cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.1017248
title: Aq916 Egl-30(N686); Goa-1(N1134)I | 2010-09-17T16:25:16+01:00

description: This experiment is part of the <em>C.elegans behavioural database</em>. For more information and the complete collection of experiments visit http://movement.openworm.org



<b>preview link</b> : https://www.youtube.com/watch?v=wr5e14Uaa30
<b>strain</b> : AQ916
<b>timestamp</b> : 2010-09-17T16:25:16+01:00
<b>gene</b> : egl-30;goa-1
<b>chromosome</b> : I
<b>allele</b> : n686;n1134
<b>strain_description</b> : egl-30(n686); goa-1(n1134)I
<b>sex</b> : hermaphrodite
<b>stage</b> : adult
<b>ventral_side</b> : anticlockwise
<b>media</b> : NGM agar low peptone
<b>arena</b> :   
  <b>style</b> : petri
  <b>size</b> : 35
  <b>orientation</b> : away
  

<b>food</b> : OP50
<b>habituation</b> : 30m wait
<b>who</b> : Laura Grundy
<b>protocol</b> : Method in E. Yemini et al. doi:10.1038/nmeth.2560. Worm transferred to arena 30 minutes before recording starts.
<b>lab</b> :   
  <b>name</b> : William R Schafer
  <b>location</b> : MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK
  

<b>software</b> :   
  <b>name</b> : tierpsy (https://github.com/ver228/tierpsy-tracker)
  <b>version</b> : cbfc23eb4f1ac2f29be75ade7a937eed58a5b219
  <b>featureID</b> : @OMG
  

<b>base_name</b> : egl-30 (n686); goa-1 (n1134) on food R_2010_09_17__16_25_16___8___9
<b>total time (s)</b> : 898.967
<b>frames per second</b> : 30.03
<b>video micrometers per pixel</b> : 4.52963
<b>number of segmented skeletons</b> : 26871
DOI: 10.5281/zenodo.1244313
title: Poa_Uav

description: This dataset contains two set of images (trajectories) taken by a UAV equipped with a downward looking camera. The region in which the UAV flyed was at Porto Alegre (more specifically, Federal University of Rio Grande do Sul, Campus do Vale), a city from Rio Grande do Sul, Brazil. Besides the sets of images, there are also a folder with five satellite images from Campus do Vale and a README file. 

 

This dataset was used in the experiments of: 

M. Mantelli, D. Pittol, R. Neuland, A. Ribacki, R. Maffei, V. Jorge, E. Prestes, M. Kolberg, "A novel measurement model based on abBRIEF for global localization of a UAV over satellite images."
description: {"references": ["A novel measurement model based on abBRIEF for global localization of a UAV over satellite images"]}
DOI: 10.1594/pangaea.556306
title: Methane concentration of the water column at CTD station SO178-39-2, Okhotsk Sea
DOI: 10.5281/zenodo.4730390
title: FSD-2k

description: Created By Félix Gontier and Mathieu Lagrange, LS2N, CNRS, Ecole Centrale Nantes Contact : mathieu.lagrange@cnrs.fr If used for research, please refer to: <pre>@article{gontier2021training, title={Polyphonic training set synthesis improves self-supervised urban sound classification}, author={Félix Gontier and Vincent Lostanlen, and Mathieu Lagrange and Nicolas Fortin and Jean-Francois Petiot and Catherine Lavandier}, journal={The Journal of the Acoustical Society of America}, year={2021}, publisher={Acoustical Society of America} } </pre> FSD-2k contains about 200 monophonic audio clips collected from online resources, which are unrelated to the city of Lorient: Freesound for birds and traffic and Librispeech for voice.<br> <br> The total duration of the dataset is of the order of 2.4k seconds, i.e., 40 minutes. Each audio samples are cut into one or several 3 seconds parts, each resulting into spectrograms of size 23x29, leading to a dataset of 609 spectrograms. Low volume amorphic background noise recordings is added and the cut audio sample is centered within the 3 seconds if shorter. &gt;&gt; import numpy as np &gt;&gt; s=np.load('FSD-2k_train_spectralData.npy') &gt;&gt; print(s.shape) (609, 23, 29) The three dimensions respectively corresponds to the sceneId, the frameId (time), and the spectralId (frequency). &gt;&gt; a=np.load('FSD-2k_train_presence.npy') &gt;&gt; print(a.shape) (609, 16, 3) The 3 dimensions corresponds to the sceneId, the frameId (time), the sourceId (traffic, voice, birds). Annotation is provided as a binary indicator of source presence for one second, that is 8 consecutive 125 ms frames with a hop of one frame.
DOI: 10.5281/zenodo.6979745
title: Cone-Beam Computed Tomography Dataset of a Seashell

description: <strong>Summary</strong> This dataset is a collection of X-ray projection images of a seashell imaged in a cone-beam computed tomography (CBCT) scanner. The dataset also includes a metadata file, specifying the scan geometry and other important scan parameters, and photographs of the sample and the measurement setup. <strong>Description</strong> <em>Sample Information</em> The sample is an empty seashell of an unknown species, approximately 4.3 cm in length and 2.5 cm in diameter. The sample was placed in a plastic tube filled with cotton wool to prevent unwanted motion during the scan. <em>Scanner</em> The measurements were acquired using a cone-beam computed tomography scanner designed and constructed in-house in the Industrial Mathematics Computed Tomography Laboratory at the University of Helsinki. The scanner consists of a molybdenum target X-ray tube (Oxford Instruments XTF5011), a motorized rotation stage (Thorlabs CR1-Z7), and a 12-bit, 2240x2368 pixel, energy-integrating flat panel detector (Hamatsu Photonics C7942CA-22). <em>Scan Settings </em> 721 X-ray projections were acquired using an angle increment of 0.5 degrees. The X-ray source voltage and tube current were set at 50 kV and 1 mA, respectively. The exposure time of the flat panel detector was set to 1000 ms. <em>Data Post-Processing</em> Two correction images were acquired before scanning the sample. A dark current image was created by averaging 100 images taken with the X-ray source off. A flat-field image was created by averaging 100 images taken with the X-ray source switched on with no sample placed in the scanner. After the scan, dark current and flat-field corrections were applied to each projection image using the Hamamatsu HiPic imaging software version 9.3. <em>Data Format</em> The X-ray projections are stored in .tif format. The metadata is contained in .txt file with formatting that is both human-readable and machine-readable. <em>Notes</em> Due to a slightly misaligned center of rotation in the scanner, the CT reconstructions can appear blurry. It was empirically observed that this problem can be compensated for quite well by shifting each projection left by 4 pixels, using circular boundary conditions, before performing any other operations on the projections. <strong>Research Group</strong> This dataset was produced by the Inverse Problems research group at the Department of Mathematics and Statistics at the University of Helsinki, Finland: https://www2.helsinki.fi/en/researchgroups/inverse-problems. <strong>Additional Links</strong> To get started with the data, we recommend looking at the HelTomo toolbox, specifically created for working with CBCT data collected in the Industrial Mathematics Computed Tomography Laboratory, and available at https://github.com/Diagonalizable/HelTomo <strong>Contact Details</strong> For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.
DOI: 10.5281/zenodo.3977825
title: Dataset and analysis script for the manuscript "Status, rivalry and admiration-seeking in narcissism and depression: a behavioral study"

description: <em>Datasets and analysis script for the manuscript "Status, rivalry and admiration-seeking in narcissism and depression: a behavioral study".</em> <em>The attached R script begins with a read-me section with a short explanation of all variables present in the datasets. This information is also uploaded as a separate readMe in .txt format. Analyses are organized with respect to their order of appearance in the preprint (https://doi.org/10.31234/osf.io/mxve9).</em> <em>The data is uploaded in two datasets: a long-format one (ds_pooled.csv) employed in the trial-by-trial regression models and a wide-format one for supplemental subject-level analyses (ds_pooled_wide.csv). The script contains code to further generate separate datasets for Samples 1 and 2 in both the long- and the wide-format. Please, refer to the manuscript for detailed information about how the data has been collected and analyzed.</em> <em>Note: The ds_items.zip file contains datasets with item scores of the included psychometric scales (trait-dominance, narcissism scales and depression scales). These files are only used in a short section of the script to compute reliability coefficients.</em>
DOI: 10.5281/zenodo.4131651
title: Practice of antibiotics use and awareness of antimicrobial resistance among undergraduate medical students

description: Antibiotic resistance is one of the actual problems associated with the irrational use of antibiotics The aim of study is to investigate practice of antibiotics use and awareness of antimicrobial resistance among undergraduate medical students Siberian State Medical University (SSMU). Material and methods. A self-administered online questionnaire was used to assess the practice of antibiotic use and awareness of antimicrobial resistance among 5-6 year students of GP and pediatrician faculties of Siberian State Medical University. Results. 334 students took part in the survey. During the last year, 58.1% of respondents used antibacterial drugs. The leading reasons for the use of antibiotics were: angina - 47.3%, viral infection of upper airway- 24.4%, sinusitis and / or otitis - 20.2%, urinary tract infections - 18.3%, bronchitis - 11.5%, pneumonia - 8 %, acute intestinal infections - 5.7%. The most frequently used: amoxicillin / clavulanic acid -57.6%, amoxicillin - 29%, azithromycin - 19.4%, ciprofloxacin - 13.4%, cephalosporins of the 3rd generation - 8.1%, clarithromycin - 7.6%. Most of the students used antibiotics as prescribed by a doctor (58.8%) and adhered to the duration of treatment prescribed by the doctor. The vast majority of students (99.4%) are aware of the problem of antibiotic resistance and 95.8% consider this problem important for Russian medicine. Conclusions. Undergraduate students of the Siberian State Medical University demonstrated adherence to medical recommendations regarding the use of antibiotics and a high awareness of the problem of antibiotic resistance.
DOI: 10.1594/pangaea.78892
title: Chlorophyll-a at UR03_Adria4a
DOI: 10.34740/kaggle/dsv/9109147
title: ⏳ endoflife.date database (duckdb)
DOI: 10.5281/zenodo.1028747
title: N2 Schafer Lab N2 (Bristol, Uk) | 2010-01-21T11:00:27+00:00

description: This experiment is part of the <em>C.elegans behavioural database</em>. For more information and the complete collection of experiments visit http://movement.openworm.org



<b>preview link</b> : https://www.youtube.com/watch?v=amvFDvxAyUk
<b>strain</b> : N2
<b>timestamp</b> : 2010-01-21T11:00:27+00:00
<b>gene</b> : -N/A-
<b>chromosome</b> : -N/A-
<b>allele</b> : -N/A-
<b>strain_description</b> : Schafer Lab N2 (Bristol, UK)
<b>sex</b> : hermaphrodite
<b>stage</b> : adult
<b>ventral_side</b> : anticlockwise
<b>media</b> : NGM agar low peptone
<b>arena</b> :   
  <b>style</b> : petri
  <b>size</b> : 35
  <b>orientation</b> : away
  

<b>food</b> : OP50
<b>habituation</b> : 30m wait
<b>who</b> : Laura Grundy
<b>protocol</b> : Method in E. Yemini et al. doi:10.1038/nmeth.2560. Worm transferred to arena 30 minutes before recording starts.
<b>lab</b> :   
  <b>name</b> : William R Schafer
  <b>location</b> : MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK
  

<b>software</b> :   
  <b>name</b> : tierpsy (https://github.com/ver228/tierpsy-tracker)
  <b>version</b> : cbfc23eb4f1ac2f29be75ade7a937eed58a5b219
  <b>featureID</b> : @OMG
  

<b>base_name</b> : N2 on food_2010_01_21__11_00_27___1___1
<b>total time (s)</b> : 899.562
<b>frames per second</b> : 31.9489
<b>video micrometers per pixel</b> : 4.46935
<b>number of segmented skeletons</b> : 19068
DOI: 10.5281/zenodo.3957257
title: Spatial Deconvolution of HER2-positive Breast Tumors Reveals Novel Intercellular Relationships | Data

description: Processed count matrices, brightfield HE-images (plain and annotated), spot selection files and meta-data associated with the manuscript "<em>Spatial Deconvolution of HER2-positive Breast Tumors Reveals Novel Intercellular Relationships</em>". The content of the files are: <strong>count-matrices.zip</strong> - processed count matrices formatted as [n_spots]x[n_genes] and named as [PATIENT][SECTION].tsv.gz. <strong>images.zip - </strong>contains two folders HE and annotation, the former holds the HE-images for respective section named as [PATIENT][SECTION].jpg, the latter holds the annotated (by the pathologist) images named by patient (only one section from each patient was annotated). <strong>spot-selection.zip - </strong>contains .tsv files to map array coordinates to pixel coordinates, allowing the spots and their associated expression values to be visualized jointly. Files are named as [PATIENT][SECTION]_selection.tsv.gz <strong>meta.zip - </strong>for all annotated sections, these files are similar to the spot-selection files, but also includes the label of each spot (e.g., breast glands, connective tissue, etc.). All files are password protected. The password will be released upon publication, until then it is exclusively shared with the reviewers.
DOI: 10.5281/zenodo.44758
title: Nueva Serie De Extensión Del Hielo Marino Ártico En Septiembre Entre 1935 Y 2014 - A New Time Series Of September Arctic Sea Ice Extent: 1935-2014

description: Archivo CSV - Presentamos una nueva serie de extensión del hielo marino ártico en el mes de septiembre desde 1935 hasta 2014 que incluye datos para el sector siberiano no utilizados hasta ahora en las series que cubren el conjunto del Ártico. La nueva serie ha sido ajustada para ser consistente con los datos de satélite

CSV file - We present a new time series of September Arctic sea ice extent from 1935 to 2014 that includes data for the Siberian sector (AARI operational charts) not used previously in the Arctic wide existing time series (Walsh, HadISST). The new record has been adjusted to be consistent with the satellite data.
description: Data from: Cea-Pirón, MA y Cano-Pasalodos, JA (2016): Nueva serie de extensión del hielo marino ártico en septiembre entre 1935 y 2014. Rev. Climatol., 16: 1-19 . https://doi.org/10.5281/zenodo.44756
DOI: 10.5281/zenodo.4554725
title: Radar and satellite joint observations on super typhoon Lekima (2019)

description: The enclosed is the S-band Doppler radar and GPM satellite joint observational data used for the analysis of typhoon Lekima (2019).
DOI: 10.5281/zenodo.4584775
title: Future global land datasets with a 1-km resolution based on the SSP-RCP scenarios

description: The continuous strengthening of human activities and intensification of climate change have jointly affected global land cover changes. Scenario-based land change simulation has been widely used as a powerful tool that can assess the impact of potential land changes. However, the current future land projection products at a global scale generally suffer from deficiencies such as low resolution, unsuitable scenarios, and few types of land. Therefore, we propose a series global land projection datasets with a resolution of 1-km from 2015 to 2100, which uses the latest IPCC coupling scenarios of socioeconomic and climate change, SSP-RCP.<br> Actually, there are two subdatasets: 1) the 1-km global LULC dataset containing seven land types; 2) the 1-km global PFT-based land dataset containing 20 land types. The production of the LULC dataset combines the top-down land demand constraints provided by the official dataset of CMIP6 and the bottom-up spatial simulation executed by cellular automata (CA). The land demand from official data sources ensures the authority of the land trajectories under different SSP-RCP scenarios. The CA model is executed separately in different regions of the world to reflect spatial heterogeneity. Based on the LULC dataset, we further subdivide the its land types into 20 types according to climate data. The results show that our global land simulation achieves a satisfactory accuracy. The spatial change of various land types in each scenario appropriately reflects the storyline of the corresponding scenario. We believe that our dataset can provide stronger data support for climate research, including climate models, due to its advantages in resolution, scenarios, and land types. Please cite the corresponding paper: Chen, G., Li, X. & Liu, X. Global land projection based on plant functional types with a 1-km resolution under socio-climatic scenarios. <em>Sci Data</em> 9, 125 (2022).
DOI: 10.5281/zenodo.5764048
title: PDAC_Pei

description: PDAC dataset curated by BHK Lab.
DOI: 10.15468/dl.g8ncv3
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((149.624903 -32.358004, 149.624903 -32.36025, 149.627149 -32.36025, 149.627149 -32.362496, 149.631641 -32.362496, 149.631641 -32.36025, 149.638378 -32.36025, 149.638378 -32.366987, 149.640624 -32.366987, 149.640624 -32.362496, 149.64287 -32.362496, 149.64287 -32.36025, 149.640624 -32.36025, 149.640624 -32.358004, 149.624903 -32.358004))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048202-240626123714530/datasets/export for details.
DOI: 10.5281/zenodo.4527462
title: Field data about pedestrian trajectories and head orientations in diverse situations during the Covid-19 pandemic

description: This dataset was obtained by recording pedestrian crowds in diverse settings in a<br> passive and privacy-respective way and manually tracking pedestrians to<br> extract their trajectories, head orientations, and information about their group<br> membership. These field data were collected in Lyon, France, between July 2020 and January 2021<br> at diverse times during the Covid-19. Details about the settings can be found in the reference below, as well as on the webpage https://seppieton.herokuapp.com/scenarios/ <strong>FORMAT </strong><br> The data are saved as text files, with each file corresponding to a different recording<br> place. The first line of the file is the header and explains the structure of each line: 1) <strong>ped_no</strong>: the pedestrian label (an integer)<br> 2) <strong>group_no</strong>: the label of the group to which the pedestrian was associated, by visual<br> identification of group members<br> 3) <strong>time</strong>: time point (in seconds)<br> 4) <strong>x</strong>: first coordinate (in metres) of the position of the centre of the pedestrian's head at this time<br> 5) <strong>y</strong>: second coordinate (in metres) of the position of the centre of the pedestrian's head at this time<br> 6) <strong>theta</strong>: angular orientation (in radian) of the pedestrian's head, i.e., the orientation<br> of the line joining the centre of the head to the mouth <br> The data are free to re-use, but please cite the reference below (or the associated<br> published version) if you make use of them for academic or business purposes. <strong>REFERENCE </strong> Garcia, W., Mendez, S. Fray, B., & Nicolas, A. Model-based assessment of the risks of viral<br> transmission in non-confined crowds, <em>Safety Science</em> <strong>144</strong> (2021), 105453, https://www.sciencedirect.com/science/article/pii/S0925753521002964. Also on arXiv:2012.08957 and<br> https://hal.archives-ouvertes.fr/hal-03060324/
description: Project funded by Agence Nationale de la Recherche (FR) under project name ANR-20-COV1-0003
description: {"references": ["arXiv:2012.08957"]}
DOI: 10.5281/zenodo.3257884
title: Raw in situ observational data sets for manuscript entitled "Effect of Typhoon Kalmaegi (2014) in northern South China Sea explored using Muti-platform satellite and Buoy observations data"

description: Raw in situ observational data sets for manuscript entitled "Effect of Typhoon Kalmaegi (2014) in northern South China Sea explored using Muti-platform satellite and Buoy observations data" , which has been submitted to Progress in Oceanography for reviewing. Only data sets collected by field observations were uploaded. Other data sets can be download from the relevant open access websites (see the descriptions in the manuscript).
DOI: 10.15468/dl.wngty9
title: Occurrence Download

description: A dataset containing 189 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((147.736186 -35.962511, 147.736186 -35.964757, 147.738432 -35.964757, 147.738432 -35.962511, 147.736186 -35.962511))"
 ]
}

 The dataset includes 189 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048203-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e9x3qb
title: Occurrence Download

description: A dataset containing 237 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON (((149.752914 -32.37597, 149.752914 -32.380462, 149.757406 -32.380462, 149.757406 -32.37597, 149.752914 -32.37597)), ((149.757406 -32.380462, 149.757406 -32.389445, 149.761897 -32.389445, 149.761897 -32.391691, 149.766389 -32.391691, 149.766389 -32.389445, 149.77088 -32.389445, 149.77088 -32.387199, 149.773126 -32.387199, 149.773126 -32.384954, 149.764143 -32.384954, 149.764143 -32.382708, 149.761897 -32.382708, 149.761897 -32.380462, 149.757406 -32.380462)), ((149.773126 -32.384954, 149.775372 -32.384954, 149.775372 -32.382708, 149.773126 -32.382708, 149.773126 -32.384954)))"
 ]
}

 The dataset includes 237 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048204-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a9czdv
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((147.646355 -35.964757, 147.646355 -35.969249, 147.655338 -35.969249, 147.655338 -35.964757, 147.646355 -35.964757))"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048205-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/dxs4y4stv4.1
title: Quantifying the Trade-off Between Yield and Contamination in Soil EPS Extraction Using CER

description: The study data set includes three soils using cation exchange resin (CER) methods to extract soil EPS content, intracellular and extracellular pollution, and optical properties. The aim of this study was to construct and optimize the extraction and quantitative analysis method of soil EPS. The extraction method of soil EPS by CER was taken as the research object, and the influence of different CER addition amount and extraction time on the extraction effect of EPS components and intracellular and extracellular pollution was characterized to further clarify the optimal extraction conditions of soil EPS by CER.
DOI: 10.15468/dl.rrt2c6
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((149.663082 -32.382708, 149.663082 -32.384954, 149.667574 -32.384954, 149.667574 -32.382708, 149.663082 -32.382708))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048206-240626123714530/datasets/export for details.
DOI: 10.5281/zenodo.10814159
title: Chemical composition, soil water content and 16S rRNA and ITS gene copy numbers of soil aggregates and bulk soil samples

description: This repository contains all data to reproduce the analyses presented in "Distinct microbial communities are linked to organic matter properties in millimetre-sized soil aggregates", Simon et al 2024, The ISME Journal (DOI: 10.1093/ismejo/wrae156).