cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/5d2b9mt5gp.2
title: Automated multi-dimensional dynamic planning algorithm for solving energy management problems in fuel cell electric vehicles

description: 1. The AuDP algorithm does not require any hyperparameter adjustment and can meet various constraints.
You can compute the optimal control results under different gas purge time constraints by adjusting the variable [OFF2ON] below.

2. The battery model integrates the SOP algorithm recommended by the battery supplier and includes a simple battery thermal model.
However, we currently do not have permission to disclose the SOP data, so parts of the code involving battery data have been encapsulated.
The open-source code is sufficient to demonstrate most of the details of AuDP.
In the future, we will seek publicly available SOP data and open-source all the code.
/*You can find the SOP algorithm details in [+Pseudocode/Cal_SOC].*/

3. It is recommended to read the AuDP files in the following order:
+AuDP_Function/AuDP.m
+AuDP_Function/AuDP_Single_Step.m
+AuDP_Function/Mode_Masking.m
+AuDP_Function/Start_Mode.m
+AuDP_Function/Work_Mode.m
+AuDP_Function/Close_Mode.m
+AuDP_Function/EV_Mode.m
+AuDP_Function/SOC_Masking.m
+AuDP_Function/Data_Merge.m
+AuDP_Function/Result_Display.m

4. Running [main.m] to get the result of the calculation.
You can also directly read the data in [+Data_Storage] to view the calculation results,
and use [+AuDP_Function/Result_Display] to visualize the data.

5. This code was developed using Matlab 2022b, so it is recommended to run it in Matlab 2022b to avoid potential errors.
On a laptop configured with a 12th Gen Intel(R) Core(TM) i9-12900HX 2.30 GHz CPU, 32.0 GB RAM, and RTX3080Ti,
the runtime of this code is approximately 185–195 seconds.
Note that different Matlab versions and laptop configurations may result in varying computation times, which is normal.

If you use or refer to this code, it is recommended that you cite our paper:
Wang KY, Song H, Guo ZQ, Zhang XM. Automated multi-dimensional dynamic planning algorithm for solving energy management problems in fuel cell electric vehicles. Energy 2025;316:134408. https://doi.org/10.1016/j.energy.2025.134408
DOI: 10.6084/m9.figshare.28868390
title: leukemia 1

description: Classifying Acute Lymphoblastic Leukemia cells from normal cells using deep learning method
DOI: 10.6084/m9.figshare.28868390.v1
title: leukemia 1

description: Classifying Acute Lymphoblastic Leukemia cells from normal cells using deep learning method
DOI: 10.17632/v7dtynj5yv.1
title: Master work "The influence of the level of digital transformation on a company's value"

description: Первый критерий – рассмотрение только тех компаний, чьи акции публично торгуются на российской бирже; 
Второй критерий – основным рынком для проведения апробации выдвинутой гипотезы и проведения расчетов является российский;
Третий критерий – расчет индекса цифровой трансформации и цифровизации проводится для каждой компании ежегодно с января 2012 г. по декабрь 2023 г. (12 полных лет).
DOI: 10.15468/dl.k2xeyf
title: Occurrence Download

description: A dataset containing 1141 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Mystic Aquarium's marine mammal and sea turtle stranding data 1976-2011"
 ]
}

 The dataset includes 1141 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001423-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n8yeje
title: Occurrence Download

description: A dataset containing 3459 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Nyctinomops laticaudatus (E.Geoffroy, 1805)"
 ]
}

 The dataset includes 3459 records from 97 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001422-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/j9w36k8b4j.1
title: Финансирование инновационных компаний с учетом их характеристик на разных этапах жизненного цикла_dataset

description: В результате формирования выборки из проведенных сделок на российском рынке за период с 2019 г. по 2024 г. в рамках финансирования инновационных компаний на разных этапах жизненного цикла, а также использования регрессионного анализа с участием интегрального показателя в виде размера финансирования и несколькими объясняющими переменными было установлено, что наиболее значимыми факторами являются: число основателей, отношение к ИТ-сектору, размер выручки, ситуация на фондовом рынке, участие венчурных фондов на прошлых раундах финансирования (значимость данных факторов варьируются исходя из этапа жизненного цикла).
Полученные методологические и прикладные результаты могут быть использованы инновационными компаниями в дальнейшем в целях формирования стратегий по привлечению финансирования в создание, внедрение и последующее развитие инновационных товаров и услуг.
DOI: 10.5281/zenodo.15281538
title: 2024 Utrecht University Open Access Monitor (peer reviewed journal articles)

description: Results of the OA monitor of Utrecht University (UU) and University Medical Center Utrecht(UMCU) for the year 2024. It lists the open access availability of all peer reviewed journal articles registered in the CRIS (Pure) of Utrecht University and/or University Medical Center Utrecht.
DOI: 10.5281/zenodo.15281539
title: 2024 Utrecht University Open Access Monitor (peer reviewed journal articles)

description: Results of the OA monitor of Utrecht University (UU) and University Medical Center Utrecht(UMCU) for the year 2024. It lists the open access availability of all peer reviewed journal articles registered in the CRIS (Pure) of Utrecht University and/or University Medical Center Utrecht.
DOI: 10.5281/zenodo.16908015
title: Raw Dataset for 2024–2025 Fantale–Dofen Rifting Episodes

description: This dataset contains the raw data used to quantify the 2024–2025 Fantale–Dofen rifting episodes.

## Included files

•     SAR_T87*.zip, SAR_T79*.zip:

Zip archives of SAR products from ascending track T87 and descending track T79 covering four episodes.

Each archive includes:

los_disp*.grd: Line-of-sight (LOS) displacement grids (unit: meters). Positive values indicate displacement from the ground toward the satellite.

range_offset*.grd and azimuth_offset*.grd: Pixel-offset displacement maps in the range and azimuth directions (unit: meters).

look_angle*.grd and azimuth_angle*.grd: Look-angle and azimuth-angle grids (unit: degrees).

•     T87_*_LOS.csv, T79_*_LOS.csv

Downsampled LOS displacement results used in modeling for tracks T87 and T79 across four episodes.

•     model_file.zip

Zip archive of model input files used in magma source modeling:

dike_model_*.dat: Dike model files.

fault_model_*.dat: Fault model files.

mogi_model.dat: Mogi source model file.

•     catalog_usgs.csv

Earthquake catalog file obtained from the USGS catalog and used in this study.
DOI: 10.5281/zenodo.16908016
title: Raw Dataset for 2024–2025 Fantale–Dofen Rifting Episodes

description: This dataset contains the raw data used to quantify the 2024–2025 Fantale–Dofen rifting episodes.

## Included files

•     SAR_T87*.zip, SAR_T79*.zip:

Zip archives of SAR products from ascending track T87 and descending track T79 covering four episodes.

Each archive includes:

los_disp*.grd: Line-of-sight (LOS) displacement grids (unit: meters). Positive values indicate displacement from the ground toward the satellite.

range_offset*.grd and azimuth_offset*.grd: Pixel-offset displacement maps in the range and azimuth directions (unit: meters).

look_angle*.grd and azimuth_angle*.grd: Look-angle and azimuth-angle grids (unit: degrees).

•     T87_*_LOS.csv, T79_*_LOS.csv

Downsampled LOS displacement results used in modeling for tracks T87 and T79 across four episodes.

•     model_file.zip

Zip archive of model input files used in magma source modeling:

dike_model_*.dat: Dike model files.

fault_model_*.dat: Fault model files.

mogi_model.dat: Mogi source model file.

•     catalog_usgs.csv

Earthquake catalog file obtained from the USGS catalog and used in this study.
DOI: 10.15468/dl.4puwjj
title: Occurrence Download

description: A dataset containing 41 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation)",
 "Continent is Asia",
 "Country is Nepal",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Felis chaus",
 "Year 2000-2026"
 ]
}

 The dataset includes 41 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004710-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j4y7up
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.74391 24.46051, 88.66676699999999 24.454933, 88.591542 24.438341, 88.52010799999999 24.411146, 88.454235 24.374024, 88.395557 24.327898, 88.345519 24.273913, 88.305351 24.213406, 88.276032 24.147875, 88.25827 24.07894, 88.252486 24.008302, 88.25879999999999 23.937703, 88.27704 23.868878, 88.306738 23.803518, 88.34715 23.743227, 88.397272 23.689481, 88.455867 23.643594, 88.521495 23.606689, 88.59255 23.579665, 88.667297 23.563183, 88.74391 23.557644, 88.82052299999999 23.563183, 88.89527 23.579665, 88.966325 23.606689, 89.031953 23.643594, 89.090548 23.689481, 89.14067 23.743227, 89.181082 23.803518, 89.21078 23.868878, 89.22902000000001 23.937703, 89.23533399999999 24.008302, 89.22955 24.07894, 89.211788 24.147875, 89.182469 24.213406, 89.142301 24.273913, 89.092263 24.327898, 89.033585 24.374024, 88.96771200000001 24.411146, 88.896278 24.438341, 88.82105300000001 24.454933, 88.74391 24.46051))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 12 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004709-260120142942310/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.31081396
title: Data and R-Script for "Emergent trees Peru beetles"

description: Data for MS "Importance of emergent trees for beetle conservation in a lowland rainforest in Peru" for peer review
DOI: 10.6084/m9.figshare.31081396.v1
title: Data for "Emergent trees Peru beetles"

description: Data for MS "Importance of emergent trees for beetle conservation in a lowland rainforest in Peru" for peer review
DOI: 10.6084/m9.figshare.31081396.v2
title: Data and R-Script for "Emergent trees Peru beetles"

description: Data for MS "Importance of emergent trees for beetle conservation in a lowland rainforest in Peru" for peer review
DOI: 10.5281/zenodo.16730669
title: GeoLocator Data Package: Eurasian Nightjar from Mongolia

description: This Geolocator DataPackage describe the migration routes and timing of European Nightjars breeding in Eastern Mongolia

Original code that performed the analysis following the GeoPressureTemplate format can be found at Rafnuss/MongolianNightjar

The main publication associated with this datapacakge is:



Lathouwers, M., Nussbaumer, R., Liechti, F. et al. Migration routes and timing of European Nightjars (Caprimulgus europaeus) breeding in eastern Mongolia. J Ornithol (2022). https://doi.org/10.1007/s10336-022-02000-4


 

Cite this datapackage with:



Lathouwers M, Nussbaumer R, Kowalczyk C, Davaasuren B, Bayrgur S, Evens R (2025). “GeoLocator Data Package: Mongolian Nightjars.” Zenodo. https://doi.org/10.5281/zenodo.16730669


 



Spatial: Polygon and c(22.75, 112.75, 112.75, 22.75, 22.75, -33.25, -33.25, 49.75, 49.75, -33.25)

Temporal: "2018-07-15" to "2020-09-28"

Taxonomic: "Caprimulgus europeaus"

NumberTags:  tags: 49  measurements: 3  light: 3  pressure: 3  activity: 3  magnetic: 3  paths: 3  pressurepaths: 3
DOI: 10.5281/zenodo.18336618
title: GeoLocator Data Package: Eurasian Nightjar from Mongolia

description: This Geolocator DataPackage describe the migration routes and timing of European Nightjars breeding in Eastern Mongolia

Original code that performed the analysis following the GeoPressureTemplate format can be found at Rafnuss/MongolianNightjar

The main publication associated with this datapacakge is:



Lathouwers, M., Nussbaumer, R., Liechti, F. et al. Migration routes and timing of European Nightjars (Caprimulgus europaeus) breeding in eastern Mongolia. J Ornithol (2022). https://doi.org/10.1007/s10336-022-02000-4


 

Cite this datapackage with:



Lathouwers M, Nussbaumer R, Kowalczyk C, Davaasuren B, Bayrgur S, Evens R (2025). “GeoLocator Data Package: Mongolian Nightjars.” Zenodo. https://doi.org/10.5281/zenodo.16730669


 



Spatial: Polygon and c(22.75, 112.75, 112.75, 22.75, 22.75, -33.25, -33.25, 49.75, 49.75, -33.25)

Temporal: "2018-07-15" to "2020-09-28"

Taxonomic: "Caprimulgus europeaus"

NumberTags:  tags: 49  measurements: 3  light: 3  pressure: 3  activity: 3  magnetic: 3  paths: 3  pressurepaths: 3
DOI: 10.5281/zenodo.18336620
title: SOG'LOM OVQATLANISHNI TASHKIL ETISHDA PEDAGOGIK YONDASHUV

description: Mazkur maqolada sog‘lom ovqatlanishni tashkil etishda pedagogik yondashuvning o‘rni va ahamiyati yoritilgan. Sog‘lom ovqatlanish tushunchasining ta’lim jarayonidagi mazmuni ochib berilgan. Ta’lim muassasalarida ovqatlanishni to‘g‘ri tashkil etish masalalari tahlil qilingan. O‘quvchilarda sog‘lom ovqatlanish madaniyatini shakllantirish yo‘llari ko‘rsatib berilgan. Pedagogik usul va vositalarning tarbiyaviy imkoniyatlari yoritilgan. O‘qituvchining bu boradagi mas’uliyati asoslab berilgan. Sog‘lom ovqatlanish orqali sog‘lom turmush tarzini shakllantirish masalalariga e’tibor qaratilgan.
DOI: 10.5281/zenodo.18336621
title: SOG'LOM OVQATLANISHNI TASHKIL ETISHDA PEDAGOGIK YONDASHUV

description: Mazkur maqolada sog‘lom ovqatlanishni tashkil etishda pedagogik yondashuvning o‘rni va ahamiyati yoritilgan. Sog‘lom ovqatlanish tushunchasining ta’lim jarayonidagi mazmuni ochib berilgan. Ta’lim muassasalarida ovqatlanishni to‘g‘ri tashkil etish masalalari tahlil qilingan. O‘quvchilarda sog‘lom ovqatlanish madaniyatini shakllantirish yo‘llari ko‘rsatib berilgan. Pedagogik usul va vositalarning tarbiyaviy imkoniyatlari yoritilgan. O‘qituvchining bu boradagi mas’uliyati asoslab berilgan. Sog‘lom ovqatlanish orqali sog‘lom turmush tarzini shakllantirish masalalariga e’tibor qaratilgan.
DOI: 10.5281/zenodo.18140177
title: Thermo Xcalibur Raw File

description: LC-MS data for analysis of ASMD plasma.
DOI: 10.5281/zenodo.18140176
title: Thermo Xcalibur Raw File

description: LC-MS data for analysis of ASMD plasma.
DOI: 10.5281/zenodo.17805752
title: ENDEMO-Europe: Dataset for European long-term cross-sectoral energy demand modelling

description: Energy demand models require a wide range of data to adequately characterize energy use across regions and consumption sectors, and to account for historical developments. The dataset we provide can serve as input for energy system modelling tools, such as the URBS model generator for cost optimization [1] and the ENDEMO framework for energy demand estimation and analysis [2, 3].

ENDEMO-Europe dataset provides processed and harmonized variables for energy demand modelling at national and NUTS 2 (county-level) scales across Europe, covering the EU-27 (excluding Cyprus and Malta), EFTA countries, the United Kingdom, and the Balkan peninsula. It spans four final consumption sectors: industry, households, commercial, trade and services (CTS), and transport.

The dataset integrates raw statistics with derived indicators, for example by combining energy per carrier, combustion efficiency, and employment data to calculate specific energy consumption per employee. Industry data include production quantities of energy-intensive subsectors, their NUTS 2 distribution, and specific energy requirements by technology, while non-energy-intensive branches are represented through GDP allocation. Household data cover floor area, hot water use, and specific energy consumption per m² or per capita; CTS data include employees per subsector and specific energy per employee; and transport data provide passenger- and tonne-kilometres, modal split, rail electrification rate, and consumption per person- or ton-kilometre.

Energy demand is distinguished by application type (heat, hydrogen, electricity), with both yearly and hourly profiles available for modelling and integrated systems analysis.

Keywords: useful energy demand; analysis; forecasting; timeseries; electricity; heat; hydrogen

 

References:

[1]    J. Dorfner et al., tum-ens/urbs: Zenodo, 2019. Accessed: Nov. 11 2025.

[2]   A. Kerekeš, L. Breuning and A. Epishev, endemo, 2025. Accessed: Dec. 3 2025. [Online]. Available: https://github.com/tum-ens/endemo

[3]   L. Breuning and A. Kerekeš, “endemo – Energy Demand Modeling for Europe,” in Energy Sciences for Europe’s Green Deal: 11th Colloquium of the Munich School of Engineering, Garching bei München, 2021. Accessed: Dec. 3 2025. [Online]. Available: https://mediatum.ub.tum.de/doc/1631566/0zjz35nd6rdatc8xb3cq5xkyx.pdf
DOI: 10.5281/zenodo.18336567
title: ENDEMO-Europe: Dataset for European long-term cross-sectoral energy demand modelling

description: Energy demand models require a wide range of data to adequately characterize energy use across regions and consumption sectors, and to account for historical developments. The dataset we provide can serve as input for energy system modelling tools, such as the URBS model generator for cost optimization [1] and the ENDEMO framework for energy demand estimation and analysis [2, 3].

ENDEMO-Europe dataset provides processed and harmonized variables for energy demand modelling at national and NUTS 2 (county-level) scales across Europe, covering the EU-27 (excluding Cyprus and Malta), EFTA countries, the United Kingdom, and the Balkan peninsula. It spans four final consumption sectors: industry, households, commercial, trade and services (CTS), and transport.

The dataset integrates raw statistics with derived indicators, for example by combining energy per carrier, combustion efficiency, and employment data to calculate specific energy consumption per employee. Industry data include production quantities of energy-intensive subsectors, their NUTS 2 distribution, and specific energy requirements by technology, while non-energy-intensive branches are represented through GDP allocation. Household data cover floor area, hot water use, and specific energy consumption per m² or per capita; CTS data include employees per subsector and specific energy per employee; and transport data provide passenger- and tonne-kilometres, modal split, rail electrification rate, and consumption per person- or ton-kilometre.

Energy demand is distinguished by application type (heat, hydrogen, electricity), with both yearly and hourly profiles available for modelling and integrated systems analysis.

Keywords: useful energy demand; analysis; forecasting; timeseries; electricity; heat; hydrogen

 

References:

[1]    J. Dorfner et al., tum-ens/urbs: Zenodo, 2019. Accessed: Nov. 11 2025.

[2]   A. Kerekeš, L. Breuning and A. Epishev, endemo, 2025. Accessed: Dec. 3 2025. [Online]. Available: https://github.com/tum-ens/endemo

[3]   L. Breuning and A. Kerekeš, “endemo – Energy Demand Modeling for Europe,” in Energy Sciences for Europe’s Green Deal: 11th Colloquium of the Munich School of Engineering, Garching bei München, 2021. Accessed: Dec. 3 2025. [Online]. Available: https://mediatum.ub.tum.de/doc/1631566/0zjz35nd6rdatc8xb3cq5xkyx.pdf
DOI: 10.15468/dl.n4ave2
title: Occurrence Download

description: A dataset containing 377 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tipula latemarginata",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 377 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004711-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/hhhmnvfzm5
title: Making Paper Pay: Commercial Paper Trading Facilitation and Corporate Tax Avoidance

description: All programs of this paper are included in "EM data" file.
DOI: 10.17632/hhhmnvfzm5.1
title: Making Paper Pay: Commercial Paper Trading Facilitation and Corporate Tax Avoidance

description: All programs of this paper are included in "EM data" file.
DOI: 10.17632/fnsk4rbffm
title: QTNANO - Tuning Aprotic Solvent Properties with Long Alkyl Chain Ionic Liquid for Lithium-based Electrolytes, Journal of Materials Chemistry A, 2022,10, 11684-11701.

description: This repository contains the primary GROMACS input and output files used in the molecular dynamics simulations, including force field parameters, topology files, molecular dynamics protocols, and equilibrated and production simulation box structures. It also includes the output files generated from the analyses used to construct the figures presented in the Results section of the manuscript “Tuning Aprotic Solvent Properties with Long Alkyl Chain Ionic Liquids for Lithium-Based Electrolytes” (DOI: https://doi.org/10.1039/D1TA10592B). All files and the overall organization of the repository were prepared by Dr. Tuanan C. Lourenço. For any questions or further information, please contact Dr. Tuanan C. Lourenço at lourenco.tuanan@gmail.com.
DOI: 10.17632/fnsk4rbffm.1
title: QTNANO - Tuning Aprotic Solvent Properties with Long Alkyl Chain Ionic Liquid for Lithium-based Electrolytes, Journal of Materials Chemistry A, 2022,10, 11684-11701.

description: This repository contains the primary GROMACS input and output files used in the molecular dynamics simulations, including force field parameters, topology files, molecular dynamics protocols, and equilibrated and production simulation box structures. It also includes the output files generated from the analyses used to construct the figures presented in the Results section of the manuscript “Tuning Aprotic Solvent Properties with Long Alkyl Chain Ionic Liquids for Lithium-Based Electrolytes” (DOI: https://doi.org/10.1039/D1TA10592B). All files and the overall organization of the repository were prepared by Dr. Tuanan C. Lourenço. For any questions or further information, please contact Dr. Tuanan C. Lourenço at lourenco.tuanan@gmail.com.
DOI: 10.17632/p4w367x9sj
title: Rheological, mechanical and microstructure properties of microfine particle alkaline-activated grouting material

description: This Mendeley Data repository provides partial datasets associated with the research article entitled “Rheological, mechanical and microstructure properties of microfine particle alkaline-activated grouting material,” published in the European Journal of Environmental and Civil Engineering. The available datasets include:
1. Rheological and mechanical testing data
2. XRD data of PCIII and SF
3. XRD analysis of 1CT and 1SF6 at 7 and 28 days
4. SEM and EDS data of 1CT and 1SF6
5. Brunauer–Emmett–Teller (BET) analysis of 1CT and 1SF6
6. FTIR analysis of 1CT and 1SF6
Access to this repository is restricted to the journal editors and individuals authorized by the authors for research-related purposes.