylindrically shaped crystal.</p> <p>As detector unit the pixelated X-ray hybrid-CMOS detector Dectris Eiger2 R 500k was used. The area of detection is 77.3 mm x 38.6 mm with a pixel size of 75 µm x 75 µm. The X-ray source was a water-cooled micro focus X-ray tube with molybdenum as anode material, a power of 30 Watt optimised at 15 kV and a spot size of 70 µm.</p> <p><strong>Sample preparation</strong>: α-Fe<sub>2</sub>O<sub>3</sub>, Fe<sub>3</sub>O<sub>4</sub>, the three α-Fe<sub>2</sub>O<sub>3</sub>/Fe<sub>3</sub>O<sub>4</sub> mixtures and the sample Mexican magnetite were applied on adhesive tape, sliced in 1cm x 1cm pieces characterized with XRF to determine the iron content as [<em>Q</em>] = mg/cm² and then stacked by taking the iron content of each slice into account to achieve an absorption of <em>µ*Q</em> of about 1 at the edge.</p> <p>The γ- Fe<sub>2</sub>O<sub>3</sub> and also the three α-Fe<sub>2</sub>O<sub>3</sub>/Fe<sub>3</sub>O<sub>4</sub> mixtures were prepared as Pellet. Here the sample material was mixed with Hoechst Wax C in a ratio of 1:6, mixed in a vortex shaker and then pressed with a hydraulic press with a Pellet diameter of 13 mm. The amount of the wax/sample powder material was weight before inserting in the press to the amount of <em>Q</em> to achieve a <em>µ*Q</em> of about 1 with a 13 mm Pellet.</p> <p>Shifts of the energy axis as well as a widening or compression of this axis could be present when comparing the data with other data sets of other spectrometer or synchrotron radiation facilities, since no precise energy calibration was carried out due to the reason that the samples were compared to the measured references and would have the same shift, widening or compression.</p> <p> </p> <p><strong>XRD:</strong> Two different commercial XRD set ups have been used. For the Mexican magnetite the Benchtop XRD spectrometer Bruker D2Phaser with a Cobalt X-ray source and a SSD160 detector (active length = 12 mm) was used. The measurement range was 10°- 90° 2theta with 0.014° step size and 4.8 s/step, resulting in a total measurement time of 8h. During the measurement the sample was rotated with 10 rpm. The sample was filled in PMMA-holders (Ø 2.5 mm) using the top-loading technique. The analysis was carried out using a 1-mm fixed divergence slit, a 2.5° primary and a 4° secondary soller collimator, a fixed knife edge (3 mm above the sample surface), and an Fe Kβ filter (2.5).</p> <p>For the X-ray diffraction measurements of the α-Fe2O3/Fe3O4 mixtures and the pure references a Panalytical X’Pert PRO diffractometer with a Bragg-Brentano setup was used. The diffractometer operates with a Cu anode and without a monochromator (Cu-Kalpha radiation) at 40 kV and 30 mA. The diffraction data were obtained over a measurement range of 10–120° 2theta. Samples were applied flat on a cut-off Si wafer attached to the sample holder.</p> <p><em> </em></p> <p><strong>Mössbauer:</strong> Mössbauer spectroscopy was performed at a MIMOS II type spectrometer with a <sup>57</sup>Co source (in rhodium matrix). For the analyses the <sup>57</sup>Fe-γ-line E = 14.4 keV was used and α-iron (α-Fe foil) was applied for the velocity calibration before the samples were analyzed. The samples were prepared in plastic powder sample holders and measured in transmission mode at room temperature. The measurement time varied between 12 h and 120 h depending on the sample.</p> <p> </p> <p><strong>Information on data sets</strong></p> <p>XAFS - this folder contains the XAFS spectra as intensity file with I0 (without the sample) and the It (transmission signal through the sample) for each sample. Multiple samples (It) share the same I0 and are therefore in the same data set. The Number in the filename between “XAFS“ and “data-set..” is the date of the measurement in the following format: YYYY_MM_DD. The first column in each file is the energy in unit eV. The abbreviation “WP” after each sample name in the header means “<strong>W</strong>ax <strong>P</strong>ellet” and indicates that the measurement was performed on a sample prepared as a wax pellet, the number (WP<strong>1</strong>) indicates the number of the pellet. Two pellets of each mixture were prepared to investigate the influence of the sample preparation. If the sample name is missing “WP#” the sample was prepared on adhesive tape as described above. The information on the contents of each data set as well as the measurement time (t = #h) for each It of the sample/reference can be found in data_dictionary_v2.txt.</p> <p>The intensity is normalized to counts per 1800 seconds in a 0.25 eV (for data-set-1) and 1 eV (for data-set-2, data-set-3 and data-set-4) energy interval with the indicated central bin energy.</p> <p> </p> <p>XRD - this folder contains the raw intensity files over 2theta (ASC-file). Each sample has its own file with the first column for the 2theta in unit degree and the second column for the measured intensity.</p> <p>The Number in the file name between XRD and sample name (e. g. Fe2O3, 30-70) is the date of the measurement in the following format: YYYY_MM_DD.</p> <p> </p> <p>MOESSBAUER - this folder contains the recoil Lorentz site analysis fit data of the samples. The files consist of the observed intensity (Iobs) over the velocity (v (mm/s)), including the calcucalted intensity (Icalc) and the fits of the subspectra (Sextet Site 1, etc. ). Each sample has it owns file. While the references substances <br>Fe2O3 and Fe3O4 were measured between 2016 and 2019, the MexicanMagnetite was measured 2020. An exact measurement date can’t be determined anymore.</p> <p> </p> <p>The corresponding sample to the short cut name (e. g. Fe2O3, 30-70,..) in the files can be found above and is listed in the <em>data_dictionary.txt</em> file as well.</p> <p> </p>
FM-Tools Release 2.2: Data Set of Metadata about Tools for Formal Methods (SV-COMP 2025, Test-Comp 2025)
<h1>Collection of Information about Formal-Methods Tools</h1> <h2>Motivation</h2> <p>There are many tools available that implement formal-methods approaches. This repository collects meta data about the tools, such that it becomes easier to reuse, integrate, and cooperate with formal-methods tools.</p> <p>A <a href="https://www.sosy-lab.org/research/pub/2024-Podelski65.Find_Use_and_Conserve_Tools_for_Formal_Methods.pdf">description</a> of the structure of this repository can be found in an article.</p> <p>A <a href="https://fm-tools.sosy-lab.org/">formatted listing</a> of some of the data in this repository can be found on a generated web site.</p> <p>A <a href="https://fm-tools.sosy-lab.org/schema.html">schema definition</a> of the data files in this repository can be found on a generated web site.</p>
Open-Set Tagging Dataset (OST)
<p>Open-set Tagging (OST) is a synthetic dataset of 1s clips used to evaluate source-centric representation learning models in the paper <a href="https://ieeexplore.ieee.org/document/10890242">Compositional Audio Representation Learning</a>.</p> <p>Due to the size of the dataset, we only share the source files, and provide the scripts to generate the dataset are available <a href="https://github.com/sripathisridhar/moads">here.</a><br><br>The dataset generation process is as follows:<br>1. From single-source FSD50K audio files, we generate a dataset of 10s soundscapes called Open-set Soundscapes (OSS) using <a href="https://github.com/justinsalamon/scaper">Scaper</a>.</p> <p>2. We then center a 1s window around the center of each sound event in the 10s soundscapes to generate Open-set Tagging (OST), which contains ~500k clips. </p> <p>If you are not going to use OSS, you can choose to synthesize it without audio-- this will synthesize only the <a href="https://github.com/marl/jams">JAMS</a> annotation files needed for the 1s clips. Using the OSS JAMS files, OST clips can be generated deterministically.</p> <p>There are five dataset variants (~17GB each), each with a different random assignment of classes to the known and unknown class categories. For further details, refer to our previous paper <a href="https://dcase.community/documents/workshop2023/proceedings/DCASE2023Workshop_Sridhar_11.pdf">Multi-label open-set audio classification</a>. In <a href="https://ieeexplore.ieee.org/document/10890242">this work</a>, OST dataset variant 1 is referred to as OST for simplicity. <br><br>We also introduce a tiny version of the dataset called OST-Tiny, which contains ~20k clips and only 10 known classes. This is convenient for faster prototyping and to evaluate models in a more challenging open-set classification scenario.</p> <p> </p>
Data Set: Raman Investigation of In Vivo Radiation Exposure on Melanin in Murine Hair
<p>Updated version contains additional data added during peer review. Files contains Raw Raman spectra collected from the hair of mice irradiataed with gamma rays of specified dose. The time following exposure (in days) that the hair was sampled, the sex of the mouse, and the total dose (Gy) is given for each spectrum. The Raman spectra were collected with excitation wavelengths of 532 nm and 785 nm. The Raman shift labels for each excitation wavelength is given the first row of the data table prior to the raw spectra.</p>
Data set for paper on Australian fur seal prey capture and foraging efficiency
<p>Data set for paper on Australian fur seal prey capture and foraging efficiency</p>
Parcel Manager data set
<p>Data contained in this repository can be used for replicating the two use cases of Parcel Manager software application presented in the paper (Colomb et al., 2021, submitted).</p> <p>The first use case enables the simulation of a scenario called <em>test scenario</em> designed specifically to test different parcel division processes and workflows with Parcel Manager. The area under study is a small community (Gennes, 681 inhabitants) located in the east of France. Data used are from the French IGN BD Topo 2018.</p> <p>The second use case enables the comparison of the shape of parcels created by simulation with the shape of parcels in real cases. The comparison concerns the parcel plans in 2003 and 2018 of 11 communities of the Seine-et-Marne department, near Paris capital city (France). Data for 2018 are from the French IGN BD Topo 2018. They have been cleaned manually as follows:</p> <ul> <li>removing parcels that represent driveways or roads which could be used by residential parcels, – removing the tiny parcels resulting from a former division process and that could prevent the access to roads of neighbouring parcels,</li> <li>removing parcels located on water surfaces or railways,</li> <li>removing roads of type ’trails’ and ’stairs’.</li> </ul> <p>Parcel data for 2003 are from the IGN BD-Parcellaire 2003. Building and road data come from the IGN BD-Topo 2005. The cleaning results of the 2018 parcel plan have been copied into the 2003 parcel plan.</p> <p>The road network encompasses every types of paths that are accessible with a car.</p> <ul> <li>Driveways are small paths for cars that connect a house to the road network. Driveways are included in parcels and are not considered as a proper road.</li> <li>Gravel roads are non-asphalted trails. They can occasionally be used by cars. Their level of attraction is low (level 2).</li> <li>Lanes are small roads dedicated to house access. Their level of attraction is maximal (level 4). Note that peripheral roads created with the straight skeleton algorithm are classified as lanes.</li> <li>Streets are roads that connect lanes. Their level of attraction is high (level 3).</li> <li>Arterial roads are high-speed roads connecting communities. Their level of attraction for parcel contact is minimal (level 1).</li> </ul> <p>In the data set created for the <em>t</em><em>est Scenario</em>, the road layer has been manually enriched: missing trails and lanes have been added; type and level of attraction of some road segments have been changed.</p>
DATA SET USED IN THE PHYLOGENETIC ANALYSIS ?, condition not preserved. Coding for Paraortygoides based on BMNH PAL A 6217 (holotype of P. radagasti) and SMRME 1303 (holotype of P. messelensis) in The Fossil Galliform Bird Paraortygoides from the Lower Eocene of the United Kingdom
DATA SET USED IN THE PHYLOGENETIC ANALYSIS ?, condition not preserved. Coding for Paraortygoides based on BMNH PAL A 6217 (holotype of P. radagasti) and SMRME 1303 (holotype of P. messelensis)
Data Set Analisis Perilaku dan Interaksi pada YouTuber Gaming berdasarkan Persebaran Gender
<p>Data Set Analisis Perilaku dan Interaksi pada YouTuber Gaming berdasarkan Persebaran Gender</p>
Electronic Supplement / Data Archive for "Comparison of a Neutral Density Model With the SET HASDM Density Database"
<p>These files provide supplemental data to accompany the paper "Comparison of a Neutral Density Model With the SET HASDM Density Database,” submitted to <em>Space Weather, </em>with manuscript number 2021SW002888. Details are provided in the file DataArchiveDocumentation.pdf.</p>
DATA SET OF EXISTING AND ONGOING CASE STUDIES IN THE ARTS 2008-2020
<p>PURPOSE: D2.1, Data Set of Case Studies in the Arts 2008-2020 fulfils the following purposes:<br> 1. Build a research database for past and ongoing good practices in the field of arts-based social interventions in AMASS partner countries.<br> 2. Construct a solid foundation for the development of new interventions with similar objectives.<br> 3. To avoid obstacles that made many previous efforts in this field unsustainable and unadaptable.<br> 4. To offer a valid and authentic knowledge repository for policy makers, developers of future projects and researchers to identify motivations, philosophies, modes of engagement and impact of arts-based social interventions.</p>
The predication of soil nematode and functional groups abundance in the terrestrial on the Tibetan Plateau (Data Set)
<p>This is the dataset which is generated from the manuscript entitled 'The predication of soil nematode and functional groups abundance in the terrestrial on the Tibetan Plateau'.</p> <p>The spatial resolution of this dataset is about 1 km, of which coordinates systems is WGS84.</p> <p>There are 6 layers of nematode abundance in this GeoTiff file:</p> <p>1. abd - Total Soil Nematode Abundance</p> <p>2. bact - Abundance of bacterivores</p> <p>3. fung - Abundance of fungivores</p> <p>4. herb - Abundance of plant parasite</p> <p>5. omni - Abundance of omnivores</p> <p>6. pred - Abundance of predators</p>
Data Set for Publication "Traceable Characterization of Low Power Voltage Instrument Transformers for PQ and PMU Applications"
<p>This is dataset for paper published in CPEM2020 Conference Proceedings:</p> <p>Crotti G., Delle Femine A., Gallo D., Giordano D., Landi C., Letizia P.,S., Luiso M., "Traceable Characterization of Low Power Voltage Instrument Transformers for PQ and PMU Applications".</p> <p>https://doi.org/10.5281/zenodo.4153819</p> <p>Excel file provides data for Figure 2 and following evaluation</p> <p> </p>
Data set for "Superconducting 2D NbS2 Grown Epitaxially by Chemical Vapor Deposition "
<p>Data set for the paper "Superconducting 2D NbS<sub>2</sub> Grown Epitaxially by Chemical Vapor Deposition"</p>
Assessment of Potential Risk Factors for COVID-19 among Health Care Workers in a Health Care Setting in Delhi, India
<p><strong><em>Executive summary</em></strong></p> <p>The novel coronavirus SARS-CoV2 (COVID-19), first detected by Wuhan Municipal Health Commission, China, in Wuhan, Hubei Province in December 2020 and eventually the disease became pandemic. It was declared as Public Health Emergency of International Concern (PHEIC) by WHO in January 2020. The COVID-19 disease primarily spreads through droplets of saliva or discharge from the nose when an infected person coughs or sneezes. People infected with the COVID-19 virus experiences mild, moderate or serious respiratory illness. </p> <p>Health workers play a critical role in the clinical management of patients with COVID-19 and hence are likely to be the most vulnerable for contracting the disease. Therefore, investigating the extent of infection in health care settings and identifying risk factors for infection among health workers along with follow-up within a facility in which a confirmed case of COVID-19 infection is receiving care can provide useful information on virus transmissibility and routes of transmission, and will bear important step in limiting amplification events in health care facilities. </p> <p> </p> <p><strong>Objectives:</strong></p> <p>1. To find out the extent of human-to-human transmission of the SARS-CoV-2 infection among health workers<br> 2. To study the clinical presentations of COVID-19 infection and the risk factors for infection among health workers.<br> 3 To evaluate the effectiveness of infection prevention and control measures among health workers in protecting against COVID-19.<br> 4. To evaluate the effectiveness of infection prevention and control programmes at health facility level <br> 5. To determine the serological response of health workers with symptomatic and possibly asymptomatic COVID-19 infection.</p> <p> </p> <p><strong>Materials and Methods:</strong></p> <p>This was a prospective cohort study conducted over a period of seven months, from December 2020 to June 2021, the period covering India’s deadly second wave of COVID-19 pandemic. This was done among the health care workers working in HIMSR & HAHC hospital, a tertiary health care setting (Dedicated COVID-19 Hospital) providing care to patients with a laboratory-confirmed COVID-19 infection. This hospital located in South East Delhi has 200 bedded COVID-19 Care Hospital and 1050 registered healthcare workers who come in contact with COVID-19-infected persons. The study population (sampling frame) included all the health personnel like doctors, nurses, paramedical staff, housekeeping staff, security staff, students of medical, nursing and paramedical sciences and other front office staff who come in contact with the patients. In this study, the first visit / interview (Baseline) was done when the staff came in contact with a confirmed COVID-19 case. The second visit / interview (Endline) was done between 22-28 days. During each of these two visits, biological sample in the form of serum was collected to check the presence of anti-COVID-19 antibodies</p> <p> </p> <p><strong>Results:</strong></p> <p>A total of 192 HCW were recruited in this study. All of them were interviewed and blood was collected for serology at the baseline visit as well as at endline. Out of 192 participants, 119 (61.97%) were detected with SARS-CoV2 antibodies at baseline whereas 73 (38.02%) were seronegative. Again, on22-28 days of follow-up, the seropositivity was 77.7% at the endline. We found that seropositivity was significantly and negatively associated with doctor as profession [OR:0.353, CI:0.176-0.710], COVID-19 symptoms [OR:0.210, CI:0.054-0.820], comorbidities [OR:0.139 , CI: 0.029 - 0.674], recent IPC Training [OR:0.250, CI:0.072 -0.864] , while positively associated with Partially [OR:3.303,CI: 1.256-8.685], as well as fully Vaccinated for COVID-19 [OR:2.428, CI:1.118-5.271]. We also observed seroconversion among 36.7% while 64.0% had increase in titre of antibodies during our follow-up period. The seroconversion was 63.2% in doctors, 42.9% in nurses and 13.0% in paramedics staff. Seroconversion was positively associated with doctor as profession [OR:11.43, CI:2.47 - 52.79] and with partially, as well as fully vaccinated for COVID-19 [OR: 32.63, CI: 5.11 - 208.49]. None of the HCW who were smokers and with any comorbidity did not found to have been seroconversion. We observe a negative and significant relationship of increase in titre of antibodies with recent any ILI symptoms [OR:0.17, 0.13 - 0.94], smokers[OR: 0.35, 95%CI: 0.13 - 0.94], HCW with comorbidities [OR:0.08,95CI: 0.01 - 0.71],, recent full IPC Training [OR:0.07, CI:0.01 -0.63] , while positively associated with partially [OR: 7.87, 95CI: 2.18 - 28.40)], as well as fully Vaccinated for COVID-19 [OR: 3.59, 95CI: 1.46 - 8.87]. Majority of the health care worker enrolled in our study had close contact exposure with COVID-19 patients while 5 had indirect exposure. It was observed that almost all (100% in both) doctors and nurses as well as almost all paramedical staff (99%) were wearing some kind of personal protective equipment (PPE) when they were exposed to a COVID-19 patient. We did not found adherences to any of the infection prevention measure adopted by the enrolled HCW during the recent contact with COVID-19 patients to be significantly associated with seroconversion. </p> <p><strong>Conclusion:</strong></p> <p>Majority of the health care worker (67% doctor, 80% nurses & 55% paramedics) enrolled in our study had close contact exposure with COVID-19 patient. The results show that among 192 HCW enrolled, 62% were seropositive at the baseline. At end line the seropositivity was increased to 77.7%. The seroconversion rate was also studied. It was found to be 36.7% in our study population (63.2% in doctors, 42.9% in nurses and 13.0% in paramedic’s staff.). Adherence to the recommended IPC measures was reported by most participants. About two third (63%) of the HCW in our study were not vaccinated against COVID-19; nurses and paramedics were higher in proportion among those who were unvaccinated. Fifteen percentage were partially vaccinated and 22% were fully vaccinated against COVID-19, with doctors comprising majority among them. We also found that vaccination had the strongest association with seropositivity, seroconversion as well as serial rise of titre.</p>
[Data set]for publication Acidification of Strawberry Puree Affects Color and Volatile Characteristics during Storage
<p>Color degradation of strawberry-based products negatively affects consumer acceptance of these products. To improve the color stability of strawberry puree during storage, an acidification step (pH 1.5 and 2.5) prior to pasteurization was performed and quality changes of strawberry puree during storage (35 °C, 40 days) were monitored. Acidification prior to pasteurization resulted in an improved color and anthocyanin stability. However, the volatile fraction of strawberry puree was negatively influenced by acidification of the puree. Low acidic conditions (pH 1.5) resulted in the hydrolysis of esters, oxidation of terpene oxides, and acid-catalyzed reactions of terpene alcohols during storage. The volatile fraction of strawberry puree stored at pH 2.5 and 3.5 (the latter being the original strawberry puree) were more similar. Furthermore, after 4 days of storage, the pH of the acidified purees was brought back to the natural pH of the system (3.5) and subsequently thermally treated and stored. During this second storage, color showed a slightly faster degradation rate in those purees that were previously acidified, whereas anthocyanins exhibited similar trends suggesting that the advantage of acidification to preserve color would be beneficial after thermal processing, but more limited during storage of the regenerated product (second storage).</p>
EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set
<p><strong>Eighteen high-resolution ecological descriptors of vegetation and terrain for Denmark "EcoDes-DK15"</strong></p> <p>The data are derived from the nationwide airborne laser scanning / LiDAR campaign of Denmark from 2014-2015 provided by the Danish Agency for Data Supply and Efficiency.</p> <p><strong>Update: EcoDes-DK15 v1.1.0 (4 Dec. 2021)</strong></p> <p>Following the recommendations and feedback during the first round of peer-review, we updated the EcoDes-DK processing pipeline and EcoDes-DK15 data set. The key changes are:</p> <ul> <li>New version of the source data optimised to contain only point data collected before the end of 2015. The source data for EcoDes-DK15 v1.0.0 unintentionally contained data from 2018. The new source data is documented <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/documentation/source_data/readme.md">here</a>.</li> <li>New "date_stamp_*" auxiliary variables that illustrate the survey dates for the vegetation points in each cell. See updated descriptor documentation <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/documentation/descriptors.md">here</a>.</li> <li>Re-scaling of "solar_radiation" variable to MJ per 100 m<sup>2</sup> per year.</li> </ul> <p><strong>Detailed documentation for the data set can be found in the accompanying manuscript and GitHub repository:</strong></p> <p>Assmann, J. J., Moeslund, J. E., Treier, U. A., and Normand, S.: EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set, Earth Syst. Sci. Data Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2021-222">https://doi.org/10.5194/essd-2021-222</a>, in review, 2021<strong><em>.</em></strong></p> <p><a href="https://github.com/jakobjassmann/ecodes-dk-lidar">https://github.com/jakobjassmann/ecodes-dk-lidar</a></p> <p>Files are compressed using bzip2 and tar archiving. The compressed archives can be extracted using commonly available archiving tools (for example <a href="https://www.7-zip.org/">7z </a>on Windows, the archiving tool on macOS and bz2 on Linux). </p> <p>A small example "teaser" subset (5 MB) of the data set, covering the Husby Klit area from Figure 7 in the manuscript, can be found <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/manuscript/figure_7/EcoDes-DK15_teaser.zip">here</a>.</p> <p><strong>Abstract (from manuscript)</strong></p> <p>Biodiversity studies could strongly benefit from three-dimensional data on ecosystem structure derived from contemporary remote sensing technologies, such as Light Detection and Ranging (LiDAR). Despite the increasing availability of such data at regional and national scales, the average ecologist has been limited in accessing them due to high requirements on computing power and remote-sensing knowledge. We processed Denmark’s publicly available national Airborne Laser Scanning (ALS) data set acquired in 2014/15 together with the accompanying elevation model to compute 70 rasterized descriptors of interest for ecological studies. With a grain size of 10 m, these data products provide a snapshot of high-resolution measures including vegetation height, structure and density, as well as topographic descriptors including elevation, aspect, slope and wetness across more than forty thousand square kilometres covering almost all of Denmark’s terrestrial surface. The resulting data set is comparatively small (~94 GB, compressed 16.8 GB) and the raster data can be readily integrated into analytical workflows in software familiar to many ecologists (GIS software, R, Python). Source code and documentation for the processing workflow are openly available via a code repository, allowing for transfer to other ALS data sets, as well as modification or re-calculation of future instances of Denmark’s national ALS data set. We hope that our high-resolution ecological vegetation and terrain descriptors (EcoDes-DK15) will serve as an inspiration for the publication of further such data sets covering other countries and regions and that our rasterized data set will provide a baseline of the ecosystem structure for current and future studies of biodiversity, within Denmark and beyond.</p> <p><strong>Acknowledgements (from manuscript)</strong></p> <p>We would like to thank Andràs Zlinszky for his contributions to earlier versions of the data set, Charles Davison for feedback regarding data use and handling, as well as Matthew Barbee and Zsófia Koma for sharing their insights on the source data merger and Zsófia’s script to generate summary statistics for the different versions of the DHM point clouds. Funding for this work was provided by the Carlsberg Foundation (Distinguished Associate Professor Fellowships) and Aarhus University Research Foundation (AUFF-E-2015-FLS-8-73) to Signe Normand (SN). This work is a contribution to SustainScapes – Center for Sustainable Landscapes under Global Change (grant NNF20OC0059595 to SN).</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.