cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0191292.10fu
title: SH0191292.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0191293.10fu
title: SH0191293.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0191294.10fu
title: SH0191294.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0191295.10fu
title: SH0191295.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15468/dl.sb3see
title: Occurrence Download

description: A dataset containing 359 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ambystoma rivulare (Taylor, 1940)"
 ]
}

 The dataset includes 359 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022740-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25398/rd.northumbria.28382021
title: Account Status qualitative survey amongst pole dancing content creators

description: Anonymised findings from a qualitative survey amongst 100 pole dancing content creators, asking them about their experiences of appealing shadowbanned content and profiles via Instagram's Account Status.
DOI: 10.15468/dl.nj3wbp
title: Occurrence Download

description: A dataset containing 706 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Morocco",
 "TaxonKey is Macaca sylvanus (Linnaeus, 1758)"
 ]
}

 The dataset includes 706 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022741-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28071708
title: A Bias-Accuracy-Privacy Trilemma for Statistical Estimation

description: Differential privacy (DP) is a rigorous notion of data privacy, used for private statistics. The canonical algorithm for differentially private mean estimation is to first clip the samples to a bounded range and then add noise to their empirical mean. Clipping controls the sensitivity and, hence, the variance of the noise that we add for privacy. But clipping also introduces statistical bias. This tradeoff is inherent: we prove that no algorithm can simultaneously have low bias, low error, and low privacy loss for arbitrary distributions. Additionally, we show that under strong notions of DP (i.e., pure or concentrated DP), unbiased mean estimation is impossible, even if we assume that the data is sampled from a Gaussian. On the positive side, we show that unbiased mean estimation is possible under a more permissive notion of differential privacy (approximate DP) if we assume that the distribution is symmetric. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.28071708.v1
title: A Bias-Accuracy-Privacy Trilemma for Statistical Estimation

description: Differential privacy (DP) is a rigorous notion of data privacy, used for private statistics. The canonical algorithm for differentially private mean estimation is to first clip the samples to a bounded range and then add noise to their empirical mean. Clipping controls the sensitivity and, hence, the variance of the noise that we add for privacy. But clipping also introduces statistical bias. This tradeoff is inherent: we prove that no algorithm can simultaneously have low bias, low error, and low privacy loss for arbitrary distributions. Additionally, we show that under strong notions of DP (i.e., pure or concentrated DP), unbiased mean estimation is impossible, even if we assume that the data is sampled from a Gaussian. On the positive side, we show that unbiased mean estimation is possible under a more permissive notion of differential privacy (approximate DP) if we assume that the distribution is symmetric. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.28057262
title: Sensitivity Analysis for Quantiles of Hidden Biases in Matched Observational Studies

description: Causal conclusions from observational studies may be sensitive to unmeasured confounding. In such cases, a sensitivity analysis is often conducted, which tries to infer the minimum amount of hidden biases or the minimum strength of unmeasured confounding needed in order to explain away the observed association between treatment and outcome. If the needed bias is large, then the treatment is likely to have significant effects. The Rosenbaum sensitivity analysis is a modern approach for conducting sensitivity analysis in matched observational studies. It investigates what magnitude the maximum of hidden biases from all matched sets needs to be in order to explain away the observed association. However, such a sensitivity analysis can be overly conservative and pessimistic, especially when investigators suspect that some matched sets may have exceptionally large hidden biases. In this article, we generalize Rosenbaum’s framework to conduct sensitivity analysis on quantiles of hidden biases from all matched sets, which are more robust than the maximum. Moreover, the proposed sensitivity analysis is simultaneously valid across all quantiles of hidden biases and is thus a free lunch added to the conventional sensitivity analysis. The proposed approach works for general outcomes, general matched studies and general test statistics. In addition, we demonstrate that the proposed sensitivity analysis also works for bounded null hypotheses when the test statistic satisfies certain properties. An R package implementing the proposed approach is available online. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.28057262.v1
title: Sensitivity Analysis for Quantiles of Hidden Biases in Matched Observational Studies

description: Causal conclusions from observational studies may be sensitive to unmeasured confounding. In such cases, a sensitivity analysis is often conducted, which tries to infer the minimum amount of hidden biases or the minimum strength of unmeasured confounding needed in order to explain away the observed association between treatment and outcome. If the needed bias is large, then the treatment is likely to have significant effects. The Rosenbaum sensitivity analysis is a modern approach for conducting sensitivity analysis in matched observational studies. It investigates what magnitude the maximum of hidden biases from all matched sets needs to be in order to explain away the observed association. However, such a sensitivity analysis can be overly conservative and pessimistic, especially when investigators suspect that some matched sets may have exceptionally large hidden biases. In this article, we generalize Rosenbaum’s framework to conduct sensitivity analysis on quantiles of hidden biases from all matched sets, which are more robust than the maximum. Moreover, the proposed sensitivity analysis is simultaneously valid across all quantiles of hidden biases and is thus a free lunch added to the conventional sensitivity analysis. The proposed approach works for general outcomes, general matched studies and general test statistics. In addition, we demonstrate that the proposed sensitivity analysis also works for bounded null hypotheses when the test statistic satisfies certain properties. An R package implementing the proposed approach is available online. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.5281/zenodo.14840053
title: unimpeded: nlcdm bao.sdss_dr16+planck_2018_plik

description: cosmological model:nlcdm, dataset:bao.sdss_dr16+planck_2018_plik
DOI: 10.5281/zenodo.14840054
title: unimpeded: nlcdm bao.sdss_dr16+planck_2018_plik

description: cosmological model:nlcdm, dataset:bao.sdss_dr16+planck_2018_plik
DOI: 10.6084/m9.figshare.28071709.v1
title: Estimation and Inference for Nonparametric Expected Shortfall Regression over RKHS

description: Expected shortfall (ES) has emerged as an important metric for characterizing the tail behavior of a random outcome, specifically associated with rarer events that entail severe consequences. In climate science, the threats of flooding and heatwaves loom large, impacting natural environments and human communities. In actuarial studies, a key observation in modeling insurance claim sizes is that features exhibit distinct effects in explaining small and large claims. This paper concerns nonparametric expected shortfall regression as a class of statistical methods for tail learning. These methods directly target upper/lower tail averages and will empower practitioners to address complex questions that are beyond the reach of mean regression-based approaches. Using kernel ridge regression, we introduce a two-step nonparametric ES estimator that involves a plugged-in quantile function estimate without sample-splitting. We provide non-asymptotic estimation and Gaussian approximation error bounds, depending explicitly on the effective dimension, sample size, regularization parameters, and quantile estimation error. To construct pointwise confidence bands, we propose a fast multiplier bootstrap procedure and establish its validity. We demonstrate the finite-sample performance of the proposed methods through numerical experiments and an empirical study aimed at examining the heterogeneous effects of different air pollutants and meteorological factors on average and high PM2.5 concentration.
DOI: 10.15468/dl.sx9qxd
title: Occurrence Download

description: A dataset containing 4230 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Melipona seminigra Friese, 1903"
 ]
}

 The dataset includes 4230 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022742-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57967/hf/4448
title: nyc-congestionpricing-cv
DOI: 10.15468/dl.3axck2
title: Occurrence Download

description: A dataset containing 1346 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 {
 "or" : [
 "Country is Angola",
 "Country is Bahrain",
 "Country is Benin",
 "Country is Cameroon",
 "Country is Comoros",
 "Country is Congo",
 "Country is Congo, Democratic Republic of the",
 "Country is Côte d’Ivoire",
 "Country is Djibouti",
 "Country is Egypt",
 "Country is Eritrea",
 "Country is Equatorial Guinea",
 "Country is Gabon",
 "Country is Gambia",
 "Country is Ghana",
 "Country is Guinea",
 "Country is Guinea-Bissau",
 "Country is Iran (Islamic Republic of)",
 "Country is Iraq",
 "Country is Jordan",
 "Country is Kenya",
 "Country is Kuwait",
 "Country is Liberia",
 "Country is Madagascar",
 "Country is Mauritania",
 "Country is Mauritius",
 "Country is Mozambique",
 "Country is Nigeria",
 "Country is Oman",
 "Country is Qatar",
 "Country is Réunion",
 "Country is Saudi Arabia",
 "Country is Senegal",
 "Country is Seychelles",
 "Country is Sierra Leone",
 "Country is Somalia",
 "Country is South Africa",
 "Country is South Sudan",
 "Country is Sudan",
 "Country is Tanzania, United Republic of",
 "Country is Togo",
 "Country is United Arab Emirates",
 "Country is Yemen",
 "Country is Zambia",
 "Country is Zimbabwe"
 ]
 },
 "TaxonKey is Avicennia marina (Forssk.) Vierh.",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil)"
 ]
 }
 },
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (VAGRANT)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 {
 "not" : {
 "DegreeOfEstablishment" : [
 "is one of (CULTIVATED)"
 ]
 }
 },
 "DegreeOfEstablishment is null"
 ]
 }
 ]
}

 The dataset includes 1346 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022743-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28380977
title: Accurate Through-Wall Breathing Rate Measurements using a C-Band FMCW Radar

description: This dataset presents a collection of through-wall breathing rate (BR) measurements obtained using a custom-built C-Band Frequency Modulated Continuous Wave (FMCW) radar operating within the 4–6 GHz frequency range. The dataset was created as part of a research study on non-invasive biometric identification through physical barriers and is particularly valuable for researchers working on radar-based vital sign monitoring, signal processing, and artificial intelligence (AI) models applied to biomedical applications.The dataset consists of breathing rate measurements from 10 individuals, including 5 males and 5 females. The radar signals were recorded through a 30 cm thick brick and cement wall at distances of 1m, 3m, and 4m. The data collection process was carefully designed to ensure high accuracy, with ground truth validation provided by the Biopac system (MP36R). The radar system used a center frequency of 5 GHz, a bandwidth of 2 GHz, a transmitted power of 20 dBm, and an antenna gain of 12 dBi. The chirp duration was set to 1 ms, and the sampling rate was 1 Msps, ensuring high-resolution signal acquisition.The dataset includes raw radar signals (intermediate frequency signals), extracted phase signals for breathing rate estimation, corresponding ground truth breathing rates from the Biopac system, and both time-domain and frequency-domain processing results. These data points make the dataset valuable for applications such as radar-based biometric identification, through-wall sensing and security applications, healthcare monitoring with contactless breathing rate detection, AI-based signal processing for vital signs extraction, and the optimization of radar signal processing techniques.In terms of performance, the FMCW radar system demonstrated high accuracy when compared to the Biopac reference measurements, achieving a Mean Absolute Error (MAE) of 0.07 BPM and a Root Mean Square Error (RMSE) of 0.09 BPM. Agreement analysis showed a strong correlation (R² ≈ 0.999) between the radar-based measurements and the ground truth data, confirming the reliability of the system for through-wall breathing rate detection.This dataset was collected in compliance with ethical guidelines approved by the Ethics and Deontology Committee of the University of Aveiro (No. 10-CED/2023). All participants provided informed consent before data acquisition. The dataset serves as a valuable resource for researchers developing advanced non-contact monitoring systems and exploring new methodologies in radar-based physiological sensing.
DOI: 10.15468/dl.mb6xhe
title: Occurrence Download

description: A dataset containing 45 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pinnotherelia laevigata H.Milne Edwards & Lucas, 1843"
 ]
}

 The dataset includes 45 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022746-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14840067
title: unimpeded: nlcdm bicep_keck_2018+planck_2018_CamSpec

description: cosmological model:nlcdm, dataset:bicep_keck_2018+planck_2018_CamSpec
DOI: 10.5281/zenodo.14840068
title: unimpeded: nlcdm bicep_keck_2018+planck_2018_CamSpec

description: cosmological model:nlcdm, dataset:bicep_keck_2018+planck_2018_CamSpec
DOI: 10.15468/dl.yxuhns
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 {
 "or" : [
 "Country is Angola",
 "Country is Bahrain",
 "Country is Benin",
 "Country is Cameroon",
 "Country is Comoros",
 "Country is Congo",
 "Country is Congo, Democratic Republic of the",
 "Country is Côte d’Ivoire",
 "Country is Djibouti",
 "Country is Egypt",
 "Country is Eritrea",
 "Country is Equatorial Guinea",
 "Country is Gabon",
 "Country is Gambia",
 "Country is Ghana",
 "Country is Guinea",
 "Country is Guinea-Bissau",
 "Country is Iran (Islamic Republic of)",
 "Country is Iraq",
 "Country is Jordan",
 "Country is Kenya",
 "Country is Kuwait",
 "Country is Liberia",
 "Country is Madagascar",
 "Country is Mauritania",
 "Country is Mauritius",
 "Country is Mozambique",
 "Country is Nigeria",
 "Country is Oman",
 "Country is Qatar",
 "Country is Réunion",
 "Country is Saudi Arabia",
 "Country is Senegal",
 "Country is Seychelles",
 "Country is Sierra Leone",
 "Country is Somalia",
 "Country is South Africa",
 "Country is South Sudan",
 "Country is Sudan",
 "Country is Tanzania, United Republic of",
 "Country is Togo",
 "Country is United Arab Emirates",
 "Country is Yemen",
 "Country is Zambia",
 "Country is Zimbabwe"
 ]
 },
 "TaxonKey is Avicennia officinalis L.",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil)"
 ]
 }
 },
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (VAGRANT)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 {
 "not" : {
 "DegreeOfEstablishment" : [
 "is one of (CULTIVATED)"
 ]
 }
 },
 "DegreeOfEstablishment is null"
 ]
 }
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022748-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u9945p
title: Occurrence Download

description: A dataset containing 594 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 {
 "or" : [
 "Country is Angola",
 "Country is Bahrain",
 "Country is Benin",
 "Country is Cameroon",
 "Country is Comoros",
 "Country is Congo",
 "Country is Congo, Democratic Republic of the",
 "Country is Côte d’Ivoire",
 "Country is Djibouti",
 "Country is Egypt",
 "Country is Eritrea",
 "Country is Equatorial Guinea",
 "Country is Gabon",
 "Country is Gambia",
 "Country is Ghana",
 "Country is Guinea",
 "Country is Guinea-Bissau",
 "Country is Iran (Islamic Republic of)",
 "Country is Iraq",
 "Country is Jordan",
 "Country is Kenya",
 "Country is Kuwait",
 "Country is Liberia",
 "Country is Madagascar",
 "Country is Mauritania",
 "Country is Mauritius",
 "Country is Mozambique",
 "Country is Nigeria",
 "Country is Oman",
 "Country is Qatar",
 "Country is Réunion",
 "Country is Saudi Arabia",
 "Country is Senegal",
 "Country is Seychelles",
 "Country is Sierra Leone",
 "Country is Somalia",
 "Country is South Africa",
 "Country is South Sudan",
 "Country is Sudan",
 "Country is Tanzania, United Republic of",
 "Country is Togo",
 "Country is United Arab Emirates",
 "Country is Yemen",
 "Country is Zambia",
 "Country is Zimbabwe"
 ]
 },
 "TaxonKey is Bruguiera gymnorhiza (L.) Lam.",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil)"
 ]
 }
 },
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (VAGRANT)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 {
 "not" : {
 "DegreeOfEstablishment" : [
 "is one of (CULTIVATED)"
 ]
 }
 },
 "DegreeOfEstablishment is null"
 ]
 }
 ]
}

 The dataset includes 594 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022749-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.