cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7057406
title: Metocean Conditions at Sørlige Nordsjø II with NORA3 (1982-2022)

description: Sørlige Nordsjø II Dataset
Description
This dataset concentrates on metocean conditions at Sørlige Nordsjø II (SN2), an offshore Norwegian area in intermediate waters. The dataset features hourly time series data for each grid point, covering the years from 1982 to 2022. This extensive temporal range makes the dataset indispensable for research on long-term climate patterns and offshore wind energy potential in the North Sea.
 
Technical Specifications

Format: netcdf4_classic
Variables in Sørlige Nordsjø II (SN2) netCDF File
time

Size: 8760x1
Dimensions: time
Datatype: double
Description: Represents the time for each data entry, starting from the year 1982 and extending through 2022.
Attributes: Units are in 'hours since 1970-01-01 00:00:00'.
z

Size: 8x1
Dimensions: z
Datatype: double
Description: Specifies the heights above the surface at which measurements were taken.
lat

Size: 317x1
Dimensions: coordinate
Datatype: double
Description: Latitude coordinates for each grid point in the area.
lon

Size: 317x1
Dimensions: coordinate
Datatype: double
Description: Longitude coordinates for each grid point in the area.
Dir

Size: 8760x317x8
Dimensions: time, coordinate, z
Datatype: double
Description: Mean wind direction at each time, location, and height.
U

Size: 8760x317x8
Dimensions: time, coordinate, z
Datatype: double
Description: Mean wind speed at each time, location, and height.
hs

Size: 8760x317
Dimensions: time, coordinate
Datatype: double
Description: Significant wave height at each time and location.
tp

Size: 8760x317
Dimensions: time, coordinate
Datatype: double
Description: Peak wave period at each time and location.
u_star

Size: 8760x317
Dimensions: time, coordinate
Datatype: double
Description: Friction velocity at each time and location.
wd

Size: 8760x317
Dimensions: time, coordinate
Datatype: double
Description: Wave heading at each time and location.
DOI: 10.5281/zenodo.8355245
title: Curvature Dynamics of a Coastal Barotropic Outflow Jet on a Slope

description: This repository contains .zip file of all of the ROMS simulations used to produce the results in a submitted manuscript. Each project directory (uploaded as a .zip) is named by the drag coefficient, bottom slope, and latitude for the particular simulation. The file structure and content within each directory is
-ocean.in #ROMS input file
-ana_drag.h #analytic depth-dependent quadratic drag formulation
-ana_fsobc.h #analytic free-surface open boundary condition
-ana_grid.h #analytic grid
-ana_m2obc.h #analytic depth-averaged open boundary condition
-ana_mask.h #analytic mask
-/results/ocean_avg_00360.nc #3-hour average of model fields on day 10 of the simulation
-/results/ocean_dia_00360.nc #3-hour average momentum diagnostics on day 10 of the simulation
-/results/path.nc #relevant variables interpolated to the jet centerline trajectory
COAWST is an open source code and can be download at https://coawstmodel-trac.sourcerepo.com/coawstmodel_COAWST/. Descriptions of the input and output files can be found in the manual distributed with the model code and in the glossary at the end of the ocean.in file.
Corresponding author: Walter Torres, wtorres@uw.edu
DOI: 10.5281/zenodo.8355246
title: Curvature Dynamics of a Coastal Barotropic Outflow Jet on a Slope

description: This repository contains .zip file of all of the ROMS simulations used to produce the results in a submitted manuscript. Each project directory (uploaded as a .zip) is named by the drag coefficient, bottom slope, and latitude for the particular simulation. The file structure and content within each directory is
-ocean.in #ROMS input file
-ana_drag.h #analytic depth-dependent quadratic drag formulation
-ana_fsobc.h #analytic free-surface open boundary condition
-ana_grid.h #analytic grid
-ana_m2obc.h #analytic depth-averaged open boundary condition
-ana_mask.h #analytic mask
-/results/ocean_avg_00360.nc #3-hour average of model fields on day 10 of the simulation
-/results/ocean_dia_00360.nc #3-hour average momentum diagnostics on day 10 of the simulation
-/results/path.nc #relevant variables interpolated to the jet centerline trajectory
COAWST is an open source code and can be download at https://coawstmodel-trac.sourcerepo.com/coawstmodel_COAWST/. Descriptions of the input and output files can be found in the manual distributed with the model code and in the glossary at the end of the ocean.in file.
Corresponding author: Walter Torres, wtorres@uw.edu
DOI: 10.5281/zenodo.10007041
title: Data products of the surface brightness modelling of the confirmed and candidates lenses in Borsato et al. 2023.

description: This repository contains the data products for the surface brightness analysis of strongly lensed candidates presented in Borsato et al. 2023. This repository is organized as follows.
The folder `cutouts' contains the cutouts (20x20 arcsec) of all the HST snapshots.
The folder `SB_models' includes the A and B folders containing the confirmed lenses and candidate lenses (A and B classes in the paper). Each of these folders contains a set of subfolders including the image cutouts, surface brightness models, residuals, noise maps, and PSFs of the confirmed lenses.
DOI: 10.5281/zenodo.10007040
title: Data products of the surface brightness modelling of the confirmed and candidates lenses in Borsato et al. 2023.

description: This repository contains the data products for the surface brightness analysis of strongly lensed candidates presented in Borsato et al. 2023. This repository is organized as follows.
The folder `cutouts' contains the cutouts (20x20 arcsec) of all the HST snapshots.
The folder `SB_models' includes the A and B folders containing the confirmed lenses and candidate lenses (A and B classes in the paper). Each of these folders contains a set of subfolders including the image cutouts, surface brightness models, residuals, noise maps, and PSFs of the confirmed lenses.
DOI: 10.5281/zenodo.10048132
title: A Dependency Graph for 460,000 Papers and Their Software Mentions from the CZI Software Mentions Dataset

description: Using the CZI Software Mentions Dataset and ecosyste.ms we create a graph of papers, their mentioned software, and recursive dependencies of each piece of software across 466,000 papers and three software registries (PyPI, CRAN, and BioConductor).
DOI: 10.5281/zenodo.10048131
title: A Dependency Graph for 460,000 Papers and Their Software Mentions from the CZI Software Mentions Dataset

description: Using the CZI Software Mentions Dataset and ecosyste.ms we create a graph of papers, their mentioned software, and recursive dependencies of each piece of software across 466,000 papers and three software registries (PyPI, CRAN, and BioConductor).
DOI: 10.6084/m9.figshare.24305440
title: data-<b>We are all ordinary: Common ingroup identity improves public’s attitudes toward doctors</b>

description: This study explores whether common ingroup identity could improve attitudes toward doctors based on the common ingroup identity model.
DOI: 10.6084/m9.figshare.24305440.v1
title: data-<b>We are all ordinary: Common ingroup identity improves public’s attitudes toward doctors</b>

description: This study explores whether common ingroup identity could improve attitudes toward doctors based on the common ingroup identity model.
DOI: 10.34740/kaggle/dsv/6811662
title: 📒 Meta Kaggle ported to MS SQL SERVER 2022 DB
DOI: 10.5519/qd.suk3k63b
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (55 records)
DOI: 10.6084/m9.figshare.23613534
title: <strong>Polycystic Ovarian Syndrome Physiologic Pathways Implicated Through Clustering of Genetic Loci.</strong> Maria I. Stamou*, Kirk T. Smith*, Hyunkyung Kim*, Ravikumar Balasubramanian, Kathryn J. Gray, Miriam Udler.

description: Tables S1-S8 for manuscript entitled " <strong>Polycystic Ovarian Syndrome Physiologic Pathways Implicated Through Clustering of Genetic Loci.".</strong> <br>
DOI: 10.6084/m9.figshare.23613534.v2
title: <strong>Polycystic Ovarian Syndrome Physiologic Pathways Implicated Through Clustering of Genetic Loci.</strong> Maria I. Stamou*, Kirk T. Smith*, Hyunkyung Kim*, Ravikumar Balasubramanian, Kathryn J. Gray, Miriam Udler.

description: Tables S1-S8 for manuscript entitled " <strong>Polycystic Ovarian Syndrome Physiologic Pathways Implicated Through Clustering of Genetic Loci.".</strong> <br>
DOI: 10.5287/ora-py75jq8e5
title: Regulation of inositol 5-phosphatase activity by the C2 domain of SHIP1 and SHIP2

description: Raw data set for both SHIP1 and SHIP2 (DNX file). Cluster and state data for Both SHIP1 and SHIP2 (Excel files) – used for significance testing of differences that were found across data sets with in the experiments. Woods plots for both experiments comparing both constructs of SHIP1 and SHIP2. Highlights absolute uptake differences that were found between constructs and shows significance differences in red/blue colouring (From Deuteros – PNG file).
DOI: 10.7944/p9y1he8l
title: Habitat Use and Community Survey of Passerine Birds in the Berry's Creek Study Area and New Jersey Meadowlands 2021 Data

description: Data set contains result from point count surveys conducted in the Berry's Creek Study Area. The data was collected by New Jersey Audobon.
DOI: 10.5066/p9x7qkbf
title: Bathymetric data for a near-shore area of the Laguna Madre near South Padre Island, Texas, July 2021

description: The U.S. Geological Survey, in cooperation with the City of South Padre Island, Texas, collected baseline bathymetric data for a near-shore area of the Laguna Madre near South Padre Island in July 2021. Bathymetric survey points were collected using real-time kinematic Global Navigation Satellite System (RTK-GNSS) equipment. The data included in this data release are presented in a comma-separated values (csv) file format.
DOI: 10.5066/p9aukoc8
title: Hydrodynamic Data for a Near-Shore Area of the Laguna Madre near South Padre Island, Texas, August 2021 to April 2022

description: The U.S. Geological Survey, in cooperation with the City of South Padre Island, Texas, collected base line hydrodynamic (tidal water level and current velocity) data for a near-shore area of the Laguna Madre near South Padre Island from August 18, 2021, to April 27, 2022. The data included in this data release are presented in a comma-separated values (csv) file format.
DOI: 10.34740/kaggle/dsv/6811725
title: How Often Do People Buy Mumbai Local Tickets
DOI: 10.5066/p9grja3x
title: Seagrass, benthic invertebrate, and water-quality data for a near-shore area of the Laguna Madre near South Padre Island, Texas, July 2021

description: The U.S. Geological Survey, in cooperation with the City of South Padre Island, Texas, collected baseline seagrass, benthic invertebrate, and water-quality data for a near-shore area of the Laguna Madre near South Padre Island in July 2021. Sample and data collection points were located within a grid consisting of eight transects and three sampling points on each transect. The data included in this data release are presented in a comma-separated values (csv) file format.
DOI: 10.6084/m9.figshare.24449452
title: Raw data of mNGS

description: We retrospectively enrolled 108 patients with infectious disease and noninfectious disease who visited West China Hospital of Sichuan University from January 2019 to June 2022. Pathogens in core needle biopsy tissue were detected using mNGS and traditional pathological examination. The diagnostic accuracy of the combined mNGS and histopathology protocol for pulmonary infectious diseases was contrasted with that of histopathological examination alone.The is the raw data of part patients who have completed mNGS of biopsy tissue.
DOI: 10.6084/m9.figshare.24449452.v1
title: Raw data of mNGS

description: We retrospectively enrolled 108 patients with infectious disease and noninfectious disease who visited West China Hospital of Sichuan University from January 2019 to June 2022. Pathogens in core needle biopsy tissue were detected using mNGS and traditional pathological examination. The diagnostic accuracy of the combined mNGS and histopathology protocol for pulmonary infectious diseases was contrasted with that of histopathological examination alone.The is the raw data of part patients who have completed mNGS of biopsy tissue.
DOI: 10.6084/m9.figshare.24450364
title: Theta rhythm in rat hippocampus during running

description: LFPs collected from 3 64-channel arrays implanted in CA1 of a rat running on a 3-arm maze to collect water rewards. This data is used to evaluate algorithms for decoding the rat's position from the LFP. Note that the values are 1-indexed, as is convention in matlab. For using in python, which is 0-indexed, I recommend subtracting 1 from lapID and trainInds.<b>X</b>: The multi-electrode LFP recorded in CA1, already downsampled to 25 Hz, highpass filtered at 2 Hz, and made complex-valued using the Hilbert transform. Shape = 3808 timesteps x 192 electrodes<b>lapID</b>: An array of shape (3808, 4) where columns = [trial number, cross validation fold, maze arm, time step in original file]. The last column is useful because the data is truncated to only include periods where the rat is running towards the reward port. <b>trainInds</b>: Indices for the training data, containing no overlap with lapID cross validation column indices. Shape = (2265, 5), with each column specifying which indices to use for training in one of 5 folds. This variable was created because we found that it was useful to have the same number of training samples from each maze arm (ie the decoder would assign maze arm without bias).<b>pos</b>: An array of shape (3808,1) indicating the position of the rat along the corresponding track, in mm.
DOI: 10.6084/m9.figshare.24450364.v1
title: Theta rhythm in rat hippocampus during running

description: LFPs collected from 3 64-channel arrays implanted in CA1 of a rat running on a 3-arm maze to collect water rewards. This data is used to evaluate algorithms for decoding the rat's position from the LFP. Note that the values are 1-indexed, as is convention in matlab. For using in python, which is 0-indexed, I recommend subtracting 1 from lapID and trainInds.<b>X</b>: The multi-electrode LFP recorded in CA1, already downsampled to 25 Hz, highpass filtered at 2 Hz, and made complex-valued using the Hilbert transform. Shape = 3808 timesteps x 192 electrodes<b>lapID</b>: An array of shape (3808, 4) where columns = [trial number, cross validation fold, maze arm, time step in original file]. The last column is useful because the data is truncated to only include periods where the rat is running towards the reward port. <b>trainInds</b>: Indices for the training data, containing no overlap with lapID cross validation column indices. Shape = (2265, 5), with each column specifying which indices to use for training in one of 5 folds. This variable was created because we found that it was useful to have the same number of training samples from each maze arm (ie the decoder would assign maze arm without bias).<b>pos</b>: An array of shape (3808,1) indicating the position of the rat along the corresponding track, in mm.
DOI: 10.5281/zenodo.10046892
title: A dataset of 8-dimensional Q-factorial Fano toric varieties of Picard rank 2

description: This is a dataset of randomly generated 8-dimensional Q-factorial Fano toric varieties of Picard rank 2.
The data is divided into four plain text files:

bound_7_terminal.txt
bound_7_non_terminal.txt
bound_10_terminal.txt
bound_10_non_terminal.txt
The numbers 7 and 10 in the file names indicate the bound on the weights used when generating the data. Those varieties with at worst terminal singularities are in the files "bound_N_terminal.txt", and those with non-terminal singularities are in the files "bound_N_non_terminal.txt". The data within each file is de-duplicated, however the data in different files may contain duplicates (for example, it is possible that "bound_7_terminal.txt" and "bound_10_terminal.txt" contain some identical entries).
 
Each line of a file specifies the entries of a (2 x 10)-matrix. For example, the first line of "bound_7_terminal.txt" is:

[[5,6,7,7,5,2,5,3,2,2],[0,0,0,1,1,2,6,4,3,3]]
and this corresponds to the 8-dimensional Q-factorial Fano toric variety with weight matrix

5  6  7  7  5  2  5  3  2  2
0  0  0  1  1  2  6  4  3  3
and stability condition given by the sum of the columns, which in this case is

44
20
It can be checked that, in this case, the corresponding variety has at worst terminal singularities. In this example the largest occurring weight in the matrix is 7.
 
The number of entries in each file is:

bound_7_terminal.txt: 5000000
bound_7_non_terminal.txt: 5000000
bound_10_terminal.txt: 10000000
bound_10_non_terminal.txt: 10000000
 
For details, see the paper:

"Machine learning detects terminal singularities", Tom Coates, Alexander M. Kasprzyk, and Sara Veneziale. Neural Information Processing Systems (NeurIPS), 2023.
 
Magma code capable of generating this dataset is in the file "terminal_dim_8.m". The bound on the weights is set on line 142 by adjusting the value of 'k' (currently set to 10). The target dimension is set on line 143 by adjusting the value of 'dim' (currently set to 8). It is important to note that this code does not attempt to remove duplicates. The code also does not guarantee that the resulting variety has dimension 8. Deduplication and verification of the dimension need to be done separately, after the data has been generated.
 
If you make use of this data, please cite the above paper and the DOI for this data:
doi:10.5281/zenodo.10046893
DOI: 10.5281/zenodo.10046893
title: A dataset of 8-dimensional Q-factorial Fano toric varieties of Picard rank 2

description: This is a dataset of randomly generated 8-dimensional Q-factorial Fano toric varieties of Picard rank 2.
The data is divided into four plain text files:

bound_7_terminal.txt
bound_7_non_terminal.txt
bound_10_terminal.txt
bound_10_non_terminal.txt
The numbers 7 and 10 in the file names indicate the bound on the weights used when generating the data. Those varieties with at worst terminal singularities are in the files "bound_N_terminal.txt", and those with non-terminal singularities are in the files "bound_N_non_terminal.txt". The data within each file is de-duplicated, however the data in different files may contain duplicates (for example, it is possible that "bound_7_terminal.txt" and "bound_10_terminal.txt" contain some identical entries).
 
Each line of a file specifies the entries of a (2 x 10)-matrix. For example, the first line of "bound_7_terminal.txt" is:

[[5,6,7,7,5,2,5,3,2,2],[0,0,0,1,1,2,6,4,3,3]]
and this corresponds to the 8-dimensional Q-factorial Fano toric variety with weight matrix

5  6  7  7  5  2  5  3  2  2
0  0  0  1  1  2  6  4  3  3
and stability condition given by the sum of the columns, which in this case is

44
20
It can be checked that, in this case, the corresponding variety has at worst terminal singularities. In this example the largest occurring weight in the matrix is 7.
 
The number of entries in each file is:

bound_7_terminal.txt: 5000000
bound_7_non_terminal.txt: 5000000
bound_10_terminal.txt: 10000000
bound_10_non_terminal.txt: 10000000
 
For details, see the paper:

"Machine learning detects terminal singularities", Tom Coates, Alexander M. Kasprzyk, and Sara Veneziale. Neural Information Processing Systems (NeurIPS), 2023.
 
Magma code capable of generating this dataset is in the file "terminal_dim_8.m". The bound on the weights is set on line 142 by adjusting the value of 'k' (currently set to 10). The target dimension is set on line 143 by adjusting the value of 'dim' (currently set to 8). It is important to note that this code does not attempt to remove duplicates. The code also does not guarantee that the resulting variety has dimension 8. Deduplication and verification of the dimension need to be done separately, after the data has been generated.
 
If you make use of this data, please cite the above paper and the DOI for this data:
doi:10.5281/zenodo.10046893
DOI: 10.5281/zenodo.10048046
title: CosMx SMI data of one HGSOC tumour reveals ovarian cancer subclones with distinct tumour microenvironments and autocrine circuits

description: High-grade serous ovarian carcinoma (HGSOC) is a polyclonal disease characterised by the presence of subclones with distinct cancer genotypes. This intratumoural heterogeneity is linked to recurrence, chemotherapy resistance, and overall poor prognosis. For one patient sample we used NanoString CosMx Spatial Molecular Imaging (SMI) to analyse the organisation of tumour subclones and the cells that comprise their niche at single cell resolution. This dataset contains the related data and analysis scripts. Our study highlights the high degree of subclonal heterogeneity in HGSOC and that subclone-specific ligand and receptor expression patterns likely modulate how these tumour cells interact with their local microenvironment.
DOI: 10.5281/zenodo.8287971
title: CosMx SMI data of one HGSOC tumour reveals ovarian cancer subclones with distinct tumour microenvironments and autocrine circuits

description: High-grade serous ovarian carcinoma (HGSOC) is a polyclonal disease characterised by the presence of subclones with distinct cancer genotypes. This intratumoural heterogeneity is linked to recurrence, chemotherapy resistance, and overall poor prognosis. For one patient sample we used NanoString CosMx Spatial Molecular Imaging (SMI) to analyse the organisation of tumour subclones and the cells that comprise their niche at single cell resolution. This dataset contains the related data and analysis scripts. Our study highlights the high degree of subclonal heterogeneity in HGSOC and that subclone-specific ligand and receptor expression patterns likely modulate how these tumour cells interact with their local microenvironment.
DOI: 10.5281/zenodo.10048057
title: CosMx SMI data of one HGSOC tumour reveals ovarian cancer subclones with distinct tumour microenvironments and autocrine circuits

description: High-grade serous ovarian carcinoma (HGSOC) is a polyclonal disease characterised by the presence of subclones with distinct cancer genotypes. This intratumoural heterogeneity is linked to recurrence, chemotherapy resistance, and overall poor prognosis. For one patient sample we used NanoString CosMx Spatial Molecular Imaging (SMI) to analyse the organisation of tumour subclones and the cells that comprise their niche at single cell resolution. This dataset contains the related data and analysis scripts. Our study highlights the high degree of subclonal heterogeneity in HGSOC and that subclone-specific ligand and receptor expression patterns likely modulate how these tumour cells interact with their local microenvironment.
DOI: 10.5281/zenodo.10048148
title: Petty Cash: Database + Indicators
DOI: 10.5281/zenodo.10048149
title: Petty Cash: Database + Indicators
DOI: 10.5281/zenodo.8287970
title: CosMx SMI data of one HGSOC tumour reveals ovarian cancer subclones with distinct tumour microenvironments and autocrine circuits

description: High-grade serous ovarian carcinoma (HGSOC) is a polyclonal disease characterised by the presence of subclones with distinct cancer genotypes. This intratumoural heterogeneity is linked to recurrence, chemotherapy resistance, and overall poor prognosis. For one patient sample we used NanoString CosMx Spatial Molecular Imaging (SMI) to analyse the organisation of tumour subclones and the cells that comprise their niche at single cell resolution. This dataset contains the related data and analysis scripts. Our study highlights the high degree of subclonal heterogeneity in HGSOC and that subclone-specific ligand and receptor expression patterns likely modulate how these tumour cells interact with their local microenvironment.
DOI: 10.5281/zenodo.10048164
title: CONIPHER benchmarking
DOI: 10.5281/zenodo.10048165
title: CONIPHER benchmarking