cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.3m7sfb
title: Occurrence Download

description: A dataset containing 2164 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Plantae",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Country is Germany",
 "DecimalLongitude is greater than or equal to 6.81173193397799",
 "DecimalLongitude is less than 6.81479148716239",
 "DecimalLatitude is greater than or equal to 47.27011",
 "DecimalLatitude is less than or equal to 55.05864"
 ]
}

 The dataset includes 2164 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045784-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fuv9va
title: Occurrence Download

description: A dataset containing 26379 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-112.0188 28.64913, -111.8688 28.64913, -111.8688 28.79913, -112.0188 28.79913, -112.0188 28.64913))"
 ]
}

 The dataset includes 26379 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045785-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.br4nrq
title: Occurrence Download

description: A dataset containing 2146 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Plantae",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Country is Germany",
 "DecimalLongitude is greater than or equal to 6.81479148716239",
 "DecimalLongitude is less than 6.81785104034678",
 "DecimalLatitude is greater than or equal to 47.27011",
 "DecimalLatitude is less than or equal to 55.05864"
 ]
}

 The dataset includes 2146 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045786-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17652686
title: 715 broommates cover : A
DOI: 10.5281/zenodo.17652687
title: 715 broommates cover : A
DOI: 10.15468/dl.fw7c7q
title: Occurrence Download

description: A dataset containing 3485 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-111.8688 28.64913, -111.7188 28.64913, -111.7188 28.79913, -111.8688 28.79913, -111.8688 28.64913))"
 ]
}

 The dataset includes 3485 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045787-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mvvspy
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rumex tolimensis"
 ]
}

 The dataset's 1 records were derived from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045754-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/rg.2.2.25630.60482
title: Project: Cave and karst fauna conservation in Lower Kinabatangan, Sabah, Malaysian Borneo
DOI: 10.15468/dl.xt4u54
title: Occurrence Download

description: A dataset containing 246259 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lucanus cervus",
 "HasCoordinate is true"
 ]
}

 The dataset includes 246259 records from 1733 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045695-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/11903655
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.5281/zenodo.15545421
title: Comparative Analysis of Anthraquinone and Chalcone Derivatives-Based Virtual Combinatorial Library. A Cheminformatics "Proof-of-Concept" Study

description: This computational “proof-of-concept” study illustrated the combinatorial approach used to explain how the selected natural products' structures undergo molecular diversity analysis. A virtual combinatorial library (1.6M) based on 20 anthraquinones and 24 chalcones were enumerated. The resulting compounds were optimized to the near drug-likeness properties and the physicochemical descriptors were calculated for all datasets including FDA, Non-FDA, and natural products (NPs) datasets from ZINC 15. UMAP and principal component analysis (PCA) were applied to compare and represent the chemical space coverage of each dataset. Subsequently, the Laplacian score, and Gini coefficient, were applied to delineate feature selection, and selectivity among properties respectively. Finally, we demonstrated the diversity between the datasets by employing Murcko’s, and central scaffolds systems, calculated three fingerprint descriptors, and analyzed their diversity by PCA and self-organizing maps (SOM). The optimized enumeration resulted in 1,610,268 compounds with NP-Likeness, and synthetic feasibility mean scores close to FDA, Non-FDA, and NPs datasets. The overlap between the chemical space of 1.6M was more prominent with NPs. Laplacian score has prioritized NP-likeness and hydrogen bond acceptor properties (1.0 and 0.923) respectively, while the Gini coefficient showed that all properties have selective effects on datasets (0.81 to 0.93). Scaffold and fingerprint diversity indicated that the descending order for the tested datasets was FDA, Non-FDA, NPs, 1.6M. Virtual combinatorial libraries based on NPs can be considered as a source of the combinatorial compound with NP-likeness properties. Furthermore, measuring molecular diversity is supposed to be performed by different methods to allow for comparison and better judgment. 


This link provides an illustration of the whole virtual combinatorial library using the TMAP algorithm in addition to the complete dataset. TMAP is a recent algorithm applied to visualize ultra-large high-dimensional chemical libraries for structures and physicochemical properties (Probst & Reymond, 2020). This approach creates and distributes intuitive tree representations of big data sets with arbitrary dimensionality in the order of 107.


To visualize the whole library of compounds, download the "index(2).rar", then extract the index.html that pop-up in the WinRAR application.
DOI: 10.5281/zenodo.6950711
title: Comparative Analysis of Anthraquinone and Chalcone Derivatives-Based Virtual Combinatorial Library. A Cheminformatics "Proof-of-Concept" Study

description: This computational “proof-of-concept” study illustrated the combinatorial approach used to explain how the selected natural products' structures undergo molecular diversity analysis. A virtual combinatorial library (1.6M) based on 20 anthraquinones and 24 chalcones were enumerated. The resulting compounds were optimized to the near drug-likeness properties and the physicochemical descriptors were calculated for all datasets including FDA, Non-FDA, and natural products (NPs) datasets from ZINC 15. UMAP and principal component analysis (PCA) were applied to compare and represent the chemical space coverage of each dataset. Subsequently, the Laplacian score, and Gini coefficient, were applied to delineate feature selection, and selectivity among properties respectively. Finally, we demonstrated the diversity between the datasets by employing Murcko’s, and central scaffolds systems, calculated three fingerprint descriptors, and analyzed their diversity by PCA and self-organizing maps (SOM). The optimized enumeration resulted in 1,610,268 compounds with NP-Likeness, and synthetic feasibility mean scores close to FDA, Non-FDA, and NPs datasets. The overlap between the chemical space of 1.6M was more prominent with NPs. Laplacian score has prioritized NP-likeness and hydrogen bond acceptor properties (1.0 and 0.923) respectively, while the Gini coefficient showed that all properties have selective effects on datasets (0.81 to 0.93). Scaffold and fingerprint diversity indicated that the descending order for the tested datasets was FDA, Non-FDA, NPs, 1.6M. Virtual combinatorial libraries based on NPs can be considered as a source of the combinatorial compound with NP-likeness properties. Furthermore, measuring molecular diversity is supposed to be performed by different methods to allow for comparison and better judgment. 


This link provides an illustration of the whole virtual combinatorial library using the TMAP algorithm in addition to the complete dataset. TMAP is a recent algorithm applied to visualize ultra-large high-dimensional chemical libraries for structures and physicochemical properties (Probst & Reymond, 2020). This approach creates and distributes intuitive tree representations of big data sets with arbitrary dimensionality in the order of 107.


To visualize the whole library of compounds, download the "index(2).rar", then extract the index.html that pop-up in the WinRAR application.
DOI: 10.5281/zenodo.6950712
title: Comparative Analysis of Anthraquinone and Chalcone Derivatives-Based Virtual Combinatorial Library. A Cheminformatics "Proof-of-Concept"

description: This computational “proof-of-concept” study illustrated the combinatorial approach used to explain how the selected natural products' structures undergo molecular diversity analysis. A virtual combinatorial library (1.6M) based on 20 anthraquinones and 24 chalcones were enumerated. The resulting compounds were optimized to the near drug-likeness properties and the physicochemical descriptors were calculated for all datasets including FDA, Non-FDA, and natural products (NPs) datasets from ZINC 15. UMAP and principal component analysis (PCA) were applied to compare and represent the chemical space coverage of each dataset. Subsequently, the Laplacian score, and Gini coefficient, were applied to delineate feature selection, and selectivity among properties respectively. Finally, we demonstrated the diversity between the datasets by employing Murcko’s, and central scaffolds systems, calculated three fingerprint descriptors, and analyzed their diversity by PCA and self-organizing maps (SOM). The optimized enumeration resulted in 1,610,268 compounds with NP-Likeness, and synthetic feasibility mean scores close to FDA, Non-FDA, and NPs datasets. The overlap between the chemical space of 1.6M was more prominent with NPs. Laplacian score has prioritized NP-likeness and hydrogen bond acceptor properties (1.0 and 0.923) respectively, while the Gini coefficient showed that all properties have selective effects on datasets (0.81 to 0.93). Scaffold and fingerprint diversity indicated that the descending order for the tested datasets was FDA, Non-FDA, NPs, 1.6M. Virtual combinatorial libraries based on NPs can be considered as a source of the combinatorial compound with NP-likeness properties. Furthermore, measuring molecular diversity is supposed to be performed by different methods to allow for comparison and better judgment. This link provides an illustration of the whole virtual combinatorial library using the TMAP algorithm in addition to the complete dataset. TMAP is a recent algorithm applied to visualize ultra-large high-dimensional chemical libraries for structures and physicochemical properties (Probst & Reymond, 2020). This approach creates and distributes intuitive tree representations of big data sets with arbitrary dimensionality in the order of 10<sup>7</sup>. <strong>To visualize the whole library of compounds, download the "index(2).rar", then extract the index.html that pop-up in the WinRAR application.</strong>
DOI: 10.5281/zenodo.6982309
title: Comparative Analysis of Anthraquinone and Chalcone Derivatives-Based Virtual Combinatorial Library. A Cheminformatics "Proof-of-Concept" Study

description: This computational “proof-of-concept” study illustrated the combinatorial approach used to explain how the selected natural products' structures undergo molecular diversity analysis. A virtual combinatorial library (1.6M) based on 20 anthraquinones and 24 chalcones were enumerated. The resulting compounds were optimized to the near drug-likeness properties and the physicochemical descriptors were calculated for all datasets including FDA, Non-FDA, and natural products (NPs) datasets from ZINC 15. UMAP and principal component analysis (PCA) were applied to compare and represent the chemical space coverage of each dataset. Subsequently, the Laplacian score, and Gini coefficient, were applied to delineate feature selection, and selectivity among properties respectively. Finally, we demonstrated the diversity between the datasets by employing Murcko’s, and central scaffolds systems, calculated three fingerprint descriptors, and analyzed their diversity by PCA and self-organizing maps (SOM). The optimized enumeration resulted in 1,610,268 compounds with NP-Likeness, and synthetic feasibility mean scores close to FDA, Non-FDA, and NPs datasets. The overlap between the chemical space of 1.6M was more prominent with NPs. Laplacian score has prioritized NP-likeness and hydrogen bond acceptor properties (1.0 and 0.923) respectively, while the Gini coefficient showed that all properties have selective effects on datasets (0.81 to 0.93). Scaffold and fingerprint diversity indicated that the descending order for the tested datasets was FDA, Non-FDA, NPs, 1.6M. Virtual combinatorial libraries based on NPs can be considered as a source of the combinatorial compound with NP-likeness properties. Furthermore, measuring molecular diversity is supposed to be performed by different methods to allow for comparison and better judgment. This link provides an illustration of the whole virtual combinatorial library using the TMAP algorithm in addition to the complete dataset. TMAP is a recent algorithm applied to visualize ultra-large high-dimensional chemical libraries for structures and physicochemical properties (Probst & Reymond, 2020). This approach creates and distributes intuitive tree representations of big data sets with arbitrary dimensionality in the order of 10<sup>7</sup>. <strong>To visualize the whole library of compounds, download the "index(2).rar", then extract the index.html that pop-up in the WinRAR application.</strong>
DOI: 10.15468/dl.kecb4a
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Pseudotsuga brevifolia W.C.Cheng & L.K.Fu"
 ]
}

 The dataset includes 20 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006750-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15784/601923
title: Datasets for Model Simulations of Tsunami Propagation from Circum-Pacific Subduction Zone to West Antarctic Ice Shelves (Thwaites)

description: The dataset includes model simulation results of the tsunami impact on the Thwaites Ice Shelf, specifically the time sequence of the water surface elevation of the tsunami, generated by circum-Pacific-Rim subduction zone earthquakes. The model simulations utilized the boundary conditions provided by the Pacific-wide simulations. The simulation scenarios include two historical events, the September 16, 2025 tsunami generated by the Mw 8.3 Illapel, Chile earthquake and the March 11, 2011 tsunami generated by the Mw 9.1 Honshu, Japan earthquake. Also included are four scenarios generated by hypothetical Mw 8.6 earthquakes in the Alaska, Central America, South America, and Kermadec subduction zones, respectively.
DOI: 10.22002/byypb-1sv76
title: Coxiella_ Verocells_28dpi

description: Coxiella burnetii Nine Mile phase II clone 4 (NMIIC4) strain. Coxiella cells purified from infected Vero cells.
description: <strong>Tilt Series Date:</strong> 2017-09-25</p>
<strong>Data Taken By:</strong> Debnath Ghosal</p>
<strong>Species / Specimen:</strong> Coxiella burnetii</p>
<strong>Strain:</strong> Nine Mile phase II clone 4</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 2°, constant angular increment, dosage: 130.0 eV/Å²,  defocus: -7.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Strain sent to us by Robert Heinzen. Data collected and processed by Debnath Ghosal using Caltech tomography pipeline.</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Refer to Heinzen lab.</p>
<strong>Sample Preparation:</strong> Freezing: 100% humidity, 4µl sample, Blot force 6, blot time 4 sec. Wait time 0 sec. Drain time 1 sec. Ethane/propane mix. Grid: Quantifoil Cu 2/2 200. Add gold to cells prior to freezing: 1ul of gold to 4 ul of cells, freeze.T</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-09-25-58/rawdata/</p>Cb_vero_201709250058.mrc,  Tilt Series (Pixel Size 0.39 nm), 968.4 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-09-25-58/rawdata/Cb_vero_201709250058.mrc"
        > <i class="download icon"></i> Download </a></p>

        Cb_vero_201709250058_part34_7.rec,  Reconstruction (Pixel Size 1.56 nm), 445.4 MB  
        
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        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/t5e3k-jkc39
title: V. cholerae RT4236 (stationary)

description: Classical strain with ctxA deletion.Cell harbors pMT5 plasmid (inducible toxT).
description: <strong>Tilt Series Date:</strong> 2015-05-11</p>
<strong>Data Taken By:</strong> Yi-Wei Chang</p>
<strong>Species / Specimen:</strong> Vibrio cholerae</p>
<strong>Strain:</strong> O395-N1</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1.0°, constant angular increment, dosage: 180.0 eV/Å²,  defocus: -6.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Strain from Ronald K. Taylor</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Overnight cultures of Vibrio cholerae O395 NI pMT5 grown in LB pH 6.5, 1 mM IPTG and 100 ug/mL ampicilin</p>
<strong>Sample Preparation:</strong> Vitrobot (MK iv) settings: 100% humidity, 3µl sample, 10 mg/ml BSA treated gold. Blot force 6, blot time 2.5 sec. Wait time 0 sec. Drain time 1 sec. Ethane/propane mix. Grid: Quantifoil Cu 2/2 finder.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ycw2015-05-11-54/rawdata/</p>20150511_AK07_10054.mrc,  Tilt Series (Pixel Size 0.39 nm), 3.4 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ycw2015-05-11-54/rawdata/20150511_AK07_10054.mrc"
        > <i class="download icon"></i> Download </a></p>

        20150511_AK07_10054_full.rec,  Reconstruction (Pixel Size 1.56 nm), 534.5 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/ycw2015-05-11-54/3dimage_56465/20150511_AK07_10054_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/7v4ay-ckv24
title: Saccharomyces cerevisiae - 6210

description: Raw data files of Saccharomyces cerevisiae - 6210
description: <strong>Tilt Series Date:</strong> 2017-04-08</p>
<strong>Data Taken By:</strong> Shrawan kumar Mageswaran</p>
<strong>Species / Specimen:</strong> Unspecified</p>
<strong>Strain:</strong> None</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 2°, constant angular increment, dosage: 120.0 eV/Å²,  defocus: -10.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> SerialEM</p>
<strong>Upload Method:</strong> reupload_downloaded</p>
<strong>Processing Software Used:</strong> IMOD</p>
<strong>Collaborators and Roles:</strong> Obtained from Dr. Markus Babst, University of Utah</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Cells were grown overnight in YPD, diluted to 0.2 OD in the morning and allowed to grow till OD ~0.6.</p>
<strong>Sample Preparation:</strong> Cells were diluted to an OD of 0.2-0.25 in YPD, applied to the grid, blotted from behind manually and plunge frozen in liquid ethane-propane mix. ~200 nm lamellae were FIB milled before imaging by TEM.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/smk2017-04-08-13/rawdata/</p>TS8.mrc,  Tilt Series (Pixel Size 0.39 nm), 1.8 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/smk2017-04-08-13/rawdata/TS8.mrc"
        > <i class="download icon"></i> Download </a></p>

        TS8_4bin_DB.rec,  Reconstruction (Pixel Size 0.0 nm), 222.2 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/smk2017-04-08-13/3dimage_86272/TS8_4bin_DB.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/kgdt9-ewf56
title: Halobacterium salinarum

description: Archaella and plate
description: <strong>Tilt Series Date:</strong> 2017-05-26</p>
<strong>Data Taken By:</strong> Debnath Ghosal</p>
<strong>Species / Specimen:</strong> Halobacterium salinarum</p>
<strong>Strain:</strong> </p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 2°, constant angular increment, dosage: 110.0 eV/Å²,  defocus: -7.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Strain sent to us by Robert Gunsalus, UCLA. Data collected and processed by Ki Woo and Debnath Ghosal</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Standard H. salinarum medium</p>
<strong>Sample Preparation:</strong> Vitrobot (MK iv) settings: 100% humidity, 4µl sample, Blot force 6, blot time 3.5 sec. Wait time 8 sec. Drain time 1 sec. Ethane/propane mix. Grid: Quantifoil Cu 2/2 200. Add gold to cells prior to freezing: 1ul of gold to 4 ul of cells, freeze.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-05-26-16/rawdata/</p>Halobacterium_20170525e0003.mrc,  Tilt Series (Pixel Size 0.39 nm), 797.5 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-05-26-16/rawdata/Halobacterium_20170525e0003.mrc"
        > <i class="download icon"></i> Download </a></p>

        Halobacterium_20170525e0003.rec,  Reconstruction (Pixel Size 1.56 nm), 889.0 MB  
        
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