cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17822325
title: Supplemental Materials: The memory scaling of reverse-mode differentiation in particle accelerator simulations with space charge

description: This dataset includes the necessary material to produce the results in the paper "The memory scaling of reverse-mode differentiation in particle accelerator simulations with space charge". This includes several data collection scripts, the data collected using those scripts, and the code used to analize and produce the figures from that data.

The required benchmarking code for Cheetah is included in this archive. This code is based on Cheetah v0.7.4, and includes the changes required for both the memory allocation/trace info and the memory use per section of Space Charge. The official Github repository for Cheetah can be found here: https://github.com/desy-ml/cheetah.

All the simulations were run on Perlmutter at NERSC (LBNL) using the NVIDIA A100 (80GB) GPU partition. A GPU is required for correctly benchmarking memory use in Cheetah.

Archive structure:- `README.md`: Overall description- `data_analysis.py`: The analysis script, responsible for cleaning the raw data in `data/memory_data.csv` and generating the regression fits in `data/analyzed_data.csv`, as well as plotting the fits of each segment and saving those figures into `outputs/`.- `data_collection.py`: The data collection script, responsible of running space charge kick simulations in a variety of configurations and saving the memory usage info in `data/memory_data.csv`- `generate_memory_plots.py`: The memory plot script, responsible for running the accelerator simulation with space charge in differentiable and non-differentiable mode, and saving the side-by-side comparison of the memory usage graphs in `outputs/`- `regression.py`: A utility file that contains the code for the weighted least-squares regression used to calculate the coefficients of the memory use.- `simulation.py`: A utility file that contains the code for running both full accelerator simulations and single space charge kicks and extracting the memory use data.- `analyzed_data.csv`: A csv containing the coefficients of the memory use of each section of space charge kick and their standard deviations.- `memory_data.csv`: A csv containing the data of the memory use of each section of space charge kick in a variety of configurations.- `cheetah_benchmark_B1.ipynb`: The code for the benchmark from Appendix B1.- `cheetah_benchmark_B2.ipynb`: The code for the benchmark from Appendix B2.- `cheetah.tar`: A modified version of Cheetah based on v0.7.4, which includes the changes required for both the memory allocation/trace info and the memory use per section of Space Charge.
DOI: 10.5281/zenodo.17822326
title: Supplemental Materials: The memory scaling of reverse-mode differentiation in particle accelerator simulations with space charge

description: This dataset includes the necessary material to produce the results in the paper "The memory scaling of reverse-mode differentiation in particle accelerator simulations with space charge". This includes several data collection scripts, the data collected using those scripts, and the code used to analize and produce the figures from that data.

The required benchmarking code for Cheetah is included in this archive. This code is based on Cheetah v0.7.4, and includes the changes required for both the memory allocation/trace info and the memory use per section of Space Charge. The official Github repository for Cheetah can be found here: https://github.com/desy-ml/cheetah.

All the simulations were run on Perlmutter at NERSC (LBNL) using the NVIDIA A100 (80GB) GPU partition. A GPU is required for correctly benchmarking memory use in Cheetah.

Archive structure:- `README.md`: Overall description- `data_analysis.py`: The analysis script, responsible for cleaning the raw data in `data/memory_data.csv` and generating the regression fits in `data/analyzed_data.csv`, as well as plotting the fits of each segment and saving those figures into `outputs/`.- `data_collection.py`: The data collection script, responsible of running space charge kick simulations in a variety of configurations and saving the memory usage info in `data/memory_data.csv`- `generate_memory_plots.py`: The memory plot script, responsible for running the accelerator simulation with space charge in differentiable and non-differentiable mode, and saving the side-by-side comparison of the memory usage graphs in `outputs/`- `regression.py`: A utility file that contains the code for the weighted least-squares regression used to calculate the coefficients of the memory use.- `simulation.py`: A utility file that contains the code for running both full accelerator simulations and single space charge kicks and extracting the memory use data.- `analyzed_data.csv`: A csv containing the coefficients of the memory use of each section of space charge kick and their standard deviations.- `memory_data.csv`: A csv containing the data of the memory use of each section of space charge kick in a variety of configurations.- `cheetah_benchmark_B1.ipynb`: The code for the benchmark from Appendix B1.- `cheetah_benchmark_B2.ipynb`: The code for the benchmark from Appendix B2.- `cheetah.tar`: A modified version of Cheetah based on v0.7.4, which includes the changes required for both the memory allocation/trace info and the memory use per section of Space Charge.
DOI: 10.15468/dl.m4y6cu
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Millettia oraria)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 17 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053851-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t5pcf3
title: Occurrence Download

description: A dataset containing 40 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Echium vulcanorum"
 ]
}

 The dataset includes 40 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053855-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4dn74u
title: Occurrence Download

description: A dataset containing 40 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Echium vulcanorum"
 ]
}

 The dataset includes 40 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053854-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15772618
title: Dataset to: Austrian NIR Soil Spectral Library for Soil Health Assessments

description: Dataset description

This is the corresponding dataset to the publication "Austrian NIR Soil Spectral Library for Soil Health Assessments" by Fohrafellner et al. (2025). In this publication, we created the first Austrian Near-Infrared (NIR) Soil Spectral Library (680 – 2500 nm) using 2,129 legacy samples from all environmental zones of Austria. Additionally, we utilized partial least squares regression modeling to evaluate the dataset's current effectiveness for soil health assessments. The dataset contains three tabs, "Document meta data", "Legend" and "Dataset". Tab "Document meta data" gives information on the authors, the data collection time frame, terms of use, etc. In "Legend", each column of the "Dataset" is described. The "Dataset" contains information on the legacy soil samples including: 



meta data (e.g. sample number, sampling year, zip code, environmental zone, land use), 

soil properties (soil organic carbon [SOC], SOC to clay ratio, total carbon, labile carbon, CaCO3, total nitrogen, plant available phosphorus, pH measured in CaCl2 and acetate, cation exchange capacity, texture [sand, silt, clay content], and clay content measured by density in suspension), and

measured NIR soil spectra, also for the standards.


Project description

This Austrian NIR Soil Spectral Library was built within the ProbeField project (November 2021 – January 2025), which was part of the European Joint Program for SOIL "Towards climate-smart sustainable management of agricultural soils" (EJP SOIL) funded by the European Union Horizon 2020 research and innovation programme (Grant Agreement N° 862695). The project aimed to create a protocol detailing procedures and methodologies for accurately estimating fertility-related properties in agricultural soils in the field. Additionally, the potential for extending this data to two- and three-dimensional mapping using co-variates was demonstrated. ProbeField further collected field spectra that closely match laboratory spectra, enabling the prediction of soil properties using models calibrated with soil spectral libraries.

References

Fohrafellner, J., Lippl, M., Bajraktarevic, A., Baumgarten, A., Spiegel, H., Körner, R. and Sandén, T.: Austrian NIR Soil Spectral Library for Soil Health Assessments, Earth System Science Data.
DOI: 10.5281/zenodo.17941270
title: Dataset to: Austrian NIR Soil Spectral Library for Soil Health Assessments

description: Dataset description

This is the corresponding dataset to the publication "Austrian NIR Soil Spectral Library for Soil Health Assessments" by Fohrafellner et al. (2025). In this publication, we created the first Austrian Near-Infrared (NIR) Soil Spectral Library (680 – 2500 nm) using 2,129 legacy samples from all environmental zones of Austria. Additionally, we utilized partial least squares regression modeling to evaluate the dataset's current effectiveness for soil health assessments. The dataset contains three tabs, "Document meta data", "Legend" and "Dataset". Tab "Document meta data" gives information on the authors, the data collection time frame, terms of use, etc. In "Legend", each column of the "Dataset" is described. The "Dataset" contains information on the legacy soil samples including: 



meta data (e.g. sample number, sampling year, zip code, environmental zone, land use), 

soil properties (soil organic carbon [SOC], SOC to clay ratio, total carbon, labile carbon, CaCO3, total nitrogen, plant available phosphorus, pH measured in CaCl2 and acetate, cation exchange capacity, texture [sand, silt, clay content], and clay content measured by density in suspension), and

measured NIR soil spectra, also for the standards.


Project description

This Austrian NIR Soil Spectral Library was built within the ProbeField project (November 2021 – January 2025), which was part of the European Joint Program for SOIL "Towards climate-smart sustainable management of agricultural soils" (EJP SOIL) funded by the European Union Horizon 2020 research and innovation programme (Grant Agreement N° 862695). The project aimed to create a protocol detailing procedures and methodologies for accurately estimating fertility-related properties in agricultural soils in the field. Additionally, the potential for extending this data to two- and three-dimensional mapping using co-variates was demonstrated. ProbeField further collected field spectra that closely match laboratory spectra, enabling the prediction of soil properties using models calibrated with soil spectral libraries.

References

Fohrafellner, J., Lippl, M., Bajraktarevic, A., Baumgarten, A., Spiegel, H., Körner, R. and Sandén, T.: Austrian NIR Soil Spectral Library for Soil Health Assessments, Earth System Science Data.
DOI: 10.5683/sp3/lpoexi
title: Provincial Input-Output Tables, Final Demand Level S: Other, 1997-2008 [Canada]
title: Tableaux symétriques d'entrées-sorties provinciaux, Demande finale niveau S: autre, 1997-2008 [Canada]

description: &lt;b&gt;Provincial Input-Output Tables (15F0042XDB)&lt;/b&gt;&lt;/br&gt;

&lt;p&gt;The Input-Output accounting system consists of three tables. The input tables (USE tables) detail the commodities that are consumed by various industries. Output tables (MAKE tables) detail the commodities that are produced by various industries. Final demand tables detail the commodities bought by many categories of buyers (consumers, industries, non-profit institutions and government) for both consumption and investment purposes. These tables allow users to track intersectoral exchanges of goods and services between industries and final demand categories such as personal expenditures, capital expenditures and public sector expenditures.&lt;/p&gt;

&lt;p&gt;The provincial input-output tables are constructed every year starting with the 1997 tables. The tables are available at the “Summary” level with 35 industries, 74 commodities and 25 final demand categories.&lt;/p&gt;
description: With the 2015 comprehensive revision, the Canadian System of Macroeconomic Accounts (CSMA) has introduced a major presentational change to the national and the provincial and territorial input-output tables. The previous CSMA input-output presentation differed from the international standard and the practice found in most national statistical organizations. The CSMA has aligned its presentation with the international standard and replaces the presentation found in catalogues 15F0041X and 15F0042X. The new presentation can be found in the product: Supply and Use Tables (15-602-X).
description: Input-Output Tables
DOI: 10.15468/dl.3psf73
title: Occurrence Download

description: A dataset containing 50 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Occurrence evidence, Living Specimen, Material sample, Specimen, Fossil, Human Observation, Observation, Machine Observation, Material citation)",
 "Country is Nepal",
 "OccurrenceStatus is Present",
 "TaxonKey is Acacia catechu"
 ]
}

 The dataset includes 50 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053856-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccdc.csd.cc2l511w
title: CSD 2388117: Experimental Crystal Structure Determination

description: Related Article: Magdalena M Lozinska, Elliott L Bruce, Veselina M. Georgieva, Elizabeth M Feverston, Shubhra J Bhadra, Garret C Lau, Timothy C Golden, Erin M Sorensen, Roger D Whitley, Ronald I Smith, Paul A Wright and William J Casteel Jr|2025|Microporous and Mesoporous Materials|387|113502|doi:10.1016/j.micromeso.2025.113502
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2l512x
title: CSD 2388118: Experimental Crystal Structure Determination

description: Related Article: Magdalena M Lozinska, Elliott L Bruce, Veselina M. Georgieva, Elizabeth M Feverston, Shubhra J Bhadra, Garret C Lau, Timothy C Golden, Erin M Sorensen, Roger D Whitley, Ronald I Smith, Paul A Wright and William J Casteel Jr|2025|Microporous and Mesoporous Materials|387|113502|doi:10.1016/j.micromeso.2025.113502
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2l513y
title: CSD 2388119: Experimental Crystal Structure Determination

description: Related Article: Magdalena M Lozinska, Elliott L Bruce, Veselina M. Georgieva, Elizabeth M Feverston, Shubhra J Bhadra, Garret C Lau, Timothy C Golden, Erin M Sorensen, Roger D Whitley, Ronald I Smith, Paul A Wright and William J Casteel Jr|2025|Microporous and Mesoporous Materials|387|113502|doi:10.1016/j.micromeso.2025.113502
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2l5172
title: CSD 2388123: Experimental Crystal Structure Determination

description: Related Article: Magdalena M Lozinska, Elliott L Bruce, Veselina M. Georgieva, Elizabeth M Feverston, Shubhra J Bhadra, Garret C Lau, Timothy C Golden, Erin M Sorensen, Roger D Whitley, Ronald I Smith, Paul A Wright and William J Casteel Jr|2025|Microporous and Mesoporous Materials|387|113502|doi:10.1016/j.micromeso.2025.113502
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2l53tq
title: CSD 2388203: Experimental Crystal Structure Determination

description: Related Article: Magdalena M Lozinska, Ruxandra G Chitac, Elliott L Bruce, Yuanyuan Du, Malavika Manoj, Daniel M Dawson, Sharon E Ashbrook, Paul A Cox, Paul A Wright|2025|Chem.-Eur.J.|31|e202500327|doi:10.1002/chem.202500327
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.fs4889
title: Occurrence Download

description: A dataset containing 460 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Millettia pulchra)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 460 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053857-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p9s1mi9l
title: GIS Data for Reconnaissance Geologic Map of the Hells Canyon Study Area, Wallowa County, Oregon, and Idaho and Adams Counties, Idaho

description: This data release for the reconnaissance geologic map of the Hells Canyon Study Area, Wallowa County, Oregon, and Idaho and Adams Counties, Idaho, is a Geologic Map Schema (GeMS, 2020)-compliant version of the geologic map published in U.S. Geological Survey (USGS) Scientific Investigations Report Map SIR 2007-5046 (Simmons, et al, 2007).
          
The database represents the geology for the 625,177-acre (2,530 square kilometers), geologically complex Hells Canyon Study Area in two plates, at a publication scale of 1:48,000. The study area includes (1) the Hells Canyon Wilderness; (2) parts of the Snake River, Rapid River, and West Fork Rapid River Wild and Scenic Rivers; (3) lands included in the second Roadless Area Review and Evaluation (RARE II); and (4) part of the Hells Canyon National Recreation Area.

References: Simmons, G.C., Gualtieri, J.L., Close, T.J., Federspiel, F.E., and Leszcykowski, A.M., 2007, Mineral resources of the Hells Canyon study area, Wallowa County, Oregon, and Idaho and Adams Counties, Idaho, with a section on aeromagnetic and gravity surveys, by D.R. Mabey: U.S. Geological Survey Scientific Investigations Report 2007-5046, 62 p., https://pubs.usgs.gov/sir/2007/5046/.
U.S. Geological Survey National Cooperative Geologic Mapping Program, 2020, GeMS (Geologic Map Schema) - A standard format for the digital publication of geologic maps: U.S. Geological Survey Techniques and Methods, book 11, chap. B10, 74 p., https://doi.org//10.3133/tm11B10.
DOI: 10.15468/dl.gtknw3
title: Occurrence Download

description: A dataset containing 11747 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Rupicola peruvianus"
 ]
}

 The dataset includes 11747 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053858-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e9kv7g
title: Occurrence Download

description: A dataset containing 2193 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Tropidacris collaris"
 ]
}

 The dataset includes 2193 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053859-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14809230
title: Gestalt recognition: beyond basic patterns - descriptors

description: With the aim of extending the descriptive palette for images, and in particular for patterns, we analysed part of the Gold Fyfe dataset (DOI: 10.5281/zenodo.11032266) following three different strategies. The difference between them is the grade of relatability from a human perspective: the Gestalt Classifier (focused in human kinaesthetic ability to enagage with images), Basic Classifier (retaining aspects of computer and human vision) and traditional image analysis descriptors (computer vision). The classifiers were generated with supervised machine learning, the traditional image analysis descriptors (i.e. Traditional descriptors) were calculated using Fiji ImageJ.

The three data files contains the outcome values for the corresponding image analysis strategy.
DOI: 10.5281/zenodo.17609948
title: Gestalt recognition: beyond basic patterns - descriptors

description: With the aim of extending the descriptive palette for images, and in particular for patterns, we analysed part of the Gold Fyfe dataset (DOI: 10.5281/zenodo.11032266) following three different strategies. The difference between them is the grade of relatability from a human perspective: the Gestalt Classifier (focused in human kinaesthetic ability to enagage with images), Basic Classifier (retaining aspects of computer and human vision) and traditional image analysis descriptors (computer vision). The classifiers were generated with supervised machine learning, the traditional image analysis descriptors (i.e. Traditional descriptors) were calculated using Fiji ImageJ.

The three data files contains the outcome values for the corresponding image analysis strategy.
DOI: 10.34740/kaggle/dsv/14166936
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.17605/osf.io/g86m7
title: Data and R Code Belonging to the Publication "A Test to Assess Teachers’ Knowledge of How to Operate Technology"

description: Here, you will find the data used for the development (e.g., validation) of the T-TK test.