cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20102854.v1
title: <i>K</i>-CDFs: a Nonparametric Clustering Algorithm via Cumulative Distribution Function

description: We propose a novel partitioning clustering procedure based on the cumulative distribution function (CDF), called <i>K</i>-CDFs. For univariate data, the <i>K</i>-CDFs represent the cluster centers by empirical CDFs and assign each observation to the closest center measured by the Crame´r-von Mises distance. The procedure is nonparametric and does not require assumptions on cluster distributions imposed by mixture models. A projection technique is used to generalize the <i>K</i>-CDFs for univariate data to an arbitrary dimension. The proposed procedure has several appealing properties. It is robust to heavy-tailed data, is not sensitive to the data dimensions, does not require moment conditions on data and can effectively detect linearly nonseparable clusters. To implement the <i>K</i>-CDFs, we propose two kinds of algorithms: a greedy algorithm as the classical Lloyd’s algorithm and a spectral relaxation algorithm. We illustrate the finite sample performance of the proposed algorithms through simulation experiments and empirical analyses of several real datasets.
DOI: 10.5066/p9nnn2xy
title: Tracking Data for Pacific Loons (Gavia pacifica)

description: This metadata document describes the data contained in the "processedData" folder of this data package. This data package contains data collected by the Argos System from 12 satellite transmitters attached to Pacific Loons on their breeding range in arctic, 2015-2016. The raw data were processed to accomplish two goals: flag implausible location estimates and decode raw sensor data. Two Comma Separate Value (CSV) tables are included in the "processedData" folder of this data package: 1) the "diag_filteredLocations" table contains one record for every location estimate, accompanied by a binary flag that denotes an algorithm's plausibility check. Each record also includes a 'Tracking_Status' variable that denotes whether the location was collected from a live animal, a dead animal, or shed transmitter, and 2) the "deploymentAttributes" table contains one record for each transmitter deployment in a CSV formatted table. The deployment attributes file contains information such as when the transmitter was attached to the animal, when tracking of a live animal ended, and a variety of variables describing the animal and transmitter.
DOI: 10.13026/m77n-sx13
title: Classification of 12-lead ECGs: The PhysioNet/Computing in Cardiology Challenge 2020

description: The electrocardiogram (ECG) is a non-invasive representation of the electrical
activity of the heart from electrodes placed on the surface of the torso. The
standard 12-lead ECG has been widely used to diagnose a variety of cardiac
abnormalities such as cardiac arrhythmias, and predicts cardiovascular
morbidity and mortality
[[1]](http://www.onlinejacc.org/content/49/10/1109.abstract). The early and
correct diagnosis of cardiac abnormalities can increase the chances of
successful treatments
[[2]](https://www.ahajournals.org/doi/full/10.1161/strokeaha.107.181486).
However, manual interpretation of the electrocardiogram is time-consuming, and
requires skilled personnel with a high degree of training
[[3]](https://www.magonlinelibrary.com/doi/abs/10.12968/bjca.2019.14.3.123).

Automatic detection and classification of cardiac abnormalities can assist
physicians in the diagnosis of the growing number of ECGs recorded. Over the
last decade, there have been increasing numbers of attempts to stimulate
12-lead ECG classification. Many of these algorithms seem to have the
potential for accurate identification of cardiac abnormalities. However, most
of these methods have only been tested or developed in single, small, or
relatively homogeneous datasets. The PhysioNet/Computing in Cardiology
Challenge 2020 provides an opportunity to address this problem by providing
data from a wide set of sources.
DOI: 10.13026/gt86-a263
title: Will Two Do? Varying Dimensions in Electrocardiography: The PhysioNet/Computing in Cardiology Challenge 2021

description: The electrocardiogram (ECG) is a non-invasive representation of the electrical
activity of the heart. Although the twelve-lead ECG is the standard diagnostic
screening system for many cardiological issues, the limited accessibility of
twelve-lead ECG devices provides a rationale for smaller, lower-cost, and
easier to use devices. While single-lead ECGs are limiting
[[1](https://pubmed.ncbi.nlm.nih.gov/9740885/)], reduced-lead ECG systems hold
promise, with evidence that subsets of the standard twelve leads can capture
useful information [[2](https://pubmed.ncbi.nlm.nih.gov/12539095)],
[[3](https://www.sciencedirect.com/science/article/abs/pii/S0022073606005346)],
[[4](https://pubmed.ncbi.nlm.nih.gov/3812249)] and even be comparable to
twelve-lead ECGs in some limited contexts. In 2017 we challenged the public to
classify AF from a single-lead ECG, and in 2020 we challenged the public to
diagnose a much larger number of cardiac problems using twelve-lead
recordings. However, there is limited evidence to demonstrate the utility of
reduced-lead ECGs for capturing a wide range of diagnostic information.

In this year's Challenge, we ask the following question: **' Will two do?'**
This year's Challenge builds on [last year's
Challenge](/content/challenge-2020/)
[[5]](https://doi.org/10.1088/1361-6579/abc960), which asked participants to
classify cardiac abnormalities from twelve-lead ECGs. We are asking you to
build an algorithm that can classify cardiac abnormalities from twelve-lead,
six-lead, four-lead, three-lead, and two-lead ECGs. We will test each
algorithm on databases of these reduced-lead ECGs, and the differences in
performances of the algorithms on these databases will reveal the utility of
reduced-lead ECGs in comparison to standard twelve-lead EGCs.
DOI: 10.13026/34va-7q14
title: Will Two Do? Varying Dimensions in Electrocardiography: The PhysioNet/Computing in Cardiology Challenge 2021

description: The electrocardiogram (ECG) is a non-invasive representation of the electrical
activity of the heart. Although the twelve-lead ECG is the standard diagnostic
screening system for many cardiological issues, the limited accessibility of
twelve-lead ECG devices provides a rationale for smaller, lower-cost, and
easier to use devices. While single-lead ECGs are limiting
[[1](https://pubmed.ncbi.nlm.nih.gov/9740885/)], reduced-lead ECG systems hold
promise, with evidence that subsets of the standard twelve leads can capture
useful information [[2](https://pubmed.ncbi.nlm.nih.gov/12539095)],
[[3](https://www.sciencedirect.com/science/article/abs/pii/S0022073606005346)],
[[4](https://pubmed.ncbi.nlm.nih.gov/3812249)] and even be comparable to
twelve-lead ECGs in some limited contexts. In 2017 we challenged the public to
classify AF from a single-lead ECG, and in 2020 we challenged the public to
diagnose a much larger number of cardiac problems using twelve-lead
recordings. However, there is limited evidence to demonstrate the utility of
reduced-lead ECGs for capturing a wide range of diagnostic information.

In this year's Challenge, we ask the following question: **' Will two do?'**
This year's Challenge builds on [last year's
Challenge](/content/challenge-2020/)
[[5]](https://doi.org/10.1088/1361-6579/abc960), which asked participants to
classify cardiac abnormalities from twelve-lead ECGs. We are asking you to
build an algorithm that can classify cardiac abnormalities from twelve-lead,
six-lead, four-lead, three-lead, and two-lead ECGs. We will test each
algorithm on databases of these reduced-lead ECGs, and the differences in
performances of the algorithms on these databases will reveal the utility of
reduced-lead ECGs in comparison to standard twelve-lead EGCs.
DOI: 10.34740/kaggle/dsv/4003473
title: Elon Musk Tweets
DOI: 10.5281/zenodo.6941532
title: Geospatial data on indicators for parks in the city of Berlin, Germany

description: The data contains features and indicators for 224 parks (at least 2 ha in size) in the city of Berlin and overall scores (indices) for natural elements, built elements (infrastructure) and spatial context (e.g. distance to public transport). All data is supplement to linked online web map. <strong>List of data and content</strong> Park_Berlin_Indicators: vector files (*.shp, *.geojson) Park_Berlin_Indicators: excel files (*.xlsx) <strong>Spatial reference</strong><br> All data is projected in ETRS 1989 UTM Zone 33N (EPSG:25833) <strong>Web-GIS</strong><br> View data and explore interactively using the online application. <strong>Data sources and processing</strong><br> For details on underlying data sources (e.g. availabilty, spatial resolution, time reference) and on data processing please refer to the linked publication, incl. Appendix 1 <strong>Acknowledgments</strong><br> We thank the City of Berlin for providing data. We greatly acknowledge OpenStreetMap (OSM) and contributers for providing important parts of the used data. This work was supported by the research project “Environmental‐Health Interactions in Cities (GreenEquityHEALTH) ‐ Challenges for Human Well‐Being under Global Changes” (project duration 2017–2022), funded by the German Federal Ministry of Education and Research (BMBF; no.01LN1705A). <strong>Based on related original publication</strong><br> Kraemer, R., & Kabisch, N. (2021). Parks in context: Advancing citywide spatial quality assessments of urban green spaces using fine-scaled indicators. Ecology and Society, 26(2). https://doi.org/10.5751/ES-12485-260245
DOI: 10.5281/zenodo.6941531
title: Geospatial data on indicators for parks in the city of Berlin, Germany

description: The data contains features and indicators for 224 parks (at least 2 ha in size) in the city of Berlin and overall scores (indices) for natural elements, built elements (infrastructure) and spatial context (e.g. distance to public transport). All data is supplement to linked online web map. <strong>List of data and content</strong> Park_Berlin_Indicators: vector files (*.shp, *.geojson) Park_Berlin_Indicators: excel files (*.xlsx) <strong>Spatial reference</strong><br> All data is projected in ETRS 1989 UTM Zone 33N (EPSG:25833) <strong>Web-GIS</strong><br> View data and explore interactively using the online application. <strong>Data sources and processing</strong><br> For details on underlying data sources (e.g. availabilty, spatial resolution, time reference) and on data processing please refer to the linked publication, incl. Appendix 1 <strong>Acknowledgments</strong><br> We thank the City of Berlin for providing data. We greatly acknowledge OpenStreetMap (OSM) and contributers for providing important parts of the used data. This work was supported by the research project “Environmental‐Health Interactions in Cities (GreenEquityHEALTH) ‐ Challenges for Human Well‐Being under Global Changes” (project duration 2017–2022), funded by the German Federal Ministry of Education and Research (BMBF; no.01LN1705A). <strong>Based on related original publication</strong><br> Kraemer, R., & Kabisch, N. (2021). Parks in context: Advancing citywide spatial quality assessments of urban green spaces using fine-scaled indicators. Ecology and Society, 26(2). https://doi.org/10.5751/ES-12485-260245
DOI: 10.25919/v0em-cn53
title: Parkes observations for project PX600 semester 2020APRS_74

description: The Galactic Center (GC) is a key target for the radio component of Breakthrough Listen (BL) program. Offering the largest amount of stars in any given direction in the sky, the GC is widely cited as a location believed to harbour advanced civilizations, and it is also the most energetic region in the Milky Way. We aim to conduct BL-GC Survey with Parkes radio telescope for around 350 hours. We will cover the frequency range from 700 MHz to 4 GHz, utilizing the newly installed ultra-wideband receiver. Our team has already leveraged both standard and bespoke tools to construct a flexible software stack to search data for signals of interest. We will look for signals from extraterrestrial intelligence (ETI) by searching for both simple narrow-band signals and complex modulated signals. Along with that, as an ancillary science, we will also search for accelerated pulsars — likely orbiting a supermassive black hole at the centre of the Milky Way Galaxy. Data products produced during this program will also be publically available to engage larger pulsar community
DOI: 10.25545/kcyyc1
title: Truesense Raw (RGBW) Images

description: This dataset contains 7 raw images captured by a Truesense Colour Filter Array (KAI-04050C sensor) in BOBCAT IGV-B2320 v1.0 camera, manufactured by IMPERX. For the specifications of the camera please see “Bobcat Intelligent camera series IGV-B2320, 2013”.
DOI: 10.5281/zenodo.5772691
title: Supplemental image data for "Mechanical annealing and memories in a disordered solid"

description: This archive contains raw and subtracted experimental images of a 2D colloidal disordered solid, sufficient to verify the major qualitative results of the paper "Mechanical annealing and memories in a disordered solid" by Nathan C. Keim and Dani Medina. Descriptions of the images may be found in the main text and Supplementary Information for that paper, a preprint of which is available at arXiv.org.
DOI: 10.5281/zenodo.5772690
title: Supplemental image data for "Mechanical annealing and memories in a disordered solid"

description: This archive contains raw and subtracted experimental images of a 2D colloidal disordered solid, sufficient to verify the major qualitative results of the paper "Mechanical annealing and memories in a disordered solid" by Nathan C. Keim and Dani Medina. Descriptions of the images may be found in the main text and Supplementary Information for that paper, a preprint of which is available at arXiv.org.
DOI: 10.6084/m9.figshare.3761562.v2294
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.20405607.v1
title: Spreadsheet The psychological impact of COVID-19 pandemic on primary health care professionals in Greece a cross-sectional study.xlsx

description: All variables are labeled (labels are displayed in the dataset spreadsheet) and de-identified in the excel spreadsheet. Missing data were left blank in the excel spreadsheet. Data were analyzed in R software (version 4.1.3) package. Analysis involved descriptive statistics (frequencies and standard deviations) to present sociodemographic characteristics, participants’ experience with COVID-19 and anxiety and depression levels according to the score in GAD-7 and PHQ-9 test. In order to calculate participants’ level of depression each item of the PHQ-9 questionnaire was rated on a 4-point Likert scale and a final total score was calculated. Answers to every item range from 0 (not at all) to 3 (nearly every day). More specifically, “not at all”: 0 points, “Several days”: 1 point, “More than half the days”:2 points and “nearly every day”:3 points. The sum of all 9 items (presented in the study questionnaire file) was used to determine the level of depression for every participant. Total scores ranging from 0 to 4 were rated as “minimal or none”, 5 to 9 as “mild”, 10 to14 as “moderate”, 15 to 19 as “moderately severe”, and 20 to 27 as “severe”. A cut-off point of 10 or above is indicative of major depressive disorder. In order to calculate participants’ level of anxiety, each item of the GAD-7 questionnaire, 7-item questionnaire (presented in the study questionnaire file) was rated on a 4-point Likert scale and answers to every item range from 0 (not at all) to 3 (nearly every day). More specifically, “not at all”: 0 points, “Several days”: 1 point, “More than half the days”:2 points and “nearly every day”:3 points. The sum of all 7 items was used to determine the level of anxiety for every participant. Total scores ranging from 0 to 4 were classified as “not at all”, 5 to 9 as “mildly”, 10 to 14 as “moderately” and ≥15 as “severely”. A cut-off point of 10 or above corresponds to moderate to severe anxiety disorder. After calculating the GAD-7 and PHQ-9 score for each participant, univariate analysis was conducted to evaluate the association between sociodemographic factors and participants’ experience with COVID-19 with the levels of anxiety and depression respectively. Multivariable logistic regression in R was used to determine the association between independent variables (socio-demographic characteristics and participants’ experience with COVID-19) which yielded statistically significant results at the univariate analysis (gender, age, facility location, previous infection, relative/friend hospitalized or deceased, being in a high risk group) with the dichotomous dependent variables determined by the cut-off point of 10 in the GAD-7 and PHQ-9 indicating clinically significant levels of anxiety and depression, respectively. Two-tailed p values of less than 0.05 were deemed statistically significant.
DOI: 10.5281/zenodo.6941628
title: Gradients4-TN397-PPPCPN-UW

description: Cruise Name: Gradients 4 , TN397 Vessel: R/V Thompson Funding: Simons Collaboration on Ocean Processes and Ecology (SCOPE) Dataset description: This dataset was collected on the R/V Thompson during the Gradients IV cruise which departed from San Diego, CA and ended in Honolulu, HI. A transect was performed on the 140 degree longitude line from roughly 19 degrees N to 2.5 degrees S before heading to Honolulu. The phosphorus content of particulate material (PP) was collected by semi-automated and sequential filtration of the ship’s underway flow through seawater onto acid-washed and combusted GFF filters. Select samples had a piggybacked GFF filter holder in sequence for collection of dissolved organic material and/or evaluation of trapping efficiency of GFF. Following filtration, filters were placed in histoprep capsules and stored at -80°C for later analysis. Post-cruise, all filters were thawed overnight, combusted at 450°C for 4.5 hours, then extracted with 0.15 M HCl for 1 hour at 60°C. PP was then analyzed spectrophotometrically as SRP in a 1.0 cm cell at 880 nm following Strickland and Parsons (1972). Recovery efficiency was assessed from the analysis of a known dry weight of certified reference material (National Institute of Standards, NIST 1515, apple leaves, certified 0.159% P by weight). The carbon and nitrogen content of particulate material (PC, PN) were collected by semi-automated and sequential filtration of the ship’s underway flow through seawater onto a combusted GFF filter. Select samples had a piggybacked GFF filter holder in sequence for collection of dissolved organic material and/or evaluation of trapping efficiency of GFF. Following filtration, filters were folded in half and placed in combusted aluminum foil and stored at -80°C for later analysis. Post-cruise, all filters were thawed, acidified, and dried overnight at 60°C. Samples were then balled into Ag and Sn boats and plated for analysis. Organic carbon and total nitrogen content were analyzed using high-temperature combustion (1020 C) on an elemental analyzer. Three standards with known %C and %N were weighed, folded in tin boats, and analyzed with the samples to develop the calibration equation for each run. The calibration equation was then used to calculate the mass (mg) of carbon and nitrogen in each sample using EAS Clarity Chromatography Station software. The ship’s location, sea surface temperature and salinity from ship’s underway instrumentation are matched to the mid-point of each PCPN filtration time, and PP data were matched to the closest PCPN filtration. Depth of 8m refers to the depth of the seawater intake on the R/V Thompson.
DOI: 10.5281/zenodo.6941627
title: Gradients4-TN397-PPPCPN-UW

description: Cruise Name: Gradients 4 , TN397 Vessel: R/V Thompson Funding: Simons Collaboration on Ocean Processes and Ecology (SCOPE) Dataset description: This dataset was collected on the R/V Thompson during the Gradients IV cruise which departed from San Diego, CA and ended in Honolulu, HI. A transect was performed on the 140 degree longitude line from roughly 19 degrees N to 2.5 degrees S before heading to Honolulu. The phosphorus content of particulate material (PP) was collected by semi-automated and sequential filtration of the ship’s underway flow through seawater onto acid-washed and combusted GFF filters. Select samples had a piggybacked GFF filter holder in sequence for collection of dissolved organic material and/or evaluation of trapping efficiency of GFF. Following filtration, filters were placed in histoprep capsules and stored at -80°C for later analysis. Post-cruise, all filters were thawed overnight, combusted at 450°C for 4.5 hours, then extracted with 0.15 M HCl for 1 hour at 60°C. PP was then analyzed spectrophotometrically as SRP in a 1.0 cm cell at 880 nm following Strickland and Parsons (1972). Recovery efficiency was assessed from the analysis of a known dry weight of certified reference material (National Institute of Standards, NIST 1515, apple leaves, certified 0.159% P by weight). The carbon and nitrogen content of particulate material (PC, PN) were collected by semi-automated and sequential filtration of the ship’s underway flow through seawater onto a combusted GFF filter. Select samples had a piggybacked GFF filter holder in sequence for collection of dissolved organic material and/or evaluation of trapping efficiency of GFF. Following filtration, filters were folded in half and placed in combusted aluminum foil and stored at -80°C for later analysis. Post-cruise, all filters were thawed, acidified, and dried overnight at 60°C. Samples were then balled into Ag and Sn boats and plated for analysis. Organic carbon and total nitrogen content were analyzed using high-temperature combustion (1020 C) on an elemental analyzer. Three standards with known %C and %N were weighed, folded in tin boats, and analyzed with the samples to develop the calibration equation for each run. The calibration equation was then used to calculate the mass (mg) of carbon and nitrogen in each sample using EAS Clarity Chromatography Station software. The ship’s location, sea surface temperature and salinity from ship’s underway instrumentation are matched to the mid-point of each PCPN filtration time, and PP data were matched to the closest PCPN filtration. Depth of 8m refers to the depth of the seawater intake on the R/V Thompson.