cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27195717.v1
title: Hi-C of WT S. pneumoniae D39Vpart of Mazzuoli, van Raaphorst et. al, 2024

description:  Nucleoid-associated proteins (NAPs) are crucial for maintaining chromosomal compaction and architecture and are actively involved in DNA replication, recombination, repair, and gene regulation. In the opportunistic pathogen <em>Streptococcus pneumoniae,</em> HU is the only identified NAP, and its role in chromosome conformation and other essential processes has not yet been investigated. Here, we use a multi-scale approach to explore the role of HU in chromosome conformation and segregation dynamics. By combining superresolution microscopy and whole-genome binding analysis, we describe the nucleoid as a dynamic structure where HU binds transiently across the entire nucleoid, with a preference for the origin of replication over the terminus. Reducing cellular HU levels impacts nucleoid maintenance and disrupts robust nucleoid scaling with cell size. This effect is similar to the distortion caused by fluoroquinolone-antibiotics, supporting earlier observations that HU is essential for maintaining DNA supercoiling. Furthermore, in cells lacking HU, the replication machinery is misplaced, and cells are unable to initiate and proceed with on-going replication. Chromosome conformation capture (Hi-C) experiments revealed that HU is required to maintain cohesion between the two chromosomal arms, in a similar way to the structural maintenance of the chromosome complex SMC. Together, we show that by promoting long-range chromosome interactions and supporting the architecture of the domain encompassing the origin, HU is fundamental for chromosome integrity and the intimately related processes of chromosome replication and segregation.
DOI: 10.6084/m9.figshare.27201531.v1
title: Hi-C of S. pneumoniae - Ptc-HUpart of Mazzuoli, van Raaphorst et. al, 2024

description:  Nucleoid-associated proteins (NAPs) are crucial for maintaining chromosomal compaction and architecture and are actively involved in DNA replication, recombination, repair, and gene regulation. In the opportunistic pathogen <em>Streptococcus pneumoniae,</em> HU is the only identified NAP, and its role in chromosome conformation and other essential processes has not yet been investigated. Here, we use a multi-scale approach to explore the role of HU in chromosome conformation and segregation dynamics. By combining superresolution microscopy and whole-genome binding analysis, we describe the nucleoid as a dynamic structure where HU binds transiently across the entire nucleoid, with a preference for the origin of replication over the terminus. Reducing cellular HU levels impacts nucleoid maintenance and disrupts robust nucleoid scaling with cell size. This effect is similar to the distortion caused by fluoroquinolone-antibiotics, supporting earlier observations that HU is essential for maintaining DNA supercoiling. Furthermore, in cells lacking HU, the replication machinery is misplaced, and cells are unable to initiate and proceed with on-going replication. Chromosome conformation capture (Hi-C) experiments revealed that HU is required to maintain cohesion between the two chromosomal arms, in a similar way to the structural maintenance of the chromosome complex SMC. Together, we show that by promoting long-range chromosome interactions and supporting the architecture of the domain encompassing the origin, HU is fundamental for chromosome integrity and the intimately related processes of chromosome replication and segregation.
DOI: 10.15468/dl.dnmyq8
title: Occurrence Download

description: A dataset containing 35482 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ipomoea batatas (L.) Lam."
 ]
}

 The dataset includes 35482 records from 473 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005843-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6z2zvr
title: Occurrence Download

description: A dataset containing 375 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Plantae",
 "HasGeospatialIssue is FALSE",
 "Geometry POLYGON ((-3.846105 36.7648, -3.846016 36.76462, -3.845915 36.76445, -3.845803 36.76428, -3.845681 36.76412, -3.845548 36.76396, -3.845405 36.76381, -3.845251 36.76367, -3.845089 36.76353, -3.844918 36.7634, -3.844738 36.76327, -3.844551 36.76316, -3.844356 36.76305, -3.844154 36.76295, -3.843946 36.76286, -3.843732 36.76278, -3.843513 36.7627, -3.84329 36.76264, -3.843062 36.76259, -3.842832 36.76254, -3.842599 36.76251, -3.842363 36.76248, -3.842127 36.76247, -3.84189 36.76246, -3.841653 36.76247, -3.841417 36.76248, -3.841182 36.76251, -3.840949 36.76254, -3.840718 36.76259, -3.840491 36.76264, -3.840268 36.76271, -3.84005 36.76278, -3.839836 36.76287, -3.839628 36.76296, -3.839427 36.76306, -3.839233 36.76317, -3.839046 36.76328, -3.838866 36.76341, -3.838696 36.76354, -3.838534 36.76368, -3.838382 36.76382, -3.838239 36.76397, -3.838107 36.76413, -3.837985 36.7643, -3.837874 36.76446, -3.836629 36.76648, -3.83653 36.76665, -3.836442 36.76682, -3.836366 36.767, -3.836301 36.76718, -3.836249 36.76737, -3.836209 36.76755, -3.836181 36.76774, -3.836165 36.76793, -3.836162 36.76812, -3.836171 36.76831, -3.836193 36.7685, -3.836227 36.76869, -3.836273 36.76887, -3.836332 36.76905, -3.836402 36.76923, -3.836484 36.76941, -3.836578 36.76959, -3.836683 36.76976, -3.836799 36.76992, -3.836925 36.77008, -3.837062 36.77023, -3.837209 36.77038, -3.837365 36.77052, -3.838084 36.77114, -3.838093 36.77115, -3.839626 36.77246, -3.839793 36.7726, -3.839967 36.77273, -3.84015 36.77285, -3.840341 36.77296, -3.840539 36.77306, -3.840743 36.77316, -3.840953 36.77324, -3.841169 36.77332, -3.841389 36.77339, -3.841614 36.77345, -3.841842 36.7735, -3.842073 36.77354, -3.842307 36.77357, -3.842542 36.77359, -3.842778 36.7736, -3.843014 36.7736, -3.843251 36.77359, -3.843486 36.77357, -3.843719 36.77354, -3.843951 36.7735, -3.844179 36.77345, -3.844404 36.77339, -3.844625 36.77333, -3.844841 36.77325, -3.845052 36.77316, -3.845256 36.77307, -3.845454 36.77297, -3.845646 36.77285, -3.845829 36.77273, -3.846005 36.77261, -3.846171 36.77247, -3.846329 36.77233, -3.846477 36.77218, -3.846616 36.77203, -3.846744 36.77187, -3.846861 36.77171, -3.846968 36.77154, -3.847063 36.77136, -3.847146 36.77119, -3.847218 36.77101, -3.847278 36.77082, -3.847326 36.77064, -3.847362 36.77045, -3.847385 36.77026, -3.84752 36.76876, -3.847531 36.76857, -3.847529 36.76838, -3.847514 36.76818, -3.847486 36.76799, -3.847445 36.7678, -3.847391 36.76761, -3.847325 36.76742, -3.847246 36.76724, -3.846105 36.7648))"
 ]
}

 The dataset includes 375 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052815-250827131500795/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.30016453
title: Data for Testing Footprints.xlsx

description: This dataset accompanies the article Testing for the footprints of stabilization economic policy in forecast errors and provides the minimal data necessary to replicate the empirical findings. It consists of six sheets:<br>DailyBonds – Contains first differences of daily 10-year government bond yields for 33 countries, covering the period January 1, 2012 – December 31, 2019. The raw yield data were obtained from the Thomson Reuters Datastream (Refinitiv Eikon) database and transformed into first differences to ensure stationarity. These data are the input for the ARMA–GARCH models and the Policy Effects Lagrange Multiplier (PELM) test applied in the paper.<br>Revenue – Contains government revenue data (as % of GDP) for the same set of countries, sourced from the IMF World Economic Outlook (WEO) database. A direct link to the corresponding IMF WEO page is included at the top of the sheet.<br>Expenditures – Contains government expenditure data (as % of GDP) from the IMF WEO database, with the link included at the top of the sheet.<br>Rev-Exp – Computed differences between government revenue and expenditures (Revenue – Expenditure), constructed to provide an additional fiscal indicator in line with referee suggestions. This series was used in supplementary tests, including analysis of the Brexit crisis.<br>N_lending_borrowing – Contains data on Net lending/borrowing (general government, % of GDP), sourced from the IMF WEO database.<br>P_net_lending_borrowing – Contains data on Primary net lending/borrowing (general government, % of GDP), also from the IMF WEO database.<br>Methodology and TechniquesThe DailyBonds sheet is used to estimate ARMA–GARCH models for sovereign bond yields and to compute the PELM test statistics, which detect stabilization policy footprints in financial markets. Fiscal indicators from the IMF WEO (revenue, expenditure, net lending/borrowing, primary net lending/borrowing) are then employed to evaluate fiscal dynamics across countries, including additional robustness checks prompted by reviewers (such as the Brexit crisis case study). The computed series Rev-Exp (revenue minus expenditure) provides an alternative fiscal stance measure that complements the WEO variables.<br>Legal and Ethical ConsiderationsThe dataset includes only derived or publicly shareable information. The bond yield data were originally obtained from Refinitiv Eikon (Thomson Reuters Datastream), which is subject to subscription licensing; we provide only the first differences required for replication, not the raw series. The IMF WEO data are publicly available and redistributed here in line with the IMF’s open data policy. No individual-level or sensitive data are included.
DOI: 10.6084/m9.figshare.30016453.v1
title: Data for Testing Footprints.xlsx

description: This dataset accompanies the article Testing for the footprints of stabilization economic policy in forecast errors and provides the minimal data necessary to replicate the empirical findings. It consists of six sheets:<br>DailyBonds – Contains first differences of daily 10-year government bond yields for 33 countries, covering the period January 1, 2012 – December 31, 2019. The raw yield data were obtained from the Thomson Reuters Datastream (Refinitiv Eikon) database and transformed into first differences to ensure stationarity. These data are the input for the ARMA–GARCH models and the Policy Effects Lagrange Multiplier (PELM) test applied in the paper.<br>Revenue – Contains government revenue data (as % of GDP) for the same set of countries, sourced from the IMF World Economic Outlook (WEO) database. A direct link to the corresponding IMF WEO page is included at the top of the sheet.<br>Expenditures – Contains government expenditure data (as % of GDP) from the IMF WEO database, with the link included at the top of the sheet.<br>Rev-Exp – Computed differences between government revenue and expenditures (Revenue – Expenditure), constructed to provide an additional fiscal indicator in line with referee suggestions. This series was used in supplementary tests, including analysis of the Brexit crisis.<br>N_lending_borrowing – Contains data on Net lending/borrowing (general government, % of GDP), sourced from the IMF WEO database.<br>P_net_lending_borrowing – Contains data on Primary net lending/borrowing (general government, % of GDP), also from the IMF WEO database.<br>Methodology and TechniquesThe DailyBonds sheet is used to estimate ARMA–GARCH models for sovereign bond yields and to compute the PELM test statistics, which detect stabilization policy footprints in financial markets. Fiscal indicators from the IMF WEO (revenue, expenditure, net lending/borrowing, primary net lending/borrowing) are then employed to evaluate fiscal dynamics across countries, including additional robustness checks prompted by reviewers (such as the Brexit crisis case study). The computed series Rev-Exp (revenue minus expenditure) provides an alternative fiscal stance measure that complements the WEO variables.<br>Legal and Ethical ConsiderationsThe dataset includes only derived or publicly shareable information. The bond yield data were originally obtained from Refinitiv Eikon (Thomson Reuters Datastream), which is subject to subscription licensing; we provide only the first differences required for replication, not the raw series. The IMF WEO data are publicly available and redistributed here in line with the IMF’s open data policy. No individual-level or sensitive data are included.
DOI: 10.5281/zenodo.17097877
title: A Comprehensive Study on Air Compressors: Definition, Types, Working Principles, Applications, and Market Dynamics
DOI: 10.5281/zenodo.17097878
title: A Comprehensive Study on Air Compressors: Definition, Types, Working Principles, Applications, and Market Dynamics
DOI: 10.15468/dl.xmj4rc
title: Occurrence Download

description: A dataset listing the 188 species recorded in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "StateProvince is one of (Antioquia)",
 "TaxonKey is one of (Mammalia [GBIF Backbone Taxonomy])",
 "BasisOfRecord is one of (Machine Observation, Specimen, Material sample)",
 "Elevation 200-900m"
 ]
 }
 ]
}

 The dataset's 188 records were derived from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052802-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9xt2q3
title: Occurrence Download

description: A dataset containing 326676 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Alnus incana"
 ]
}

 The dataset includes 326676 records from 1004 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052748-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xnnxdt
title: Occurrence Download

description: A dataset containing 274 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Dendrocopos major",
 "BasisOfRecord is Specimen",
 "Country is one of (Japan, Korea, Republic of, Korea (Democratic People’s Republic of))"
 ]
}

 The dataset includes 274 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052816-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yp8yxs
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Dendrocopos major",
 "BasisOfRecord is Specimen",
 "Country is one of (China)",
 "DecimalLongitude is greater than 126",
 "DecimalLatitude is less than 49.5"
 ]
}

 The dataset includes 10 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052817-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e63ke8
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Dendrocopos major",
 "BasisOfRecord is Specimen",
 "Country is one of (Russian Federation)",
 "DecimalLongitude is greater than 136",
 "DecimalLatitude is less than 54"
 ]
}

 The dataset includes 14 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052818-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15015599
title: Ochnaceae subtribe Ochninae – circumscribing and naming monophyletic genera

description: This dataset contains the files associated with the manuscript "Ochnaceae subtribe Ochninae – circumscribing and naming monophyletic genera"

 

 

The files listed are the following:

 

1_Appendix 1 : Voucher specimens and gene sequence accessions used in this study. Newly reported sequences in bold

 

2_Appendix S1_Voucher_Stats.xlsx : Table, including taxa, vouchers, sequencing methodology and recovery statistics for samples with Angiosperms353 (only) and Angiosperms353+Ochnaceae specific probe kit (using the HybPiper script; see https://github.com/mossmatters/HybPiper/wiki#hybpiper-stats

 

3_A353_CleanedAligments.tar.gz : includes the aligned loci using Mafft for samples with Angiosperms353 (post Treeshrink)

4_A353_IQTrees.tar.gz : includes the genes trees generated using IQTree for the each loci matrices for samples with Angiosperms353

5_A353_Astral.tar.gz : is the coalescent astral tree generated with Astral and using PAFTOL_006813_L001 (Luxemburgia octandra) as outgroup.

6_Combined_CleanedAligments.tar.gz : includes the aligned loci using Mafft for Angiosperms353+Ochnaceae (post Treeshrink)

7_Combined_IQTrees.tar.gz : includes the genes trees generated using IQTree for the each loci matrices for samples with Angiosperms353+Ochnaceae



8_Combined_Astral.tar.gz : is the coalescent astral tree generated with Astral and using PAFTOL_006813_L001 (Luxemburgia octandra) as outgroup.
DOI: 10.5281/zenodo.15015600
title: Ochnaceae subtribe Ochninae – circumscribing and naming monophyletic genera

description: This dataset contains the files associated with the manuscript "Ochnaceae subtribe Ochninae – circumscribing and naming monophyletic genera"

 

 

The files listed are the following:

 

1_Appendix 1 : Voucher specimens and gene sequence accessions used in this study. Newly reported sequences in bold

 

2_Appendix S1_Voucher_Stats.xlsx : Table, including taxa, vouchers, sequencing methodology and recovery statistics for samples with Angiosperms353 (only) and Angiosperms353+Ochnaceae specific probe kit (using the HybPiper script; see https://github.com/mossmatters/HybPiper/wiki#hybpiper-stats

 

3_A353_CleanedAligments.tar.gz : includes the aligned loci using Mafft for samples with Angiosperms353 (post Treeshrink)

4_A353_IQTrees.tar.gz : includes the genes trees generated using IQTree for the each loci matrices for samples with Angiosperms353

5_A353_Astral.tar.gz : is the coalescent astral tree generated with Astral and using PAFTOL_006813_L001 (Luxemburgia octandra) as outgroup.

6_Combined_CleanedAligments.tar.gz : includes the aligned loci using Mafft for Angiosperms353+Ochnaceae (post Treeshrink)

7_Combined_IQTrees.tar.gz : includes the genes trees generated using IQTree for the each loci matrices for samples with Angiosperms353+Ochnaceae



8_Combined_Astral.tar.gz : is the coalescent astral tree generated with Astral and using PAFTOL_006813_L001 (Luxemburgia octandra) as outgroup.
DOI: 10.15468/dl.djy7wd
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lycium ferocissimum",
 "Country is Malta",
 "HasCoordinate is true"
 ]
}

 The dataset includes 9 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052821-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f2v4mc
title: Occurrence Download

description: A dataset containing 1400 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lycium ferocissimum",
 "Country is New Zealand",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1400 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052822-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14198791
title: Using Monoscopic Multispectral Earth Observation Images to Predict Terrain Features With Deep Neural Networks - Supplementary material

description: This is supplementary material to the journal paper, "Using Monoscopic Multispectral Earth Observation Images to Predict Terrain Features With Deep Neural Networks", published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS) 25 August 2025.

---------------------------

Contents:

Source_code.zip: Contains the Python source code for training the R2U-Net for predicting terrain slopes in optical Sentinel-2 L2A images, with AW3D30 DSM as targets. The Python files for downloading and processing the raw images have already been used to produce the provided merged L2A and DSM images in Data.zip ready for using as input to the R2U-Net for training and evaluation.requirements.txt contains the Python environment libraries needed to train and run the source codes.The YAML files contain the folder paths to the merged images used as input to the network.

Data.zip: Contains the merged L2A and DSM images used to train and evaluate the deep learning models. It also contains supplementary material related to the Sentinel-2 image data used; like the Sentinel-2 image file names and ids for downloading from the Copernicus Browser, for both training and testing with the deep learning model, and image location maps.NB! Full unzipped Data-folder is 57.1GB.

Results.zip: Contains some of the results achieved with the trained R2U-Net models. For each of the two trained models, tensorflow event files from training and evaluation are provided to be visualized with tensorboard. A final folder contains images used in the article for this supplementary material, with corresponding names to match the article figures.Provided are also the best model checkpoints from training the multispectral and RGB models, used to evaluate the trained models and produce the images and results in Results.zip.