cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14204005
title: Dataset from Rummel et al.: "Spatially resolved salt intrusion mechanisms in a tidal estuary and the impact of channel deepening" - Part 1

description: Model data from the numerical setup of the Weser River Estuary used in Rummel et al. (submitted to JGR:Oceans): "Spatially resolved salt intrusion mechanisms in a tidal estuary and the impact of channel deepening" - Part 1.

The dates in the file names are connected to specific model runs and do not explain the modelled time period.

Explanation of datasets:



2D_elev*  -  2D model output for the entire year 2016  for validation at one location each (associated station name included in file name), original topography.

3D_stat*  -  3D model output for the entire year 2016 for validation at one location each (associated station name included in file name), original topography.

3D_cross_30_80*  -  3D model output for one month of 2016 for the model domain from Weser km 30 to 80 including variables needed for the salt transport decomposition.



2024-05-23  -  March 2016, original topography

2024-06-07  -  March 2016, dredged topography

2024-06-06  -  September 2016, original topography (different temporal resolution)

2024-06-10  -  September 2016, dredged topography



3D_channel*  -  3D model output for the navigational channel in the entire model domain for the entire year 2016.



2024-04-02  -  original topography

2024-05-16  -  dredged topography



3D_cross_55/65_2024-09-02*  -  3D model output for September 2016, original topography for crosssections at Weser km 55 and 65 including variables needed for the salt transport decomposition.
DOI: 10.5281/zenodo.14007800
title: Data and scripts supporting "Metabolic profiling of patient-derived organoids reveals nucleotide synthesis as a metabolic vulnerability in malignant rhabdoid tumors"

description: This submission contains the processed sequencing data and analysis scripts, as well as the raw and processed LC-MS metabolomics files accompanying our manuscript "Metabolic profiling of patient-derived organoids reveals nucleotide synthesis as a metabolic vulnerability in malignant rhabdoid tumors" (Cell Reports Medicine, 2024)
DOI: 10.5281/zenodo.14007801
title: Processed data and scripts supporting "Metabolic profiling of patient-derived organoids reveals nucleotide synthesis as a metabolic vulnerability in malignant rhabdoid tumors"

description: This submission contains the processed data and scripts accompanying our manuscript "Metabolic profiling of patient-derived organoids reveals nucleotide synthesis as a metabolic vulnerability in malignant rhabdoid tumors" (Cell Reports Medicine, 2024)
DOI: 10.5281/zenodo.14206325
title: Data and scripts supporting "Metabolic profiling of patient-derived organoids reveals nucleotide synthesis as a metabolic vulnerability in malignant rhabdoid tumors"

description: This submission contains the processed sequencing data and analysis scripts, as well as the raw and processed LC-MS metabolomics files accompanying our manuscript "Metabolic profiling of patient-derived organoids reveals nucleotide synthesis as a metabolic vulnerability in malignant rhabdoid tumors" (Cell Reports Medicine, 2024)
DOI: 10.15468/dl.ugc79s
title: Occurrence Download

description: A dataset containing 2791 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Geometry POLYGON((-70.29866 -18.41611,-69.70677 -18.41611,-69.70677 -17.95923,-70.29866 -17.95923,-70.29866 -18.41611))",
 "Geometry POLYGON((-70.18731 -17.28938,-69.64923 -17.28938,-69.64923 -16.58657,-70.18731 -16.58657,-70.18731 -17.28938))",
 "Geometry POLYGON((-70.65182 -15.79105,-70.52622 -15.79105,-70.52622 -14.84216,-70.65182 -14.84216,-70.65182 -15.79105))",
 "Geometry POLYGON((-73.18169 -13.74393,-71.44071 -13.74393,-71.44071 -13.68352,-73.18169 -13.68352,-73.18169 -13.74393))",
 "Geometry POLYGON((-76.25877 -18.41611,-70.29866 -18.41611,-70.29866 -13.92913,-76.25877 -13.92913,-76.25877 -18.41611))",
 "Geometry POLYGON((-70.29866 -18.41611,-69.70677 -17.95923,-69.64923 -17.28938,-70.18731 -16.58657,-70.52622 -15.79105,-70.65182 -14.84216,-71.44071 -13.68352,-73.18169 -13.74393,-76.25877 -13.92913,-70.29866 -18.41611))"
 ]
 },
 "HasGeospatialIssue is false",
 "TaxonKey is Nycticorax nycticorax (Linnaeus, 1758)"
 ]
}

 The dataset includes 2791 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029125-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ds2jey
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Amanita advena Tulloss, Ovrebo & Halling"
 ]
}

 The dataset includes 13 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029126-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fnnyqg
title: Occurrence Download

description: A dataset containing 58 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-76.17203 -13.08655,-76.41036 -14.59727,-75.24808 -15.37504,-74.63004 -15.41485,-74.10983 -14.73639,-75.02219 -13.84014,-76.17203 -13.08655))",
 "HasGeospatialIssue is false",
 "TaxonKey is Asthenes dorbignyi (Reichenbach, 1853)"
 ]
}

 The dataset includes 58 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029127-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10709612
title: Computational supremacy in quantum simulation

description: This data repository is intended to support the findings of the research article 'Computational supremacy in quantum simulation'.
DOI: 10.5281/zenodo.11033416
title: Computational supremacy in quantum simulation

description: This data repository is intended to support the findings of the research article 'Computational supremacy in quantum simulation'.

v2 has a corrected annealing schedule ADV1.csv and explanation in the README.

v2 removed correlations for 5x5x8 diamond at 7ns, which are not converged for MPS and were erroneously included in v1.
DOI: 10.15468/dl.5bmyrq
title: Occurrence Download

description: A dataset containing 224 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Atoposmia abjecta (Cresson, 1878)"
 ]
}

 The dataset includes 224 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029128-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26208/vc11-wg13
title: A simple theory for molecular chemotaxis driven by specific binding interactions

description: Recent experiments have suggested that enzymes and other small molecules chemotax toward their substrates. However, the physical forces driving this chemotaxis are currently debated. In this work, we consider a simple thermodynamic theory for molecular chemotaxis that is based on the McMillan–Mayer theory of dilute solutions and Schellman’s theory for macromolecular binding. Even in the absence of direct interactions, the chemical binding equilibrium introduces a coupling term into the relevant free energy, which then reduces the chemical potential of both enzymes and their substrates. Assuming a local thermodynamic equilibrium, this binding contribution to the chemical potential generates an effective thermodynamic force that promotes chemotaxis by driving each solute toward its binding partner. Our numerical simulations demonstrate that, although small, this thermodynamic force is qualitatively consistent with several experimental studies. Thus, our study may provide additional insight into the role of the thermodynamic binding free energy for molecular chemotaxis.
DOI: 10.15468/dl.dy97p7
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-70.64721 -15.35452,-70.45725 -16.22932,-69.73809 -17.22989,-69.13615 -16.61741,-69.10065 -16.12687,-69.68382 -15.18435,-70.64721 -15.35452))",
 "HasGeospatialIssue is false",
 "TaxonKey is Ardea cocoi Linnaeus, 1766"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029129-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qb3xrf
title: Occurrence Download

description: A dataset containing 126 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Argentina, Chile)",
 "TaxonKey is Microclysia Butler, 1882"
 ]
}

 The dataset includes 126 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029131-241107131044228/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.27122532
title: FAStEN: An Efficient Adaptive Method for Feature Selection and Estimation in High-Dimensional Functional Regressions

description: Functional regression analysis is an established tool for many contemporary scientific applications. Regression problems involving large and complex datasets are ubiquitous, and feature selection is crucial for avoiding overfitting and achieving accurate predictions. We propose a new, flexible and ultra-efficient approach to perform feature selection in a sparse high dimensional function-on-function regression problem, and we show how to extend it to the scalar-on-function framework. Our method, called FAStEN, combines functional data, optimization, and machine learning techniques to perform feature selection and parameter estimation simultaneously. We exploit the properties of Functional Principal Components and the sparsity inherent to the Dual Augmented Lagrangian problem to significantly reduce computational cost, and we introduce an adaptive scheme to improve selection accuracy. In addition, we derive asymptotic oracle properties, which guarantee estimation and selection consistency for the proposed FAStEN estimator. Through an extensive simulation study, we benchmark our approach to the best existing competitors and demonstrate a massive gain in terms of CPU time and selection performance, without sacrificing the quality of the coefficients’ estimation. The theoretical derivations and the simulation study provide a strong motivation for our approach. Finally, we present an application to brain fMRI data from the AOMIC PIOP1 study. Complete FAStEN code is provided at https://github.com/IBM/funGCN. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.27122532.v1
title: FAStEN: An Efficient Adaptive Method for Feature Selection and Estimation in High-Dimensional Functional Regressions

description: Functional regression analysis is an established tool for many contemporary scientific applications. Regression problems involving large and complex datasets are ubiquitous, and feature selection is crucial for avoiding overfitting and achieving accurate predictions. We propose a new, flexible and ultra-efficient approach to perform feature selection in a sparse high dimensional function-on-function regression problem, and we show how to extend it to the scalar-on-function framework. Our method, called FAStEN, combines functional data, optimization, and machine learning techniques to perform feature selection and parameter estimation simultaneously. We exploit the properties of Functional Principal Components and the sparsity inherent to the Dual Augmented Lagrangian problem to significantly reduce computational cost, and we introduce an adaptive scheme to improve selection accuracy. In addition, we derive asymptotic oracle properties, which guarantee estimation and selection consistency for the proposed FAStEN estimator. Through an extensive simulation study, we benchmark our approach to the best existing competitors and demonstrate a massive gain in terms of CPU time and selection performance, without sacrificing the quality of the coefficients’ estimation. The theoretical derivations and the simulation study provide a strong motivation for our approach. Finally, we present an application to brain fMRI data from the AOMIC PIOP1 study. Complete FAStEN code is provided at https://github.com/IBM/funGCN. Supplementary materials for this article are available online.
DOI: 10.6073/pasta/0f7d9febcfc6db180b9a5ec9514d6865
title: Water properties of Arco Lake, Budd Lake, Deming Lake, and Josephine Lake in Itasca State Park from 2006-2009 and 2019-2023.

description: Depth profiles of water column chemical and physical properties were assessed with seasonal-scale frequency from four lakes in the Itasca State Park from 2006-2009 and from 2019-2023. The data was used to assess the mixing status and major geochemical constituents within the lakes. Several parameters were routinely measured with deployable probes at meter or sub-meter resolution at the deepest location in each lake. Water samples were also collected for laboratory analysis. Bathymetry data collected in 2022 is supplied as rasters.
DOI: 10.6084/m9.figshare.27233999
title: Sample Efficient Nonparametric Regression via Low-Rank Regularization

description: Nonparametric regression suffers from curse of dimensionality, requiring a relatively large sample size for accurate estimation beyond the univariate case. In this article, we consider a simple method of dimension reduction in nonparametric regression via series estimation, based on the concept of low-rankness which was previously studied in parametric multivariate reduced-rank regression and matrix regression. For d&gt;2, the low-rank assumption is realized via tensor regression. We establish a faster convergence rate of the estimator in the (approximate) low-rank case. Limitations of the model are also discussed. Through simulation studies and real data analysis, we compare the estimation accuracy of the proposed method with that of existing approaches. The results demonstrate that the proposed method yields estimates with lower RMSE compared to existing methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.27233999.v1
title: Sample efficient nonparametric regression via low-rank regularization

description: Nonparametric regression suffers from curse of dimensionality, requiring a relatively large sample size for accurate estimation beyond the univariate case. In this paper, we consider a simple method of dimension reduction in nonparametric regression via series estimation, based on the concept of low-rankness which was previously studied in parametric multivariate reduced-rank regression and matrix regression. For d&gt;2, the low-rank assumption is realized via tensor regression. We establish a faster convergence rate of the estimator in the (approximate) low-rank case. Limitations of the model are also discussed. Through simulation studies and real data analysis, we compare the estimation accuracy of the proposed method with that of existing approaches. The results demonstrate that the proposed method yields estimates with lower RMSE compared to existing methods.
DOI: 10.6084/m9.figshare.27233999.v2
title: Sample Efficient Nonparametric Regression via Low-Rank Regularization

description: Nonparametric regression suffers from curse of dimensionality, requiring a relatively large sample size for accurate estimation beyond the univariate case. In this article, we consider a simple method of dimension reduction in nonparametric regression via series estimation, based on the concept of low-rankness which was previously studied in parametric multivariate reduced-rank regression and matrix regression. For d&gt;2, the low-rank assumption is realized via tensor regression. We establish a faster convergence rate of the estimator in the (approximate) low-rank case. Limitations of the model are also discussed. Through simulation studies and real data analysis, we compare the estimation accuracy of the proposed method with that of existing approaches. The results demonstrate that the proposed method yields estimates with lower RMSE compared to existing methods. Supplementary materials for this article are available online.
DOI: 10.15468/dl.sqkf3x
title: Occurrence Download

description: A dataset listing the 345 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Masaridae"
 ]
}

 The dataset's 345 records were derived from 106 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029112-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dxpztz
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-70.90298 -15.01979,-70.90298 -16.10348,-69.97539 -16.86047,-69.107 -16.70931,-69.26489 -16.00865,-69.52146 -14.86724,-70.90298 -15.01979))",
 "HasGeospatialIssue is false",
 "TaxonKey is Volatinia jacarina (Linnaeus, 1766)"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029132-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14207232
title: SemTab 2024: Semantic Web Challenge on Tabular Data to Knowledge Graph Matching Data Sets - WikidataTables2024R1 and WikidataTables2024R2

description: Data Sets from the ISWC 2024 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, Round 1, Wikidata Tables. Links to other datasets can be found on the challenge website: https://sem-tab-challenge.github.io/2024/ as well as the proceedings of the challenge published on CEUR.

For details about the challenge, see: http://www.cs.ox.ac.uk/isg/challenges/sem-tab/

For 2024 edition, see: https://sem-tab-challenge.github.io/2024/

Note on License: This data includes data from the following sources. Refer to each source for license details:- Wikidata https://www.wikidata.org/

THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
DOI: 10.5281/zenodo.4282879
title: SemTab 2020: Semantic Web Challenge on Tabular Data to Knowledge Graph Matching Data Sets

description: Data Sets from the ISWC 2020 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching. For details, see: http://www.cs.ox.ac.uk/isg/challenges/sem-tab/ Note on License: This data includes data from the following sources. Refer to each source for license details:<br> - Wikidata https://www.wikidata.org/ THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
DOI: 10.5281/zenodo.6154708
title: SemTab 2021: Semantic Web Challenge on Tabular Data to Knowledge Graph Matching Data Sets

description: Data Sets from the ISWC 2021 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching. For details, see: http://www.cs.ox.ac.uk/isg/challenges/sem-tab/ Note on License: This data includes data from the following sources. Refer to each source for license details:<br> - Wikidata https://www.wikidata.org/ THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
DOI: 10.5281/zenodo.3518530
title: SemTab 2024: Semantic Web Challenge on Tabular Data to Knowledge Graph Matching Data Sets - WikidataTables2024R1 and WikidataTables2024R2

description: Data Sets from the ISWC 2024 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, Round 1, Wikidata Tables. Links to other datasets can be found on the challenge website: https://sem-tab-challenge.github.io/2024/ as well as the proceedings of the challenge published on CEUR.

For details about the challenge, see: http://www.cs.ox.ac.uk/isg/challenges/sem-tab/

For 2024 edition, see: https://sem-tab-challenge.github.io/2024/

Note on License: This data includes data from the following sources. Refer to each source for license details:- Wikidata https://www.wikidata.org/

THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
DOI: 10.5281/zenodo.7416036
title: SemTab 2022: Semantic Web Challenge on Tabular Data to Knowledge Graph Matching Data Sets - "Hard Tables" R1 and R2

description: Data Sets from the ISWC 2022 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching. For details about the challenge, see: http://www.cs.ox.ac.uk/isg/challenges/sem-tab/ For 2022 edition, see: https://sem-tab-challenge.github.io/2022/ Note on License: This data includes data from the following sources. Refer to each source for license details:<br> - Wikidata https://www.wikidata.org/ THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
DOI: 10.5281/zenodo.8393535
title: SemTab 2023: Semantic Web Challenge on Tabular Data to Knowledge Graph Matching Data Sets - "Wikidata Tables" R1

description: Data Sets from the ISWC 2023 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, Round 1, Wikidata Tables. Links to other datasets can be found on the challenge website: https://sem-tab-challenge.github.io/2023/ as well as the proceedings of the challenge published on CEUR. For details about the challenge, see: http://www.cs.ox.ac.uk/isg/challenges/sem-tab/ For 2023 edition, see: https://sem-tab-challenge.github.io/2023/ Note on License: This data includes data from the following sources. Refer to each source for license details:<br> - Wikidata https://www.wikidata.org/ THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.