cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.1594/pangaea.355966
title: Water temperature real-time profiles from cruise NHTM92 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.355967
title: Water temperature real-time profiles from cruise NIDK92 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.355969
title: Water temperature real-time profiles from cruise PJJU92 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.355973
title: Water temperature real-time profiles from cruise VC945092 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.355974
title: Water temperature real-time profiles from cruise WTDO92 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.355977
title: Water temperature real-time profiles from cruise CGBS92 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.355981
title: Water temperature real-time profiles from cruise PJJU92 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.355983
title: Water temperature real-time profiles from cruise TFEA92 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.355988
title: Water temperature real-time profiles from cruise CG268392 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.355989
title: Water temperature real-time profiles from cruise CGBS92 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.355991
title: Water temperature real-time profiles from cruise DAKE92 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.355993
title: Water temperature real-time profiles from cruise FNIB92 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.355994
title: Water temperature real-time profiles from cruise GACA92 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.356333
title: Water temperature real-time profiles from cruise CG268394 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.356175
title: Water temperature XBT profiles from cruise DLEZ93 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.356337
title: Water temperature real-time profiles from cruise CGBS95 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.356338
title: Water temperature real-time profiles from cruise CGCB95 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.355874
title: Water temperature real-time profiles from cruise WTER92 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.355876
title: Water temperature real-time profiles from cruise WTEW90 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.355909
title: Water temperature XBT profiles from cruise FNGS92 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.355950
title: Water temperature XBT profiles from cruise WTER91 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.355959
title: Water temperature and salinity real-time profiles from cruise UFJN91 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.6084/m9.figshare.24770336
title: Additional file 1 of Comparative genomic analysis of clinical Enterococcus faecalis distinguishes strains isolated from the bladder

description: Additional file 1: Supplemental Table 1. Genome information for phylo- and pan-genomic analysis.
DOI: 10.6084/m9.figshare.24770336.v1
title: Additional file 1 of Comparative genomic analysis of clinical Enterococcus faecalis distinguishes strains isolated from the bladder

description: Additional file 1: Supplemental Table 1. Genome information for phylo- and pan-genomic analysis.
DOI: 10.6084/m9.figshare.24770648
title: Additional file 2 of Comparative genomic analysis of clinical Enterococcus faecalis distinguishes strains isolated from the bladder

description: Additional file 2: Supplemental Table 2. All significantly enriched genes detected by databases
DOI: 10.6084/m9.figshare.24770648.v1
title: Additional file 2 of Comparative genomic analysis of clinical Enterococcus faecalis distinguishes strains isolated from the bladder

description: Additional file 2: Supplemental Table 2. All significantly enriched genes detected by databases
DOI: 10.6084/m9.figshare.26650737.v1
title: Additional file 3 of Comparative genomic analysis of clinical Enterococcus faecalis distinguishes strains isolated from the bladder

description: Additional file 3: Supplemental Table 3. Genomes used in machine learning test dataset
DOI: 10.1594/pangaea.355917
title: Water temperature XBT profiles from cruise FNQM91 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.1594/pangaea.355961
title: Water temperature real-time profiles from cruise DAKE92 (SCQC)

description: For definition of quality flags see hdl:10013/epic.31518.d001
DOI: 10.6084/m9.figshare.26650737
title: Additional file 3 of Comparative genomic analysis of clinical Enterococcus faecalis distinguishes strains isolated from the bladder

description: Additional file 3: Supplemental Table 3. Genomes used in machine learning test dataset
DOI: 10.6084/m9.figshare.26650740
title: Additional file 4 of Comparative genomic analysis of clinical Enterococcus faecalis distinguishes strains isolated from the bladder

description: Additional file 4: Supplemental Table 4. Importance of gene hits for machine learning distinguishment of bladder vs non-bladder genomes.
DOI: 10.6084/m9.figshare.26650740.v1
title: Additional file 4 of Comparative genomic analysis of clinical Enterococcus faecalis distinguishes strains isolated from the bladder

description: Additional file 4: Supplemental Table 4. Importance of gene hits for machine learning distinguishment of bladder vs non-bladder genomes.
DOI: 10.15468/dl.wa42js
title: Occurrence Download

description: A dataset containing 374 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Philippines",
 "TaxonKey is Pterocarpus indicus Willd."
 ]
}

 The dataset includes 374 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048693-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a6lvhp
title: Occurrence Download

description: A dataset containing 1924448 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bucephala clangula (Linnaeus, 1758)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1924448 records from 247 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003355-180131172636756/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yhggnw
title: Occurrence Download

description: A dataset containing 856 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Canada",
 "GadmGid is CAN.2_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Alces alces (Linnaeus, 1758)"
 ]
}

 The dataset includes 856 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048697-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ufisie
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Phylloscartes ceciliae Teixeira, 1987"
 ]
}

 The dataset includes 12 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003356-150811131857512/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9exza7
title: Occurrence Download

description: A dataset containing 1282 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dendropsophus molitor (Schmidt, 1857)",
 "Year 2000-2021"
 ]
}

 The dataset includes 1282 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049858-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zguabk
title: Occurrence Download

description: A dataset containing 206 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Priodontes maximus (Kerr, 1792)"
 ]
}

 The dataset includes 206 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003356-150831095807960/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dm9sfk
title: Occurrence Download

description: A dataset containing 31431 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Araneae"
 ]
}

 The dataset includes 31431 records from 97 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035277-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.aonla1
title: Occurrence Download

description: A dataset containing 475 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tillandsia monadelpha (É.Morren) Baker"
 ]
}

 The dataset includes 475 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003356-150912100344238/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ehzgdg
title: Occurrence Download

description: A dataset containing 475 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lutjanus guttatus (Steindachner, 1869)"
 ]
}

 The dataset includes 475 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003356-150922153815467/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tud9jv
title: Occurrence Download

description: A dataset containing 63 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Order Didelphimorphia"
 ]
}

 The dataset includes 63 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046153-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sq3twu
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Bacteria in Antarctic glacial foreland soils"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041308-181108115102211/datasets/export for details.
DOI: 10.15468/dl.yswf8t
title: Occurrence Download

description: A dataset containing 10392116 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is China"
 ]
}

 The dataset includes 10392116 records from 5175 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035275-231002084531237/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.28238220
title: Heavy tail robust estimation and inference for average treatment effects

description: We study the probability tail properties of Inverse probability weighting (IPW) estimators of the average treatment effect (ATE) when there is limited overlap between the covariate distributions of the treatment and control groups. Under unconfoundedness of treatment assignment conditional on covariates, such limited overlap is manifested in the propensity score for certain units being very close (but not equal) to 0 or 1. This renders IPW estimators possibly heavy tailed, and with a slower than n rate of convergence. Historically estimators are either based on the assumption of strict overlap, i.e., the propensity score is bounded away from 0 and 1; or they truncate the propensity score; or trim observations based on a variety of techniques based on covariate or propensity score values. Trimming or truncation is ultimately based on the covariates, ignoring important information about the inverse probability weighted random variable <i>Z</i> that identifies ATE by <i>E</i>[<i>Z</i>] = ATE. We propose a tail-trimmed IPW estimator whose performance is robust to limited overlap. In terms of the propensity score, which is generally unknown, we plug-in its parametric estimator in the infeasible <i>Z</i>, and then negligibly trim the resulting feasible <i>Z</i> adaptively by its large values. Trimming leads to bias if <i>Z</i> has an asymmetric distribution and an infinite variance, hence we estimate and remove the bias using important improvements on existing theory and methods. Our estimator sidesteps dimensionality, bias and poor correspondence properties associated with trimming by the covariates or propensity score. Monte Carlo experiments demonstrate that trimming by the covariates or the propensity score requires the removal of a substantial portion of the sample to render a low bias and close to normal estimator, while our estimator has low bias and mean-squared error, and is close to normal, based on the removal of very few sample extremes.
DOI: 10.6084/m9.figshare.28090065
title: Inference in the presence of unknown rates

description: The convergence rate of an estimator can vary when applied to datasets from different populations. As the population is unknown in practice, so is the corresponding convergence rate. In this article, we introduce a method to conduct inference on estimators whose convergence rates are unknown. Specifically, we extend the subsampling approach of Bertail, Politis, and Romano (1999) to situations where the convergence rate may include logarithmic components. This extension proves to be particularly relevant in certain statistical inference problems. To illustrate the practical relevance and implementation of our results, we discuss two main examples: (i) non parametric regression with measurement error; and (ii) intercept estimation in binary choice models. In each case, our approach provides robust inference in settings where convergence rates are unknown; simulation results validate our findings.
DOI: 10.6084/m9.figshare.28090065.v1
title: Inference in the presence of unknown rates

description: The convergence rate of an estimator can vary when applied to datasets from different populations. As the population is unknown in practice, so is the corresponding convergence rate. In this article, we introduce a method to conduct inference on estimators whose convergence rates are unknown. Specifically, we extend the subsampling approach of Bertail, Politis, and Romano (1999) to situations where the convergence rate may include logarithmic components. This extension proves to be particularly relevant in certain statistical inference problems. To illustrate the practical relevance and implementation of our results, we discuss two main examples: (i) non parametric regression with measurement error; and (ii) intercept estimation in binary choice models. In each case, our approach provides robust inference in settings where convergence rates are unknown; simulation results validate our findings.
DOI: 10.6084/m9.figshare.28238220.v2
title: Heavy tail robust estimation and inference for average treatment effects

description: We study the probability tail properties of Inverse probability weighting (IPW) estimators of the average treatment effect (ATE) when there is limited overlap between the covariate distributions of the treatment and control groups. Under unconfoundedness of treatment assignment conditional on covariates, such limited overlap is manifested in the propensity score for certain units being very close (but not equal) to 0 or 1. This renders IPW estimators possibly heavy tailed, and with a slower than n rate of convergence. Historically estimators are either based on the assumption of strict overlap, i.e., the propensity score is bounded away from 0 and 1; or they truncate the propensity score; or trim observations based on a variety of techniques based on covariate or propensity score values. Trimming or truncation is ultimately based on the covariates, ignoring important information about the inverse probability weighted random variable <i>Z</i> that identifies ATE by <i>E</i>[<i>Z</i>] = ATE. We propose a tail-trimmed IPW estimator whose performance is robust to limited overlap. In terms of the propensity score, which is generally unknown, we plug-in its parametric estimator in the infeasible <i>Z</i>, and then negligibly trim the resulting feasible <i>Z</i> adaptively by its large values. Trimming leads to bias if <i>Z</i> has an asymmetric distribution and an infinite variance, hence we estimate and remove the bias using important improvements on existing theory and methods. Our estimator sidesteps dimensionality, bias and poor correspondence properties associated with trimming by the covariates or propensity score. Monte Carlo experiments demonstrate that trimming by the covariates or the propensity score requires the removal of a substantial portion of the sample to render a low bias and close to normal estimator, while our estimator has low bias and mean-squared error, and is close to normal, based on the removal of very few sample extremes.
DOI: 10.48597/4w5a-5r6x
title: Measurement of VOC (volatile organic compounds) at Zürich-Kaserne

description: Measurement of VOC (volatile organic compounds) at Zürich-Kaserne
DOI: 10.48597/e7w9-ekhy
title: VOC (volatile organic compounds) at Zürich-Kaserne

description: Measurement of VOC (volatile organic compounds) at Zürich-Kaserne
DOI: 10.48597/y5c6-uph7
title: Measurement of VOC (volatile organic compounds) at Zürich-Kaserne

description: Measurement of VOC (volatile organic compounds) at Zürich-Kaserne
DOI: 10.15468/dl.kvnsep
title: Occurrence Download

description: A dataset containing 28570 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Euonymus fortunei (Turcz.) Hand.-Mazz."
 ]
}

 The dataset includes 28570 records from 379 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016678-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccrqg30
title: CCDC 706772: Experimental Crystal Structure Determination

description: Related Article: E.A.Muravyova, S.V.Shishkina, V.I.Musatov, I.V.Knyazeva, O.V.Shishkin, S.M.Desenko, V.A.Chebanov|2009|Synthesis|2009|1375|doi:10.1055/s-0028-1088024
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccrqg41
title: CCDC 706773: Experimental Crystal Structure Determination

description: Related Article: E.A.Muravyova, S.V.Shishkina, V.I.Musatov, I.V.Knyazeva, O.V.Shishkin, S.M.Desenko, V.A.Chebanov|2009|Synthesis|2009|1375|doi:10.1055/s-0028-1088024
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.13140/rg.2.2.30459.03363
title: MAAJID MOHI UD DIN MALIK PHD (COURSE WORK)
DOI: 10.5517/ccs8sh9
title: CCDC 723431: Experimental Crystal Structure Determination

description: Related Article: J.Cote, S.K.Collins|2009|Synthesis|2009|1499|doi:10.1055/s-0028-1088165
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccs8sjb
title: CCDC 723432: Experimental Crystal Structure Determination

description: Related Article: J.Cote, S.K.Collins|2009|Synthesis|2009|1499|doi:10.1055/s-0028-1088165
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccs8skc
title: CCDC 723433: Experimental Crystal Structure Determination

description: Related Article: J.Cote, S.K.Collins|2009|Synthesis|2009|1499|doi:10.1055/s-0028-1088165
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.13140/rg.2.2.19973.27369
title: Shistosoma mansoni
DOI: 10.5517/ccrwwvb
title: CCDC 712003: Experimental Crystal Structure Determination

description: Related Article: S.R.Waldvogel, A.Faust, W.Barkmann, R.Frohlich, O.Wolff|2009|Synthesis|2009|1651|doi:10.1055/s-0029-1216642
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccrkvpt
title: CCDC 702357: Experimental Crystal Structure Determination

description: Related Article: Bo Qian, Ming-Jin Fan, Yong-Xin Xie, Lu-Yong Wu, Yun Shi, Yong-Min Liang|2009|Synthesis|2009|1689|doi:10.1055/s-0028-1088070
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.