cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15770/eum_saf_ac_0032
title: UV Data Record Release 1, solar noon UV Index - Multimission
title: MxG-RP1-O-UV_NOON_UVI
title: O3M-148

description: This product gives the solar noon UV index (UVI) for a given day. The solar spectral irradiance arriving on a horizontal Earth's surface at solar noon and a given location is weighted by the erythemal action spectrum and integrated over wavelength to yield the erythemal dose rate, and then converted to UVI by simple multiplication. The erythemal action spectrum describes the reddening of human skin after exposure to ultraviolet radiation. Input total column ozone data are obtained from GOME-2/Metop measurements while daily cloud data are collected from multiple overpasses of AVHRR/3 instrument onboard both Metop and NOAA satellites. The inputs are interpolated to solar noon time.
DOI: 10.5281/zenodo.13255986
title: UAV-based Lidar point clouds of Experimental Station Britz, Brandenburg, 2024

description: UAV-based Lidar data of a Experimental Forest Station Britz



Date of acquisition: 09.07.2024

Location: Experimental Station Britz, Britz, Brandenburg, Germany



DEIMS.iD: https://deims.org/8ee82a9b-5086-4547-b5aa-4064e3314762


UAV: DJI M300 RTK with SAPOS connection

Flight altitude above ground level: 40 m

Line spacing: 7 m

Sensor: Yellowscan Mapper Plus

EPSG: 25833; GCG2016

Data products: •    High-resolution point cloud in .laz format•    Yellowscan Cloudstation strip adjustment report•    Trimble Applanix POSPAC - diagnostic report


Acknowledgment

Dr. Tanja Sanders

Dr. Marco Natkhin

Institute of Forest EcosystemsJohann Heinrich von Thünen InstituteFederal Research Institute for Rural Areas, Forestry and Fisheries
DOI: 10.5281/zenodo.13255987
title: UAV-based Lidar point clouds of Experimental Station Britz, Brandenburg, 2024

description: UAV-based Lidar data of a Experimental Forest Station Britz



Date of acquisition: 09.07.2024

Location: Experimental Station Britz, Britz, Brandenburg, Germany



DEIMS.iD: https://deims.org/8ee82a9b-5086-4547-b5aa-4064e3314762


UAV: DJI M300 RTK with SAPOS connection

Flight altitude above ground level: 40 m

Line spacing: 7 m

Sensor: Yellowscan Mapper Plus

EPSG: 25833; GCG2016

Data products: •    High-resolution point cloud in .laz format•    Yellowscan Cloudstation strip adjustment report•    Trimble Applanix POSPAC - diagnostic report


Acknowledgment

Dr. Tanja Sanders

Dr. Marco Natkhin

Institute of Forest EcosystemsJohann Heinrich von Thünen InstituteFederal Research Institute for Rural Areas, Forestry and Fisheries
DOI: 10.5281/zenodo.17881277
title: The Hidden Sweet Tooth of the Black Soldier Fly (Hermetia illucens)

description: Raw tables and R code for article "The Hidden Sweet Tooth of the Black Soldier Fly (Hermetia illucens)"

Two-choice feeding assay records, PER records, electrophysiological records (spike recording), and R code for statistical analyses.
DOI: 10.5281/zenodo.18740943
title: The Hidden Sweet Tooth of the Black Soldier Fly (Hermetia illucens)

description: Raw tables and R code for article "The Hidden Sweet Tooth of the Black Soldier Fly (Hermetia illucens)"

Two-choice feeding assay records, PER records, electrophysiological records (spike recording), and R code for statistical analyses.
DOI: 10.15468/dl.u44rwa
title: Occurrence Download

description: A dataset containing 365 species occurrences available in GBIF matching the query:
{
 "and" : [
 "SpeciesKey is Thymus serpylloides",
 "Year is greater than or equal to 1970",
 "Year is less than or equal to 2026",
 "HasCoordinate is TRUE"
 ]
}

 The dataset includes 365 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003264-260221153910048/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.31389277
title: The Potential Regulatory Roles of Hsa_Circ_0002629/Hsa-miR-532-3p axis for the Retina Neovascularization in Diabetic Retinopathy <i>In Vitro</i> and <i>In Vivo</i>

description: Diabetic retinopathy (DR), a major microvascular complication of diabetes, can lead to blindness, and its incidence is increasing. CircRNAs have been associated with a variety of tumors and vascular diseases, including DR. Here, we investigated the potential regulatory roles of hsa_circ_0002629/hsa-miR-532-3p in retinal neovascularization in DR. Human retinal microvascular epithelial cells (hRECs) were exposed to three different conditions: low glucose (LG), LG plus mannitol, and high glucose (HG). Then, cells cultured under HG conditions were transfected with short interfering RNAs (siRNAs) targeting hsa_circ_0002629, hsa-miR-532-3p mimics and inhibitors, and a hsa_circ_0002629 overexpression (OE) plasmid to assess hREC viability in hyperglycemia. Streptozotocin (STZ)-induced DR model rats were injected with rno-miR-532-3p agomir or antagomir every 2 weeks. Dual-luciferase reporter assays and FISH were used to investigate the direct binding relationship among hsa_circ_0002629, hsa-miR-532-3p, and <i>HMGA2</i>. QPCR analysis revealed that the expression levels of hsa_circ_0002629 were markedly elevated, while those of hsa-miR-532-3p were decreased in aqueous humor samples of DR patients and HG-exposed hRECs. Hsa_circ_0002629 overexpression improved hREC viability in hyperglycemia, reflected in augmented DNA synthesis, proliferation, migration, and tube formation; however, these effects were partially rescued by hsa-miR-532-3p mimics. In STZ-induced DR model rats, vitreous microinjection of rno-miR-532-3p agomir alleviated the pathologic features DR, including retinal vascular leakage, capillary decellularization, pericyte loss, fibrosis, and gliosis. Moreover, dual-luciferase reporter and FISH assays indicated that hsa-miR-532-3p directly targets both hsa_circ_0002629 and <i>HMGA2.</i> These results indicated that the hsa_circ_0002629/hsa-miR-532-3p axis regulates the development of DR <i>via HMGA2</i> within the DR microenvironment. The hsa_circ_0002629/hsa-miR-532-3p/<i>HMGA2</i> signaling pathway plays an essential role in the progression of DR and may represent a promising target for the clinical diagnosis and treatment of this condition.
DOI: 10.6084/m9.figshare.31389277.v1
title: The Potential Regulatory Roles of Hsa_Circ_0002629/Hsa-miR-532-3p axis for the Retina Neovascularization in Diabetic Retinopathy <i>In Vitro</i> and <i>In Vivo</i>

description: Diabetic retinopathy (DR), a major microvascular complication of diabetes, can lead to blindness, and its incidence is increasing. CircRNAs have been associated with a variety of tumors and vascular diseases, including DR. Here, we investigated the potential regulatory roles of hsa_circ_0002629/hsa-miR-532-3p in retinal neovascularization in DR. Human retinal microvascular epithelial cells (hRECs) were exposed to three different conditions: low glucose (LG), LG plus mannitol, and high glucose (HG). Then, cells cultured under HG conditions were transfected with short interfering RNAs (siRNAs) targeting hsa_circ_0002629, hsa-miR-532-3p mimics and inhibitors, and a hsa_circ_0002629 overexpression (OE) plasmid to assess hREC viability in hyperglycemia. Streptozotocin (STZ)-induced DR model rats were injected with rno-miR-532-3p agomir or antagomir every 2 weeks. Dual-luciferase reporter assays and FISH were used to investigate the direct binding relationship among hsa_circ_0002629, hsa-miR-532-3p, and <i>HMGA2</i>. QPCR analysis revealed that the expression levels of hsa_circ_0002629 were markedly elevated, while those of hsa-miR-532-3p were decreased in aqueous humor samples of DR patients and HG-exposed hRECs. Hsa_circ_0002629 overexpression improved hREC viability in hyperglycemia, reflected in augmented DNA synthesis, proliferation, migration, and tube formation; however, these effects were partially rescued by hsa-miR-532-3p mimics. In STZ-induced DR model rats, vitreous microinjection of rno-miR-532-3p agomir alleviated the pathologic features DR, including retinal vascular leakage, capillary decellularization, pericyte loss, fibrosis, and gliosis. Moreover, dual-luciferase reporter and FISH assays indicated that hsa-miR-532-3p directly targets both hsa_circ_0002629 and <i>HMGA2.</i> These results indicated that the hsa_circ_0002629/hsa-miR-532-3p axis regulates the development of DR <i>via HMGA2</i> within the DR microenvironment. The hsa_circ_0002629/hsa-miR-532-3p/<i>HMGA2</i> signaling pathway plays an essential role in the progression of DR and may represent a promising target for the clinical diagnosis and treatment of this condition.
DOI: 10.15468/dl.3nxhns
title: Occurrence Download

description: A dataset containing 14814 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hippopotamus amphibius"
 ]
}

 The dataset includes 14814 records from 139 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003267-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18714476
title: LLM Inference Energy Efficiency Benchmark Dataset
title: SweetSpot: An Analytical Model for Predicting Energy Efficiency of LLM Inference

description: A comprehensive benchmark dataset capturing power consumption, performance metrics, and timing traces for Large Language Models (LLMs) running on TensorRT-LLM inference engine using NVIDIA H100 GPUs. The dataset was produces for the paper Determining Energy Efficiency Sweet Spots in Production LLM Inference (ICPE 2026), consult it for any additional information. 

If you do use this dataset in your research, please make sure to cite our paper.

Overview

This dataset contains detailed measurements of 13 different LLM models across various workload configurations, providing insights into token generation performance, energy consumption, and throughput characteristics. Each model was benchmarked under multiple input/output token lengths combinations to enable thorough analysis of inference behavior.

Models Included

The dataset covers 13 open-source LLM models ranging from 1.3B to 9B parameters:

- Falcon: falcon-rw-7b
- Gemma: gemma-2-2b-it, gemma-2-9b-it
- Granite: granite-3b-code-base-2k, granite-8b-code-base-4k
- LLaMA: Llama-3.1-8B-Instruct, Llama-3.2-1B-Instruct, Llama-3.2-3B-Instruct
- OPT: opt-1.3b, opt-2.7b, opt-6.7b
- Qwen: Qwen2-1.5B, Qwen2-7B

Metrics

Each model's CSV file contains the following columns:

- Workload Configuration: Input Length, Output Length, Max Batch Size, Number of Requests
- Performance: Request Throughput, Total/Per User Output Throughput, Total Token Throughput
- Latency: Total Latency, Average Request Latency
- Energy: Energy (KJ), Energy Per Token (J), Efficiency (Tokens/J)
- System: Max Runtime Tokens

Trace Files (traces/*.csv)

Time-series data captured at 100ms~500ms intervals:

- Timestamp (ms): Time since benchmark start
- gpu_power: GPU Power Consumption (Watts)
- gpu_clock: GPU Clock Frequency (MHz)

The metrics were sampled using the NVIDIA Management Library Python API.

Citation
@misc{cavagna2026determiningenergyefficiencysweet,
      title={Determining Energy Efficiency Sweet Spots in Production LLM Inference}, 
      author={Hiari Pizzini Cavagna and Andrea Proia and Giacomo Madella and Giovanni B. Esposito and Francesco Antici and Daniele Cesarini and Zeynep Kiziltan and Andrea Bartolini},
      year={2026},
      eprint={2602.05695},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2602.05695}, 
}
DOI: 10.5281/zenodo.18714475
title: LLM Inference Energy Efficiency Benchmark Dataset
title: SweetSpot: An Analytical Model for Predicting Energy Efficiency of LLM Inference

description: A comprehensive benchmark dataset capturing power consumption, performance metrics, and timing traces for Large Language Models (LLMs) running on TensorRT-LLM inference engine using NVIDIA H100 GPUs. The dataset was produces for the paper Determining Energy Efficiency Sweet Spots in Production LLM Inference (ICPE 2026), consult it for any additional information. 

If you do use this dataset in your research, please make sure to cite our paper.

Overview

This dataset contains detailed measurements of 13 different LLM models across various workload configurations, providing insights into token generation performance, energy consumption, and throughput characteristics. Each model was benchmarked under multiple input/output token lengths combinations to enable thorough analysis of inference behavior.

Models Included

The dataset covers 13 open-source LLM models ranging from 1.3B to 9B parameters:

- Falcon: falcon-rw-7b
- Gemma: gemma-2-2b-it, gemma-2-9b-it
- Granite: granite-3b-code-base-2k, granite-8b-code-base-4k
- LLaMA: Llama-3.1-8B-Instruct, Llama-3.2-1B-Instruct, Llama-3.2-3B-Instruct
- OPT: opt-1.3b, opt-2.7b, opt-6.7b
- Qwen: Qwen2-1.5B, Qwen2-7B

Metrics

Each model's CSV file contains the following columns:

- Workload Configuration: Input Length, Output Length, Max Batch Size, Number of Requests
- Performance: Request Throughput, Total/Per User Output Throughput, Total Token Throughput
- Latency: Total Latency, Average Request Latency
- Energy: Energy (KJ), Energy Per Token (J), Efficiency (Tokens/J)
- System: Max Runtime Tokens

Trace Files (traces/*.csv)

Time-series data captured at 100ms~500ms intervals:

- Timestamp (ms): Time since benchmark start
- gpu_power: GPU Power Consumption (Watts)
- gpu_clock: GPU Clock Frequency (MHz)

The metrics were sampled using the NVIDIA Management Library Python API.

Citation
@misc{cavagna2026determiningenergyefficiencysweet,
      title={Determining Energy Efficiency Sweet Spots in Production LLM Inference}, 
      author={Hiari Pizzini Cavagna and Andrea Proia and Giacomo Madella and Giovanni B. Esposito and Francesco Antici and Daniele Cesarini and Zeynep Kiziltan and Andrea Bartolini},
      year={2026},
      eprint={2602.05695},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2602.05695}, 
}
DOI: 10.5281/zenodo.17909869
title: Geo-referenced environmental data collected by mobile sensing vehicles

description: This dataset consists of geo-referenced and time-stamped environmental measurements, including CO₂ concentration, air temperature, and relative humidity, collected by a fleet of mobile sensors in Benevento, Italy. The sensors are Sensirion SCD41 CO₂ sensors, managed by a vehicle-mounted RaspeberryPi, which perform local preprocessing, GPS acquisition, timestamp synchronization, and data transmission.

Data was collected by three vehicles with identical sensors, covering diverse urban trajectories to ensure wide spatial coverage. Each vehicle generated continuous data streams of CO₂, temperature, humidity, latitude, longitude, and timestamps. The dataset comprises raw sensor readings, spatial coordinates, temporal metadata, and vehicle IDs, enabling the reconstruction of trajectories and facilitating environmental analyses. Sampling intervals range from 1 to 5 seconds.

This dataset supports research on urban air quality, mobile sensing, spatio-temporal analysis, environmental monitoring, and vehicular sensor networks. It can be used for benchmarking interpolation algorithms, validating mobile sensing strategies, evaluating uncertainty propagation, and mobility-aware sampling. Additionally, it offers insight into sensor behavior in real-world conditions and methods for robust urban monitoring.

This dataset is described in a Data in Brief article currently under revision, entitled “Geo-Temporal Vehicular Environmental Sensing Dataset,” authored by the same authors.

This dataset was used in the Case Study section of the following article Mobile Urban Sensing: Spatio-Temporal Observability Analysis and Optimization:

@article{COLARUSSO2026101907,    title = {Mobile Urban Sensing: Spatio-Temporal Observability Analysis and Optimization},    journal = {Internet of Things},    pages = {101907},    year = {2026},    issn = {2542-6605},    doi = {https://doi.org/10.1016/j.iot.2026.101907},    url = {https://www.sciencedirect.com/science/article/pii/S2542660526000375},    author = {Carmine Colarusso and Marco Consales and Ida Falco and Eugenio Zimeo}}
DOI: 10.5525/gla.researchdata.2169
title: Non-gonadal PIWI protein, Aubergine, regulates regenerative stem cell proliferation and tumorigenesis in the Drosophila adult intestine

description: Image and numeric source data files corresponding to research article to be published.
DOI: 10.5281/zenodo.17909868
title: Geo-referenced environmental data collected by mobile sensing vehicles

description: This dataset consists of geo-referenced and time-stamped environmental measurements, including CO₂ concentration, air temperature, and relative humidity, collected by a fleet of mobile sensors in Benevento, Italy. The sensors are Sensirion SCD41 CO₂ sensors, managed by a vehicle-mounted RaspeberryPi, which perform local preprocessing, GPS acquisition, timestamp synchronization, and data transmission.

Data was collected by three vehicles with identical sensors, covering diverse urban trajectories to ensure wide spatial coverage. Each vehicle generated continuous data streams of CO₂, temperature, humidity, latitude, longitude, and timestamps. The dataset comprises raw sensor readings, spatial coordinates, temporal metadata, and vehicle IDs, enabling the reconstruction of trajectories and facilitating environmental analyses. Sampling intervals range from 1 to 5 seconds.

This dataset supports research on urban air quality, mobile sensing, spatio-temporal analysis, environmental monitoring, and vehicular sensor networks. It can be used for benchmarking interpolation algorithms, validating mobile sensing strategies, evaluating uncertainty propagation, and mobility-aware sampling. Additionally, it offers insight into sensor behavior in real-world conditions and methods for robust urban monitoring.

This dataset is described in a Data in Brief article currently under revision, entitled “Geo-Temporal Vehicular Environmental Sensing Dataset,” authored by the same authors.

This dataset was used in the Case Study section of the following article Mobile Urban Sensing: Spatio-Temporal Observability Analysis and Optimization:

@article{COLARUSSO2026101907,    title = {Mobile Urban Sensing: Spatio-Temporal Observability Analysis and Optimization},    journal = {Internet of Things},    pages = {101907},    year = {2026},    issn = {2542-6605},    doi = {https://doi.org/10.1016/j.iot.2026.101907},    url = {https://www.sciencedirect.com/science/article/pii/S2542660526000375},    author = {Carmine Colarusso and Marco Consales and Ida Falco and Eugenio Zimeo}}
DOI: 10.15468/dl.etu6zr
title: Occurrence Download

description: A dataset containing 278080 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-20 -38,55 -38,55 38,-20 38,-20 -38))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Amphibia"
 ]
}

 The dataset includes 278080 records from 233 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003201-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ngp48n
title: Occurrence Download

description: A dataset containing 146735 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is 2022_CEN PACA_1"
 ]
}

 The dataset includes 146735 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003244-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.2908/ilc_pnp13
title: Geschlechtsspezifische durchschnittliche Rentenlücke nach Altersgruppe
title: Average gender pension gap by age group
title: Écart moyen de pension entre les femmes et les hommes par groupe d’âge

description: Die geschlechtsspezifische durchschnittliche Rentenlücke wird definiert als der Unterschied zwischen dem durchschnittlichen (brutto) Renteneinkommen von Frauen und Männern, ausgedrückt in Prozent. Das Renteneinkommen umfasst Altersrenten, Hinterbliebenenleistungen sowie regelmäßige Renten aus individuellen privaten Vorsorgeplänen.
description: L’écart moyen de pension entre les femmes et les hommes est défini comme la différence entre le montant moyen (brut) de pension des femmes et celui des hommes, exprimée en pourcentage. Le pension comprend les prestations de vieillesse, les prestations de réversion ainsi que les retraites issues de régimes privés individuels perçues régulièrement.
description: The average gender pension gap is defined as the difference between women’s and men’s average (gross) pension income, expressed in percentage. Pension income includes old-age benefits, survivors’ benefits, and regular pensions from individual private plans.
DOI: 10.2908/ilc_pnp13m
title: Geschlechtsspezifisches medianes Rentengefälle nach Alter
title: Median gender pension gap by age group
title: Écart médian de pension entre les femmes et les hommes par groupe d’âge

description: Die geschlechtsspezifische mediane Rentenlücke wird definiert als der Unterschied zwischen dem medianen (brutto) Renteneinkommen von Frauen und Männern, ausgedrückt in Prozent. Das Renteneinkommen umfasst Altersrenten, Hinterbliebenenleistungen sowie regelmäßige Renten aus individuellen privaten Vorsorgeplänen.
description: L’écart médian de pension entre les femmes et les hommes est défini comme la différence entre montant médian (brut) de pension des femmes et celui des hommes, exprimée en pourcentage. Le pension comprend les prestations de vieillesse, les prestations de réversion ainsi que les retraites issues de régimes privés individuels perçues régulièrement.
description: The median gender pension gap is defined as the difference between women’s and men’s median (gross) pension income, expressed in percentage. Pension income includes old-age benefits, survivors’ benefits, and regular pensions from individual private plans.
DOI: 10.15468/dl.kg6q29
title: Occurrence Download

description: A dataset containing 77246 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Arachis hypogaea"
 ]
}

 The dataset includes 77246 records from 463 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003228-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18740999
title: Custom target regions BED file used in CHIP analysis workflow (GRCh38)

description: This BED file contains the custom target regions used in our CHIP analysis workflow.

The original target region file was obtained from the UK Biobank resource:"Target region used by the WES capture experiment (Resource 3803)".

The file is based on the IDT xGen Exome Research Panel and was modified by removing genes not relevant to the study.

This derived file is shared to ensure full computational reproducibility of the workflow.

Source: https://biobank.ndph.ox.ac.uk/ukb/refer.cgi?id=3803
DOI: 10.5281/zenodo.18740998
title: Custom target regions BED file used in CHIP analysis workflow (GRCh38)

description: This BED file contains the custom target regions used in our CHIP analysis workflow.

The original target region file was obtained from the UK Biobank resource:"Target region used by the WES capture experiment (Resource 3803)".

The file is based on the IDT xGen Exome Research Panel and was modified by removing genes not relevant to the study.

This derived file is shared to ensure full computational reproducibility of the workflow.

Source: https://biobank.ndph.ox.ac.uk/ukb/refer.cgi?id=3803