cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13268784
title: Profiling results for top pair plus jets production with Pepper

description: Profiling results for top pair plus jets production with Pepper v1.1.1

Contents

This includes data/output for the following GPU accelerated unweighted event generation runs:



A run over a few seconds to produce pepper-internal timing results (`*timers.csv` files)

The same run, but with `nvprof` (`*.nvprof` files)

A run with a single event batch, with `ncu --import-source on --set full` (`*.ncu-rep` files)


The simulated process is top pair production with n jets, with n = 0...4, at 13 TeV. The configuration files (`pepper.ini`) are included. Also the `pepper_cache` directories are included.

All results are for the "main" pepper variant (which uses Kokkos to utilise the GPU) and for the "native" pepper variant (which uses CUDA directly).

Software stack and CPU/GPU hardware details

The software/hardware used for the profiling are as follows:



GPU Driver: NVIDIA-SMI: 550.100, Driver Version: 550.100, CUDA Version: 12.4

GPU: Tesla V100S-PCIE-32GB

Cuda compilation tools: release 11.6, V11.6.124

Kokkos 4.3.01

gcc (GCC) 11.4.1 20231218 (Red Hat 11.4.1-3)

Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz


Note that the event generation includes write-out of event files. The LHEH5 files are written to local SSD storage. The event files are not included in this dataset.

Additional run parameters




TTBar
number of batches
batch size
rate (main variant)
rate (native variant)


+0j
20
1,179,648
1.4e10
2.0e10


+1j
20
1,179,648
1.5e10
2.4e10


+2j
20
1,179,648 * 2
1.3e10
2.7e10


+3j
20
1,179,648 / 2
6.4e9
1.4e10


+4j
20
1,179,648
2.2e9
2.2e9




The number of batches has been chosen such that the most important sub processes are samples and such that the overall runtime is at least a few seconds. The batch size has been chosen by scanning over factors of two and picking the best-performing batch size with the native variant. The event rate is the number given in the Pepper output. It does not include the closing time of the generated HDF5 file, which can be significant for the two lowest multiplicities. This can be checked by inspecting the corresponding relative and absolute timing results in the `*timing.csv` files included in the dataset.
DOI: 10.5281/zenodo.13268785
title: Profiling results for top pair plus jets production with Pepper v1.1.1

description: Profiling results for top pair plus jets production with Pepper v1.1.1

Contents

This includes data/output for the following GPU accelerated unweighted event generation runs:



A run over a few seconds to produce pepper-internal timing results (`*timers.csv` files)

The same run, but with `nvprof` (`*.nvprof` files)

A run with a single event batch, with `ncu --import-source on --set full` (`*.ncu-rep` files)


The simulated process is top pair production with n jets, with n = 0...4, at 13 TeV. The configuration files (`pepper.ini`) are included. Also the `pepper_cache` directories are included.

All results are for the "main" pepper variant (which uses Kokkos to utilise the GPU) and for the "native" pepper variant (which uses CUDA directly).

Software stack and CPU/GPU hardware details

The software/hardware used for the profiling are as follows:



GPU Driver: NVIDIA-SMI: 550.100, Driver Version: 550.100, CUDA Version: 12.4

GPU: Tesla V100S-PCIE-32GB

Cuda compilation tools: release 11.6, V11.6.124

Kokkos 4.3.01

gcc (GCC) 11.4.1 20231218 (Red Hat 11.4.1-3)

Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz


Note that the event generation includes write-out of event files. The LHEH5 files are written to local SSD storage. The event files are not included in this dataset.

Additional run parameters




TTBar
number of batches
batch size
rate (main variant)
rate (native variant)


+0j
20
1,179,648
1.4e10
2.0e10


+1j
20
1,179,648
1.5e10
2.4e10


+2j
20
1,179,648 * 2
1.3e10
2.7e10


+3j
20
1,179,648 / 2
6.4e9
1.4e10


+4j
20
1,179,648
2.2e9
2.2e9




The number of batches has been chosen such that the most important sub processes are likely to be sampled and such that the overall runtime is at least a few seconds. The batch size has been chosen by scanning over factors of two and picking the best-performing batch size with the native variant. The event rate is the number given in the Pepper output. It does not include the closing time of the generated HDF5 file, which can be significant for the two lowest multiplicities. This can be checked by inspecting the corresponding relative and absolute timing results in the `*timing.csv` files included in the dataset.
DOI: 10.5281/zenodo.13283966
title: Profiling results for top pair plus jets production with Pepper

description: Profiling results for top pair plus jets production with Pepper v1.1.1

Contents

This includes data/output for the following GPU accelerated unweighted event generation runs:



A run over a few seconds to produce pepper-internal timing results (`*timers.csv` files)

The same run, but with `nvprof` (`*.nvprof` files)

A run with a single event batch, with `ncu --import-source on --set full` (`*.ncu-rep` files)


The simulated process is top pair production with n jets, with n = 0...4, at 13 TeV. The configuration files (`pepper.ini`) are included. Also the `pepper_cache` directories are included.

All results are for the "main" pepper variant (which uses Kokkos to utilise the GPU) and for the "native" pepper variant (which uses CUDA directly).

Software stack and CPU/GPU hardware details

The software/hardware used for the profiling are as follows:



GPU Driver: NVIDIA-SMI: 550.100, Driver Version: 550.100, CUDA Version: 12.4

GPU: Tesla V100S-PCIE-32GB

Cuda compilation tools: release 11.6, V11.6.124

Kokkos 4.3.01

gcc (GCC) 11.4.1 20231218 (Red Hat 11.4.1-3)

Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz


Note that the event generation includes write-out of event files. The LHEH5 files are written to local SSD storage. The event files are not included in this dataset.

Additional run parameters




TTBar
number of batches
batch size
rate (main variant)
rate (native variant)


+0j
20
1,179,648
1.4e10
2.0e10


+1j
20
1,179,648
1.5e10
2.4e10


+2j
20
1,179,648 * 2
1.3e10
2.7e10


+3j
20
1,179,648 / 2
6.4e9
1.4e10


+4j
20
1,179,648
2.2e9
2.2e9




The number of batches has been chosen such that the most important sub processes are samples and such that the overall runtime is at least a few seconds. The batch size has been chosen by scanning over factors of two and picking the best-performing batch size with the native variant. The event rate is the number given in the Pepper output. It does not include the closing time of the generated HDF5 file, which can be significant for the two lowest multiplicities. This can be checked by inspecting the corresponding relative and absolute timing results in the `*timing.csv` files included in the dataset.
DOI: 10.15468/dl.tm5egy
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((149.303754 -33.303485, 149.303754 -33.307977, 149.306 -33.307977, 149.306 -33.303485, 149.303754 -33.303485))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057858-240626123714530/datasets/export for details.
DOI: 10.15468/dl.avdnsb
title: Occurrence Download

description: A dataset containing 144 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "GadmGid is CHN.4_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Aix galericulata (Linnaeus, 1758)"
 ]
}

 The dataset includes 144 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057859-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ftydak
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON (((148.40768 -33.307977, 148.40768 -33.310223, 148.412172 -33.310223, 148.412172 -33.307977, 148.40768 -33.307977)), ((148.412172 -33.310223, 148.412172 -33.312469, 148.414418 -33.312469, 148.414418 -33.310223, 148.412172 -33.310223)))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057860-240626123714530/datasets/export for details.
DOI: 10.15468/dl.0pvp0n
title: Occurrence Download

description: A dataset containing 215 species occurrences available in GBIF matching the query:
{
 "or" : [
 "TaxonKey is Amblyopyrum muticum (Boiss.) Eig",
 "TaxonKey is Amblyopyrum muticum var. muticum",
 "TaxonKey is Amblyopyrum muticum var. loliaceum (Jaub. & Spach) Eig"
 ]
}

 The dataset includes 215 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016476-180131172636756/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.enlx9c
title: Occurrence Download

description: A dataset containing 61754 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Country is France",
 "Country is Spain",
 "Country is Israel"
 ]
 },
 "HasCoordinate is true",
 "TaxonKey is Hordeum L.",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 61754 records from 89 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004518-171219132708484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z70qav
title: Occurrence Download

description: A dataset containing 34814 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Country is Spain",
 "Country is France",
 "Country is Türkiye",
 "Country is Israel",
 "Country is Greece",
 "Country is Syrian Arab Republic",
 "Country is Jordan",
 "Country is Iran (Islamic Republic of)"
 ]
 },
 "HasCoordinate is true",
 "TaxonKey is Aegilops L.",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 34814 records from 95 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004517-171219132708484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21348/b.0001
title: NORSAR Seismic Bulletins
DOI: 10.5281/zenodo.11422081
title: Likelihoods for the CTA sensitivity to a dark matter line signal from the Galactic centre (S. Abe et al., 2024])

description: We present likelihoods to estimate upper limits and detection prospects for monochromatic gamma-ray signals from DM pair-annihilation in the Galactic centre, based on the Cherenkov Telescope Array (CTA) consortium publication "Dark Matter Line Searches with the Cherenkov Telescope Array" by S. Abe et al. [arxiv:2403.04857]. These likelihoods are based on the analysis benchmark assumptions summarized in Table 3 of that publication. In particular, they assume an Einasto profile for the DM density distribution and 500 hrs of Galactic centre observations. We further supply likelihoods for the same analysis benchmark assumptions, but for an Einasto profile with an inner core of size 1 kpc.

As indicated by the filenames, the four files contain likelihoods for setting limits and claiming discovery, respectively. For more technical details, see the headers of these files.
DOI: 10.5281/zenodo.11510586
title: Numerical Investigation of Observational Flux Partitioning Methods for Water Vapor and Carbon Dioxide
DOI: 10.5281/zenodo.5848494
title: Candidate compounds from the design of covalent Bruton's tyrosine kinase (BTK) inhibitors via focused deep generative modeling

description: <pre>A total of 1491 candidate inhibitors for covalent inhibition of BTK are deposited as SMILES strings together with 34 known covalent BTK inhibitors used to guide generative computational design. The study will be reported in a publication in Molecules under the authors' names.</pre>
DOI: 10.5281/zenodo.8404628
title: INAGEOID2020: A Vertical Datum in Indonesia

description: This repository contains the tabular data of InaGEOID2020 with 0.01 degree grid spacing. 

The data format is Longitude (deg) Latitude (deg) Undulation (m).

Please refer to:

Pahlevi, A., Syafarianty, A., Susilo, S. et al. Geoid Undulation Model as Vertical Reference in Indonesia. Sci Data 11, 822 (2024). https://doi.org/10.1038/s41597-024-03646-w

This research funded by DIPA BIG TAHUN ANGGARAN 2024 NOMOR: SP DIPA-083.01.1.017216/2024
DOI: 10.6084/m9.figshare.24313015
title: Data for article "Natural structural transition of gas hydrates from sI to sII in the deep seafloor"

description: raw data includes in situ Raman spectra of Haima cold seep, newly exposed gas hydrates and exposed gas hydrates.
DOI: 10.6084/m9.figshare.25931452
title: Supplementary Material for: Comparison of breast fine-needle aspiration cytology and tissue sampling for high-throughput proteomic analysis and cancer biomarker detection

description: Background: Fine-needle aspiration cytology (FNAC) specimens are widely utilized for the diagnosis and molecular testing of various cancers. We performed a comparative proteomic analysis of three different sample types, including breast fine-needle aspiration cytology (FNAC), core needle biopsy (CNB), and surgically resection tissues. Our goal was to evaluate the suitability of FNAC for in-depth proteomic analysis and for identifying potential therapeutic biomarkers in breast cancer. 
Methods: High-throughput proteomic analysis was conducted on matched FNAC, CNB, and surgical resection tissue samples obtained from breast cancer patients. The protein identification, including currently established or promising therapeutic targets, was compared among the three different sample types. Gene Ontology (GO) enrichment analysis was also performed on all matched samples.
Results: Compared to tissue samples, FNAC testing revealed a comparable number of proteins (7,179 in FNAC; 7,196 in CNB; 7,190 in resection samples). Around 85% of proteins were mutually identified in all sample types. FNAC along with CNB showed a positive correlation between the number of enrolled tumor cells and identified proteins. In the GO analysis, the FNAC samples demonstrated a higher number of genes for each pathway and GO terms than tissue samples. CCND1, CDK6, HER2, and IGF1R were found in higher quantities in the FNAC compared to tissue samples, while TUBB2A was only detected in the former.
Conclusions: FNAC is suitable for high-throughput proteomic analysis, in addition to an emerging source that could be used to identify and quantify novel cancer biomarkers.
DOI: 10.15468/dl.p2v2qs
title: Occurrence Download

description: A dataset containing 4069948 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mergus merganser Linnaeus, 1758",
 "HasCoordinate is true",
 "Year is greater than or equal to 2011",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 4069948 records from 454 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057854-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.393r3u
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "OccurrenceStatus is Present",
 "TaxonKey is Fusarium aquaeductuum (Radlk. & Rabenh.) Lagerh. & Rabenh.",
 "Year 2000-2024"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057861-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25318/3210040801-eng
title: Tillage and seeding practices, Census of Agriculture, inactive

description: Census of Agriculture, 2011 and 2016. Tillage and seeding practices used on land seeded or to be seeded.
DOI: 10.26093/cds/vizier.36140071
title: SNF20080514-002 and LSQ12fxd spectra

description: VizieR online Data Catalogue associated with article published in journal Astronomy & Astrophysics with title 'Understanding Type Ia supernovae through their U-band spectra.' (bibcode: 2018A&A...614A..71N)
DOI: 10.5066/p14fnxdv
title: Upper Skagit Reservoir Food Web Data, 2005-2021

description: This release includes data used to evaluate the structure and function of the Ross Lake and Diablo Lake food webs. This includes data on zooplankton density and production (zooplankton_density.csv and daphnia_region_production_biomass.csv), lake volume estimates used to expand zooplankton density and production data (lake_volume_estimates.csv), fish sampling (FishSampleEvents.csv), fish biological information including diets, age, and stable isotope analysis (FishFullData_formatted.csv, FishPreyLength.csv), scale back-calculations (salmonid_back_calc.csv, rss_back_calc.csv), fish energy density (calorimetry_processed.csv), stable isotope data for invertebrate end members (si_inverts.csv), and hydroacoustic sampling (ross_ha_densities.csv, diablo_ha_densities.csv). Fish and zooplankton sampling and stable isotope analysis were conducted by the USGS in 2019-2021, and hydroacoustics surveys were conducted in 2021. These data were also combined with fish data (species, size, age) from the National Park Service collected in 2005-2017 to evaluate fish size and age structure across a larger period of time. The bioenergetics model and information on running the model including inputs and outputs from our simulations and a user guide can be found the in FB4.zip folder. Please refer to the associated publications for additional details about data collection and processing.
DOI: 10.5067/adawh64dcru0
title: inst6_3d_ana_Np: MERRA 3D Analyzed State, Meteorology Instantaneous 6-hourly (p-coord, 2/3x1/2L42) Version 5.2.0
DOI: 10.5067/hnga0eww0r09
title: MERRA-2 inst3_2d_gas_Nx: 2d,3-Hourly,Instantaneous,Single-Level,Assimilation,Aerosol Optical Depth Analysis V5.12.4
DOI: 10.5067/seabass/arctic_rswq/data001
title: Arctic_RSWQ
DOI: 10.5281/zenodo.10308565
title: Dung beetles drive direct and indirect changes in ecosystem multifunctionality

description: This project was funded by the Australian Department of Agriculture, Water and the Environment and Meat and Livestock Australia through Research through the Dung Beetles as Ecosystem Engineers (DBEE) research programme (2018–2022) managed by Charles Sturt University (grant agreement no: RnD4Profit-16-03-016).
DOI: 10.5281/zenodo.11064756
title: Crop exposure Switzerland (2021)

description: "Crop exposure for Switzerland in 2021 obtained from geoddienste.ch.
Including a crop mask, the number of fields and the total crop area at 1, 2, 4, and 8 km resolution for the following crop types:
winter wheat, maize (incl. silage and forage maize), winter barley, rapeseed, and grapevine as well as an aggregate crop class field crops (incl. wheat, maize, barley, and rapeseed).
The dataset can be used for any kind of risk assessment studies for these crops in Switzerland.
DOI: 10.5281/zenodo.11064767
title: Reported hail damage for 12 hail events in Switzerland

description: Hail damage to crops for 10-12 hail days (depending on the crop) between 2017 and 2021 from the Swiss Hail insurance company
including the number of damaged fields at 1, 2, 4, and 8 km resolution for the following crop types:
winter wheat, maize (incl. silage and forage maize), winter barley, rapeseed, and grapevine as well as an aggregate crop class field crops
(incl. wheat, maize, barley, and rapeseed).
The dataset can be used to calibrate and verify crop hail damage models.
DOI: 10.5281/zenodo.11064781
title: Radar data for 12 hail events in Switzerland

description: Radar-based daily hail hazard data at 1km spatial resolution for 12 hail days in Switzerland between 2017 and 2021
provided by the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss).
Included dates are the ones where hail damage information is available from 
"Reported hail damage for 12 hail events in Switzerland"
(YYYY-MM-DD): 2017-06-27, 2017-07-08, 2017-08-01, 2019-06-15, 2019-06-30, 2019-07-01, 2021-06-20, 2021-06-21, 2021-06-28, 2021-07-12, 2021-07-13, 2021-07-24.
The dataset contains two variables based on single-polarization radar data: the Maximum Expected Severe Hail Size (MESHS) and the Probability of Hail (POH).
DOI: 10.5281/zenodo.11088101
title: DARDAR 1˚x1˚ gridded statistics of cloud fraction by cloud phase type over the Arctic for the period 2007 - 2010
title: The correlation between Arctic sea ice, cloud phase and radiation using A-train satellites
title: Arctic, cloud phase, climate, CALIPSO, CloudSat

description: Description

These files, further described below, contain the necessary DARDAR variables to reproduce the figures and tables of Cesana et al. (2024), which are not publically available. The cloud phase statistics have been processed over the Arctic only (60˚N to 82˚N) for the years 2007 to 2010, and accumulated onto a 1˚x1˚ spatial grid, using the DARDAR instantaneous files (Delanoe and Logan, 2010).

dardar_grid_2D_2007-2010.nc: This file contains the monthly mean total, ice, liquid and mixed 2D cloud covers (clt, clt_ice, clt_water and clt_mixed, respectively), as well as the total number of profiles per grid box (total_profiles), the latitude and longitude (lat, lon) and the time.

dardar_grid_3D_2007-2010.nc: This file contains the same variables as the 2D files except with the altitude dimension, over 40 480m altitude bins, from 0 to 19.2 km. Alt corresponds to the mid-level altitude of each altitude bins.

 

References

Cesana, G.V., O. Pierpaoli, M. Ottaviani, L. Vu, Z. Jin and I. Silber, 2024: The correlation between Arctic sea ice, cloud phase and radiation using A-train satellites. Atmos. Chem. Phys., accepted.

Delanoe, J. and Hogan, R. J., 2010: Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds, Journal of Geophysical Research: Atmospheres, 115, https://doi.org/https://doi.org/10.1029/2009JD012346.
DOI: 10.5281/zenodo.4675053
title: Spectrum data for calculation of biological effectiveness of proton beams

description: Datasets used in Bellinzona, E.V.; Grzanka, L.; Attili, A.; Tommasino, F.; Friedrich, T.; Krämer, M.; Scholz, M.; Battistoni, G.; Embriaco, A.; Chiappara, D.; Cirrone, G.A.P.; Petringa, G.; Durante, M.; Scifoni, E. Biological Impact of Target Fragments on Proton Treatment Plans: An Analysis Based on the Current Cross-Section Data and a Full Mixed Field Approach. <em>Cancers</em> <strong>2021</strong>, <em>13</em>, 4768. https://doi.org/10.3390/cancers13194768
DOI: 10.5517/ccdc.csd.cc1mgkwj
title: CCDC 1503558: Experimental Crystal Structure Determination

description: Related Article: Ulrich Flörke, Ahmed Boshaala|2016|CSD Communication|||