cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26695152
title: Additional file 3 of Comparison of diffusion ranges at different local anesthetic volumes during superior laryngeal nerve block

description: Supplementary Material 3.
DOI: 10.6084/m9.figshare.26695152.v1
title: Additional file 3 of Comparison of diffusion ranges at different local anesthetic volumes during superior laryngeal nerve block

description: Supplementary Material 3.
DOI: 10.5281/zenodo.13338090
title: CRISM ML dataset

description: This dataset is required to train the models in the CRISM ML toolbox [1].

In the project, we demonstrate the utility of machine learning in two essential CRISM analysis tasks: nonlinear noise removal and mineral classification. We train a hierarchical Bayesian model for estimating distributions of spectral patterns on pixel-scale training data collected from dozens of well-characterized CRISM images. 

The following files are included:



CRISM_bland_unratioed.mat: unratioed training spectra for bland pixels.

CRISM_labeled_pixels_ratioed.mat: ratioed training spectra for mineral classes.

CRISM_labeled_pixel_patterns.pdf: visualization of the training segmentation maps and average spectra.


The training spectra are in Matlab v7.3 (and newer) format. To load them in Python, use the mat73 library, because scipy doesn't support the format.

The bland unratioed spectra have the following variables:




Name
Size
Description




pixspec
337 617 × 350
Unratioed spectra


im_names
340
List of CRISM image names, mapping them to numerical IDs


pixims
337 617
Numerical ID of the image the spectrum is from


pixcrds
337 617 × 2
(x,y) coordinates of the points in the original image




 

The labeled ratioed pixels have the following variables:




Name
Size
Description


pixspec
592 413 × 350
Ratioed spectra


pixlabs
592 413
Mineral labels


im_names
77
List of CRISM image names, mapping them to numerical IDs


pixims
592 413
Numerical ID of the image the spectrum is from


pixpat
592 413
ID of the connected patch in the image the pixel belongs to


pixcrds
592 413 × 2
(x,y) coordinates of pixels in their respective image




 

Citation (cite this paper when using the data):



Plebani, E., Ehlmann, B. L., Leask, E. K., Fox, V. K., & Dundar, M. M. (2022). A machine learning toolkit for CRISM image analysis. Icarus, 376, 114849.
DOI: 10.5281/zenodo.13338091
title: CRISM ML dataset

description: This dataset is required to train the models in the CRISM ML toolbox [1].

In the project, we demonstrate the utility of machine learning in two essential CRISM analysis tasks: nonlinear noise removal and mineral classification. We train a hierarchical Bayesian model for estimating distributions of spectral patterns on pixel-scale training data collected from dozens of well-characterized CRISM images. 

The following files are included:



CRISM_bland_unratioed.mat: unratioed training spectra for bland pixels.

CRISM_labeled_pixels_ratioed.mat: ratioed training spectra for mineral classes.

CRISM_labeled_pixel_patterns.pdf: visualization of the training segmentation maps and average spectra.


The training spectra are in Matlab v7.3 (and newer) format. To load them in Python, use the mat73 library, because scipy doesn't support the format.

The bland unratioed spectra have the following variables:




Name
Size
Description




pixspec
337 617 × 350
Unratioed spectra


im_names
340
List of CRISM image names, mapping them to numerical IDs


pixims
337 617
Numerical ID of the image the spectrum is from


pixcrds
337 617 × 2
(x,y) coordinates of the points in the original image




 

The labeled ratioed pixels have the following variables:




Name
Size
Description


pixspec
592 413 × 350
Ratioed spectra


pixlabs
592 413
Mineral labels


im_names
77
List of CRISM image names, mapping them to numerical IDs


pixims
592 413
Numerical ID of the image the spectrum is from


pixpat
592 413
ID of the connected patch in the image the pixel belongs to


pixcrds
592 413 × 2
(x,y) coordinates of pixels in their respective image




 

Citation (cite this paper when using the data):



Plebani, E., Ehlmann, B. L., Leask, E. K., Fox, V. K., & Dundar, M. M. (2022). A machine learning toolkit for CRISM image analysis. Icarus, 376, 114849.
DOI: 10.6084/m9.figshare.25441008
title: Additional file 2 of Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics

description: Additional file 2. Supplementary Data 1. Annotation of the genes from the synthetic microbial community of 12 species using different taxonomic classification tools and strategies.
DOI: 10.6084/m9.figshare.25441008.v1
title: Additional file 2 of Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics

description: Additional file 2. Supplementary Data 1. Annotation of the genes from the synthetic microbial community of 12 species using different taxonomic classification tools and strategies.
DOI: 10.6084/m9.figshare.26695167
title: Additional file 3 of Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics

description: Additional file 3. Supplementary Data 2. Identification result of protein groups and peptides from the synthetic microbial community using different databases.
DOI: 10.6084/m9.figshare.26695167.v1
title: Additional file 3 of Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics

description: Additional file 3. Supplementary Data 2. Identification result of protein groups and peptides from the synthetic microbial community using different databases.
DOI: 10.6084/m9.figshare.26695170
title: Additional file 4 of Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics

description: Additional file 4. Supplementary Data 3. Annotation of genes from the stool sample using different taxonomic classification tools and strategies.
DOI: 10.6084/m9.figshare.26695170.v1
title: Additional file 4 of Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics

description: Additional file 4. Supplementary Data 3. Annotation of genes from the stool sample using different taxonomic classification tools and strategies.
DOI: 10.6084/m9.figshare.26695173
title: Additional file 5 of Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics

description: Additional file 5. Supplementary Data 4. Identification result of protein groups and peptides from the stool sample using different databases.
DOI: 10.6084/m9.figshare.26695173.v1
title: Additional file 5 of Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics

description: Additional file 5. Supplementary Data 4. Identification result of protein groups and peptides from the stool sample using different databases.
DOI: 10.6084/m9.figshare.26695176
title: Additional file 6 of Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics

description: Additional file 6. Supplementary Data 5. The relative taxonomic abundance at the metaproteomic (MP) level and at the metagenomic (MG) level.
DOI: 10.6084/m9.figshare.26695176.v1
title: Additional file 6 of Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics

description: Additional file 6. Supplementary Data 5. The relative taxonomic abundance at the metaproteomic (MP) level and at the metagenomic (MG) level.
DOI: 10.6084/m9.figshare.26695179
title: Additional file 7 of Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics

description: Additional file 7. Supplementary Data 6. Annotation of the genes from the in-silico mixed data using different taxonomic classification tools and strategies.
DOI: 10.6084/m9.figshare.26695179.v1
title: Additional file 7 of Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics

description: Additional file 7. Supplementary Data 6. Annotation of the genes from the in-silico mixed data using different taxonomic classification tools and strategies.
DOI: 10.6084/m9.figshare.26695182
title: Additional file 8 of Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics

description: Additional file 8. Supplementary Data 7. Identification result of protein groups and peptides from the in-silico mixed metaproteomic data using different databases.
DOI: 10.6084/m9.figshare.26695182.v1
title: Additional file 8 of Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics

description: Additional file 8. Supplementary Data 7. Identification result of protein groups and peptides from the in-silico mixed metaproteomic data using different databases.
DOI: 10.6084/m9.figshare.26695239
title: Additional file 5 of IGF2BP3 prevent HMGB1 mRNA decay in bladder cancer and development

description: Additional file 5: Table S1. List of common HALLMARKs significantly correlated with IGF2BP3 expression in TCGA BLCA, E-MATB-4321, and GSE48276 datasets.
DOI: 10.6084/m9.figshare.26695239.v1
title: Additional file 5 of IGF2BP3 prevent HMGB1 mRNA decay in bladder cancer and development

description: Additional file 5: Table S1. List of common HALLMARKs significantly correlated with IGF2BP3 expression in TCGA BLCA, E-MATB-4321, and GSE48276 datasets.
DOI: 10.6084/m9.figshare.26695242
title: Additional file 6 of IGF2BP3 prevent HMGB1 mRNA decay in bladder cancer and development

description: Additional file 6: Table S2. POSTAR3 Binding site records of target HMGB1 RNA.
DOI: 10.6084/m9.figshare.26695242.v1
title: Additional file 6 of IGF2BP3 prevent HMGB1 mRNA decay in bladder cancer and development

description: Additional file 6: Table S2. POSTAR3 Binding site records of target HMGB1 RNA.
DOI: 10.15468/dl.rssb4h
title: Occurrence Download

description: A dataset containing 1214 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "OccurrenceStatus is Present",
 "TaxonKey is Isoodon auratus (Ramsay, 1887)"
 ]
}

 The dataset includes 1214 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0072866-240626123714530/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.26695293
title: Additional file 3 of Clinical effectiveness of a multitarget urine DNA test for urothelial carcinoma detection: a double-blinded, multicenter, prospective trial

description: Supplementary Material 3
DOI: 10.6084/m9.figshare.26695293.v1
title: Additional file 3 of Clinical effectiveness of a multitarget urine DNA test for urothelial carcinoma detection: a double-blinded, multicenter, prospective trial

description: Supplementary Material 3
DOI: 10.6084/m9.figshare.25441028
title: Additional file 2 of miR-430 regulates zygotic mRNA during zebrafish embryogenesis

description: Additional file 2: Table S1. Comma-separated values, &lt;.csv&gt;; Total raw read and labeled read counts for all experiments and samples, including spike-ins. Rows represent different 3′UTR isoforms (for coding genes) or full-length transcript (non-coding genes). Columns represent: A, Ensembl gene IDs by itself (non-coding transcripts) + 3′UTR isoforms (coding transcripts), or ERCC IDs (spike ins); B, Chromosome name; C, Genomic start of feature; D, Genomic end of feature; E; DNA strand of feature; F, Number of Ts within feature; H-AO, Total raw read counts; AP-BW, Labeled read counts. Column names from H to BW contain informative names for stages in hours and replicates or treatment condition (LNA_Mis = control LNA, LNA430 = miR-430 LNA).
DOI: 10.6084/m9.figshare.25441028.v1
title: Additional file 2 of miR-430 regulates zygotic mRNA during zebrafish embryogenesis

description: Additional file 2: Table S1. Comma-separated values, &lt;.csv&gt;; Total raw read and labeled read counts for all experiments and samples, including spike-ins. Rows represent different 3′UTR isoforms (for coding genes) or full-length transcript (non-coding genes). Columns represent: A, Ensembl gene IDs by itself (non-coding transcripts) + 3′UTR isoforms (coding transcripts), or ERCC IDs (spike ins); B, Chromosome name; C, Genomic start of feature; D, Genomic end of feature; E; DNA strand of feature; F, Number of Ts within feature; H-AO, Total raw read counts; AP-BW, Labeled read counts. Column names from H to BW contain informative names for stages in hours and replicates or treatment condition (LNA_Mis = control LNA, LNA430 = miR-430 LNA).
DOI: 10.6084/m9.figshare.26695326
title: Additional file 6 of miR-430 regulates zygotic mRNA during zebrafish embryogenesis

description: Additional file 6: Table S2. Comma-separated values, &lt;.csv&gt;; Coding and non-coding gene’s MZT class, hourly grouped transcriptional activation events and miR-430 targets. Each row represents a single gene. Columns represent: A, Ensembl gene IDs; B, Whether or not genes are annotated as coding or non-coding; C, MZT gene class as explained in Additional file 3: Fig. S2; D, To which of the hourly groups of transcriptional activation events the gene was assigned, if any; E; Highest expressed 3′UTR isoform used for miR-430 target prediction (see methods); F-J, Number and types of miR-430 binding sites found in the 3′UTR (see methods); K, Whether or not this gene has been previously deemed a miR-430 target, a control gene, a new putative target (putative_target; this study), or none of the previous (others).
DOI: 10.6084/m9.figshare.26695326.v1
title: Additional file 6 of miR-430 regulates zygotic mRNA during zebrafish embryogenesis

description: Additional file 6: Table S2. Comma-separated values, &lt;.csv&gt;; Coding and non-coding gene’s MZT class, hourly grouped transcriptional activation events and miR-430 targets. Each row represents a single gene. Columns represent: A, Ensembl gene IDs; B, Whether or not genes are annotated as coding or non-coding; C, MZT gene class as explained in Additional file 3: Fig. S2; D, To which of the hourly groups of transcriptional activation events the gene was assigned, if any; E; Highest expressed 3′UTR isoform used for miR-430 target prediction (see methods); F-J, Number and types of miR-430 binding sites found in the 3′UTR (see methods); K, Whether or not this gene has been previously deemed a miR-430 target, a control gene, a new putative target (putative_target; this study), or none of the previous (others).
DOI: 10.6084/m9.figshare.26695341
title: Additional file 4 of TMEM120B strengthens breast cancer cell stemness and accelerates chemotherapy resistance via β1-integrin/FAK-TAZ-mTOR signaling axis by binding to MYH9

description: Additional file 4: DEGs within TMEM120B-KO cells
DOI: 10.6084/m9.figshare.26695341.v1
title: Additional file 4 of TMEM120B strengthens breast cancer cell stemness and accelerates chemotherapy resistance via β1-integrin/FAK-TAZ-mTOR signaling axis by binding to MYH9

description: Additional file 4: DEGs within TMEM120B-KO cells
DOI: 10.6084/m9.figshare.26695344.v1
title: Additional file 5 of TMEM120B strengthens breast cancer cell stemness and accelerates chemotherapy resistance via β1-integrin/FAK-TAZ-mTOR signaling axis by binding to MYH9

description: Additional file 5: Candidates for TMEM120B binding partner
DOI: 10.25549/independent-c69-2448
title: SCLC v City of LA, 34. Petition for writ of mandate / prohibition, 1991-05-30

description: 34: Case no. Related case nos: B057636, B057609. Court of Appeal of California Second Appellate District, Division Two. Southern Christian Leadership Conference of Greater Los Angeles, et al., petitioners, v. Superior Court of the State of California for the County of Los Angeles, respondent. City of Los Angeles, City Council; Daryl Gates, Chief of Police; The City of Los Angeles; James K. Hahn, Los Angeles City Attorney; Board of Police Commissioners, City of Los Angeles, real parties in interest. California Superior Court case no. BS006789 and California Superior Court case no. BC025356, Honorable Ronald Sohigian, Judge. Petition for writ of mandate / prohibition, 1991 May 30. Includes Appendix A: Orcer, Court of Appeal, case no. B057636, filed 1991 April 16. Includes Appendix B: Order, Court of Appeal, case no. BS006789, filed 1991 April 16. Includes Proof of service, 1991 May 31.
DOI: 10.15468/dl.qtwhik
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mammalia",
 "DecimalLatitude is greater than 32.7793560164475",
 "DecimalLatitude is less than 33.1700469953277",
 "DecimalLongitude is greater than -85.9630805188559",
 "DecimalLongitude is less than -85.5003639620917"
 ]
}

 The dataset includes 21 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003004-160311141623029/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jdx8tr
title: Occurrence Download

description: A dataset containing 281098 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "PublishingOrg is one of (2a7e3080-28a9-11dd-97cd-b8a03c50a862, e2e717bf-551a-4917-bdc9-4fa0f342c530, eac88d99-9f6c-4031-8fc4-8088f0e0dfe7, eace4687-50e8-4f9a-829b-29ff8ff1fa8b, 0c23482f-89f3-4efa-b6ed-7b25dadde4fc)",
 "Year 2020-2023",
 "Country is one of (Colombia)",
 "GadmGid is one of (COL.5_2, COL.15.11_2, COL.15.116_2, COL.15.20_2, COL.15.60_2, COL.15.94_2, COL.15.92_2, COL.15.109_2, COL.15.30_2, COL.15.38_2, COL.15.91_2, COL.15.85_2)"
 ]
 }
 ]
}

 The dataset includes 281098 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003571-240216155721649/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hwfthm
title: Occurrence Download

description: A dataset containing 57928 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Zoological Museum Amsterdam, University of Amsterdam (NL) - Lepidoptera_Nymphalidae_Palearctic"
 ]
}

 The dataset includes 57928 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003004-160822134323880/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yhqmzf
title: Occurrence Download

description: A dataset containing 422 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Zapus hudsonius luteus Miller, 1911)"
 ]
}

 The dataset includes 422 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003588-240216155721649/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nb2u1y
title: Occurrence Download

description: A dataset containing 1353 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Abies pinsapo Boiss."
 ]
}

 The dataset includes 1353 records from 85 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003004-170925132151566/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d99tba
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Ormosia microphylla Merr."
 ]
}

 The dataset includes 15 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005306-230828120925497/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q0mnhy
title: Occurrence Download

description: A dataset containing 65674 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Crotophaga sulcirostris Swainson, 1827"
 ]
}

 The dataset includes 65674 records from 136 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003004-171025204727650/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/independent-c69-2410
title: SCLC v City of LA, 34. Petition for writ of mandate / prohibition, 1991-05-30

description: 34: Case no. Related case nos: B057636, B057609. Court of Appeal of California Second Appellate District, Division Two. Southern Christian Leadership Conference of Greater Los Angeles, et al., petitioners, v. Superior Court of the State of California for the County of Los Angeles, respondent. City of Los Angeles, City Council; Daryl Gates, Chief of Police; The City of Los Angeles; James K. Hahn, Los Angeles City Attorney; Board of Police Commissioners, City of Los Angeles, real parties in interest. California Superior Court case no. BS006789 and California Superior Court case no. BC025356, Honorable Ronald Sohigian, Judge. Petition for writ of mandate / prohibition, 1991 May 30. Includes Appendix A: Orcer, Court of Appeal, case no. B057636, filed 1991 April 16. Includes Appendix B: Order, Court of Appeal, case no. BS006789, filed 1991 April 16. Includes Proof of service, 1991 May 31.