cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.31223026.v1
title: Energy Use Analysis vs Gross Internal Area

description: EUI vs GIA using NHS ERIC data from 2018 to 2025
DOI: 10.5281/zenodo.18412072
title: Pottery technological analysis of the Copper Age Transdanubia (Western Hungary) – Supplement 1

description: Supplement 1. Technological data of every analysed vessel.
DOI: 10.5281/zenodo.18412073
title: Pottery technological analysis of the Copper Age Transdanubia (Western Hungary) – Supplement 1

description: Supplement 1. Technological data of every analysed vessel.
DOI: 10.5281/zenodo.18411869
title: Ceramic Depositions, Ritual Features, and Irregular Burials of the Tumulus Culture in Hungary – Table 5

description: Tab. 5. Non-normative burials and features containing human remains of the Tumulus Culture.
DOI: 10.5281/zenodo.18411868
title: Ceramic Depositions, Ritual Features, and Irregular Burials of the Tumulus Culture in Hungary – Table 5

description: Tab. 5. Non-normative burials and features containing human remains of the Tumulus Culture.
DOI: 10.15468/dl.7dy3ae
title: Occurrence Download

description: A dataset containing 20477 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-143.61041 26.18838,-58.80650 26.18838,-58.80650 60.12951,-143.61041 60.12951,-143.61041 26.18838))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Centrocercus urophasianus"
 ]
}

 The dataset includes 20477 records from 61 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003705-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18411742
title: Ceramic Depositions, Ritual Features, and Irregular Burials of the Tumulus Culture in Hungary – Table 1

description: Tab. 1. Tumulus Culture ceramic depositions and non-normative burials, features containing human remains in Hungary.
DOI: 10.5281/zenodo.18411743
title: Ceramic Depositions, Ritual Features, and Irregular Burials of the Tumulus Culture in Hungary – Table 1

description: Tab. 1. Tumulus Culture ceramic depositions and non-normative burials, features containing human remains in Hungary.
DOI: 10.5281/zenodo.18212410
title: Data and code for: Rapid speed control of traveling-wave ultrasonic motors using sensorless deep reinforcement learning

description: This record provides processed datasets (CSV) and Python plotting scripts to reproduce Figs. 2–8 in the manuscript “Rapid speed control of traveling-wave ultrasonic motors using sensorless deep reinforcement learning” (Sasamura & Morita, under review).Raw experimental data and training logs/model weights are not included due to confidentiality restrictions related to an industry collaboration.

Data are released under CC BY 4.0; code is released under the MIT License.
DOI: 10.5281/zenodo.18212411
title: Data and code for: Rapid speed control of traveling-wave ultrasonic motors using sensorless deep reinforcement learning

description: This record provides processed datasets (CSV) and Python plotting scripts to reproduce Figs. 2–8 in the manuscript “Rapid speed control of traveling-wave ultrasonic motors using sensorless deep reinforcement learning” (Sasamura & Morita, under review).Raw experimental data and training logs/model weights are not included due to confidentiality restrictions related to an industry collaboration.

Data are released under CC BY 4.0; code is released under the MIT License.
DOI: 10.5281/zenodo.15022706
title: Automated Modernization of Machine Learning Engineering Notebooks for Reproducibility

description: Interactive computational notebooks (e.g., Jupyter notebooks) are widely used in machine learning engineering (MLE) to program and share end-to-end pipelines, from data preparation to model training and evaluation. However, environment erosion—the rapid evolution of hardware and software ecosystems for machine learning—has rendered many published MLE notebooks non-reproducible in contemporary environments, hindering code reuse and scientific progress. To quantify this gap, we study 12,720 notebooks mined from 79 popular Kaggle competitions: only 35.4% remain reproducible today. Crucially, we find that environment backporting, i.e., downgrading dependencies to match the submission time, does not improve reproducibility but rather introduces additional failure modes.

To address environment erosion, we design and implement MLEModernizer, an LLM-driven agentic framework that treats the contemporary environment as a fixed constraint and modernizes notebook code to restore reproducibility. MLEModernizer iteratively executes notebooks, collects execution feedback, and applies targeted fixes in three types: error-repair, runtime-reduction, and score-calibration. Evaluated on 7,402 notebooks that are non-reproducible under the baseline environment, MLEModernizer makes 5,492 (74.2%) reproducible. MLEModernizer enables practitioners to validate, reuse, and maintain MLE artifacts as the hardware and software ecosystems continue to evolve.======================================================================================================================The per-fix CSV outputs for each file-level and cell-level modernization (i.e., 

./results/upgrade/{cell, file}/csv_output/fix_{fix_number}

 for fix_number ∈ {1,…,16}) are not included in this replication package due to size constraints.

However, when applicable, we have recorded the score for each fix---consistent with MLE-Bench requirement---in

./results/upgrade/{cell, file}/executable_files_w_timer_parrallel.json

, and we have included all reproducible CSVs at

./results/upgrade/{cell, file}/csv_output/{filename}.csv

If the per-fix CSV outputs are required, we would recommand replicating the corresponding notebook(s) to generate the CSV again.
DOI: 10.5281/zenodo.15022707
title: Automated Modernization of Machine Learning Engineering Notebooks for Reproducibility

description: Interactive computational notebooks (e.g., Jupyter notebooks) are widely used in machine learning engineering (MLE) to program and share end-to-end pipelines, from data preparation to model training and evaluation. However, environment erosion—the rapid evolution of hardware and software ecosystems for machine learning—has rendered many published MLE notebooks non-reproducible in contemporary environments, hindering code reuse and scientific progress. To quantify this gap, we study 12,720 notebooks mined from 79 popular Kaggle competitions: only 35.4% remain reproducible today. Crucially, we find that environment backporting, i.e., downgrading dependencies to match the submission time, does not improve reproducibility but rather introduces additional failure modes.

To address environment erosion, we design and implement MLEModernizer, an LLM-driven agentic framework that treats the contemporary environment as a fixed constraint and modernizes notebook code to restore reproducibility. MLEModernizer iteratively executes notebooks, collects execution feedback, and applies targeted fixes in three types: error-repair, runtime-reduction, and score-calibration. Evaluated on 7,402 notebooks that are non-reproducible under the baseline environment, MLEModernizer makes 5,492 (74.2%) reproducible. MLEModernizer enables practitioners to validate, reuse, and maintain MLE artifacts as the hardware and software ecosystems continue to evolve.======================================================================================================================The per-fix CSV outputs for each file-level and cell-level modernization (i.e., 

./results/upgrade/{cell, file}/csv_output/fix_{fix_number}

 for fix_number ∈ {1,…,16}) are not included in this replication package due to size constraints.

However, when applicable, we have recorded the score for each fix---consistent with MLE-Bench requirement---in

./results/upgrade/{cell, file}/executable_files_w_timer_parrallel.json

, and we have included all reproducible CSVs at

./results/upgrade/{cell, file}/csv_output/{filename}.csv

If the per-fix CSV outputs are required, we would recommand replicating the corresponding notebook(s) to generate the CSV again.
DOI: 10.15468/dl.tj6aqr
title: Occurrence Download

description: A dataset containing 115151 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-162.00244 14.78680,-55.99756 14.78680,-55.99756 57.21320,-162.00244 57.21320,-162.00244 14.78680))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Gymnorhinus cyanocephalus"
 ]
}

 The dataset includes 115151 records from 72 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003697-260129131611470/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.31223035
title: Backlog Intensity vs Energy Use Intensity

description: Backlog Intensity vs Energy Use Intensity using NHS ERIC data from 2018-2025
DOI: 10.6084/m9.figshare.31223035.v1
title: Backlog Intensity vs Energy Use Intensity

description: Backlog Intensity vs Energy Use Intensity using NHS ERIC data from 2018-2025
DOI: 10.5281/zenodo.18432596
title: High-throughput Characterization of Bistable Beam Stability Boundaries

description: Overview

This dataset provides a comprehensive experimental mapping of the snap-through stability boundaries for bistable elastic beams. The data was generated using a high-throughput rotating platform at the Flexible Structures Laboratory (EPFL). By prescribing programmable angular velocity profiles Ω(t), the platform subjects specimens to tunable centrifugal and Euler force fields.

The collection comprises around 20.000 individual experiments across 65 distinct configurations, enabling the statistical characterization of nonlinear dynamic instabilities.

Dataset Structure

The data is provided in a single compressed archive. To maintain metadata integrity, the experimental parameters are encoded directly into the folder hierarchy:

Database/ → [BC_Type]/ → [Configuration]/ → [Loading_Parameters]/ → Data/





BC_Type: Category of boundary conditions (e.g., Thickness, TiltAngle, SymmetricClamps).




Configuration: Specific geometric values (e.g., t2.10_eps0.035_theta5.0).




Loading_Parameters: The drive conditions: amp_[a]_freq_[f]_state_[s0], where amp is amplitude [rad/s], f is frequency [Hz], and s0 is the initial stable state (0 or 1).




Data: Contains three independent repetitions per condition.



File Contents (per repetition)





images_[timestamp]/: Raw image sequences of the beam.



Note: While captured in the lab frame, these images are digitally stabilized to the rotating frame. Minor wiggling may occur due to rotation correction.





processed_data_epm_[timestamp].json: Time-series of midspan displacement δ(t), velocity, and acceleration from the motor encoder.




splines_epm_[timestamp].json: Series of centerline data encoded as Hermite exponential splines.



Technical Specifications

Parameter Ranges












Parameter
Range




Thickness (t)
1.60 to 2.70 mm


Pre-compression (ε)
0.015 to 0.060


Tilt Angle (θ)
0 to ±20deg


Clamp Angles (φi,φe)
0 to ±15deg





Export to Sheets




Working with Spline Data

The centerlines are represented as C2-continuous splines with 5 knots. To load and analyze the .json spline files, use the Python library splinebox. See their tutorial to load series of splines.

Citation

If you use this dataset in your research, please cite the following paper:



Gutierrez-Prieto, E., Yakir, G., & Reis, P.M. (2026). High-throughput characterization of snap-through stability boundaries of bistable beams in a programmable rotating platform. Mechanics of Materials.
DOI: 10.5281/zenodo.18432597
title: High-throughput Characterization of Bistable Beam Stability Boundaries

description: Overview

This dataset provides a comprehensive experimental mapping of the snap-through stability boundaries for bistable elastic beams. The data was generated using a high-throughput rotating platform at the Flexible Structures Laboratory (EPFL). By prescribing programmable angular velocity profiles Ω(t), the platform subjects specimens to tunable centrifugal and Euler force fields.

The collection comprises around 20.000 individual experiments across 65 distinct configurations, enabling the statistical characterization of nonlinear dynamic instabilities.

Dataset Structure

The data is provided in a single compressed archive. To maintain metadata integrity, the experimental parameters are encoded directly into the folder hierarchy:

Database/ → [BC_Type]/ → [Configuration]/ → [Loading_Parameters]/ → Data/





BC_Type: Category of boundary conditions (e.g., Thickness, TiltAngle, SymmetricClamps).




Configuration: Specific geometric values (e.g., t2.10_eps0.035_theta5.0).




Loading_Parameters: The drive conditions: amp_[a]_freq_[f]_state_[s0], where amp is amplitude [rad/s], f is frequency [Hz], and s0 is the initial stable state (0 or 1).




Data: Contains three independent repetitions per condition.



File Contents (per repetition)





images_[timestamp]/: Raw image sequences of the beam.



Note: While captured in the lab frame, these images are digitally stabilized to the rotating frame. Minor wiggling may occur due to rotation correction.





processed_data_epm_[timestamp].json: Time-series of midspan displacement δ(t), velocity, and acceleration from the motor encoder.




splines_epm_[timestamp].json: Series of centerline data encoded as Hermite exponential splines.



Technical Specifications

Parameter Ranges












Parameter
Range




Thickness (t)
1.60 to 2.70 mm


Pre-compression (ε)
0.015 to 0.060


Tilt Angle (θ)
0 to ±20deg


Clamp Angles (φi,φe)
0 to ±15deg





Export to Sheets




Working with Spline Data

The centerlines are represented as C2-continuous splines with 5 knots. To load and analyze the .json spline files, use the Python library splinebox. See their tutorial to load series of splines.

Citation

If you use this dataset in your research, please cite the following paper:



Gutierrez-Prieto, E., Yakir, G., & Reis, P.M. (2026). High-throughput characterization of snap-through stability boundaries of bistable beams in a programmable rotating platform. Mechanics of Materials.
DOI: 10.6084/m9.figshare.31223038
title: Building Age Profile vs Energy Use Intensity

description: Correlation Analysis between Building Age Profile and Energy Use Intensity using NHS ERIC data 2024-25
DOI: 10.6084/m9.figshare.31223038.v1
title: Building Age Profile vs Energy Use Intensity

description: Correlation Analysis between Building Age Profile and Energy Use Intensity using NHS ERIC data 2024-25
DOI: 10.6084/m9.figshare.31222993
title: Dataset for Paper in European Planning Studies titled "Accommodating Urban Population Changes in the European Union Through Infill, Densification, Inclusion, and Extension, 1980-2020". Uploaded on 31 January 2026

description: Cities in the European Union accommodated population changes during 1980-2020 by adapting their urban forms in four complementary processes: (1) the infill of vacant lands within existing urban footprints with new built-up areas, (2) the densification of existing built-up areas, (3) the inclusion of existing settlements in their expansion areas, and (4) the outward extension of their built-up areas. We report on the contribution of each of these four processes to accommodating urban population changes during a given decade. We use built-up area and population data from the Global Human Settlements Layer (GHSL) to derive urban footprint boundaries and estimate the shares of the population added to cities in every decade—the 1980s, the 1990s, the 2000s, and the 2010s—that was accommodated by the four different changes in urban form. Our dataset includes 497 cities and metropolitan areas in the 26 countries (excluding Cyprus) in the European Union (EU) that had 50,000 people or more in 2020. The increase in infill of existing footprints typically accommodated the largest share of the added population of EU cities in all four decades—compared to inclusion and outward extension—while built-up area densities within existing city footprints generally declined.
DOI: 10.6084/m9.figshare.31222993.v1
title: Dataset for Paper in European Planning Studies titled "Accommodating Urban Population Changes in the European Union Through Infill, Densification, Inclusion, and Extension, 1980-2020". Uploaded on 31 January 2026

description: Cities in the European Union accommodated population changes during 1980-2020 by adapting their urban forms in four complementary processes: (1) the infill of vacant lands within existing urban footprints with new built-up areas, (2) the densification of existing built-up areas, (3) the inclusion of existing settlements in their expansion areas, and (4) the outward extension of their built-up areas. We report on the contribution of each of these four processes to accommodating urban population changes during a given decade. We use built-up area and population data from the Global Human Settlements Layer (GHSL) to derive urban footprint boundaries and estimate the shares of the population added to cities in every decade—the 1980s, the 1990s, the 2000s, and the 2010s—that was accommodated by the four different changes in urban form. Our dataset includes 497 cities and metropolitan areas in the 26 countries (excluding Cyprus) in the European Union (EU) that had 50,000 people or more in 2020. The increase in infill of existing footprints typically accommodated the largest share of the added population of EU cities in all four decades—compared to inclusion and outward extension—while built-up area densities within existing city footprints generally declined.
DOI: 10.5281/zenodo.18445065
title: Data availability for theoretical quantum ring simulations
DOI: 10.5281/zenodo.18445066
title: Data availability for theoretical quantum ring simulations
DOI: 10.13140/rg.2.2.30903.38569
title: THE SENATE S.B. NO. 2713 THIRTY-THIRD LEGISLATURE, 2026 STATE OF HAWAII "Senior Safe Zone Act"
DOI: 10.15468/dl.ebsnf3
title: Occurrence Download

description: A dataset containing 553027977 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 553027977 records from 19472 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003698-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d8n233
title: Occurrence Download

description: A dataset containing 12647 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Inula helvetica"
 ]
}

 The dataset includes 12647 records from 124 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049738-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15228706
title: Replication Package for a Study of Software Refactorings in Real-World Open-Source Java Projects

description: This replication package accompanies our study on real-world refactorings in open-source Java projects. It includes the following components:





keywords.csv — A list of predefined keywords (adopted from prior studies) used for commit message filtering.




master_replication_file.csv — A dataset of commits containing refactorings mined from six widely used open-source Java systems: Spring Framework, Elasticsearch, Kafka, Hadoop, Tomcat, and JUnit4. The file includes commit hashes, associated refactoring types, and context-specific indicators.




taxonomy.csv — A consolidated taxonomy that classifies the discovered refactorings by edit type and specificity.




printed_version_of_the_webpage.pdf — A printed version of the webpage presenting the under-explored refactorings, along with real-world references.




underexploredrefactorings.github.io-main.zip — The full source code for the webpage, which can be run locally to reproduce the interactive browsing experience.



The package is designed to support reproducibility and further research on refactoring detection, classification, and tool support. Instructions for reproducing the results are provided in the README file. These include steps for cloning the studied repositories and inspecting commits. Each commit can be accessed using:



git show <commit-hash>
DOI: 10.5281/zenodo.16908694
title: Replication Package for a Study of Software Refactorings in Real-World Open-Source Java Projects

description: This replication package accompanies our study on real-world refactorings in open-source Java projects. It includes the following components:





keywords.csv — A list of predefined keywords (adopted from prior studies) used for commit message filtering.




master_replication_file.csv — A dataset of commits containing refactorings mined from six widely used open-source Java systems: Spring Framework, Elasticsearch, Kafka, Hadoop, Tomcat, and JUnit4. The file includes commit hashes, associated refactoring types, and context-specific indicators.




taxonomy.csv — A consolidated taxonomy that classifies the discovered refactorings by edit type and specificity.




printed_version_of_the_webpage.pdf — A printed version of the webpage presenting the under-explored refactorings, along with real-world references.




underexploredrefactorings.github.io-main.zip — The full source code for the webpage, which can be run locally to reproduce the interactive browsing experience.



The package is designed to support reproducibility and further research on refactoring detection, classification, and tool support. Instructions for reproducing the results are provided in the README file. These include steps for cloning the studied repositories and inspecting commits. Each commit can be accessed using:



git show <commit-hash>
DOI: 10.5281/zenodo.16905840
title: Turbo du Théorème de Chica et le Théorème Solitaire de 11 par 12

description: Nouvelle approche sur le critère de divisibilité de 11 et de 7.
DOI: 10.5281/zenodo.16908711
title: Turbo du Théorème de Chica et le Théorème Solitaire de 11 par 12

description: Nouvelle approche sur le critère de divisibilité de 11 et de 7.
DOI: 10.6084/m9.figshare.28870721.v1
title: Pranevicius et al (205)_ Suppl Figure 1

description: Supp, Fig 1
DOI: 10.6084/m9.figshare.28870721
title: Pranevicius et al (205)_ Suppl Figure 1

description: Supp, Fig 1
DOI: 10.15468/dl.85zjsa
title: Occurrence Download

description: A dataset containing 238 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Rhododendron sphaeroblastum Balf.fil. & Forrest"
 ]
}

 The dataset includes 238 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001794-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bkt2sz
title: Occurrence Download

description: A dataset containing 538 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Rhododendron faberi Hemsl."
 ]
}

 The dataset includes 538 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001795-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g5f6hz
title: Occurrence Download

description: A dataset containing 130 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Rhododendron megeratum Balf.fil. & Forrest"
 ]
}

 The dataset includes 130 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001797-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.