cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.2wqk7m
title: Occurrence Download

description: A dataset containing 5141 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "HasCoordinate is TRUE",
 "TaxonKey is one of (Macaca brunnescens (Matschie, 1901), Macaca fascicularis (Raffles, 1821), Macaca hecki (Matschie, 1901), Macaca maura (Schinz, 1825), Macaca nemestrina (Linnaeus, 1766), Macaca nigra (Desmarest, 1822), Macaca nigrescens (Temminck, 1849), Macaca ochreata (Ogilby, 1841), Macaca pagensis (Miller, 1903), Macaca siberu Fuentes & Olson, 1995, Macaca tonkeana (Meyer, 1899), Nasalis larvatus (van Wurmb, 1787), Presbytis bicolor Aimi & Bakar, 1992, Presbytis canicrus Miller, 1934, Presbytis chrysomelas (Müller, 1838), Presbytis comata (Desmarest, 1822), Presbytis fredericae (Sody, 1930), Presbytis frontata (Müller, 1838), Presbytis hosei (Thomas, 1889), Presbytis melalophos (Raffles, 1821), Presbytis mitrata Eschscholtz, 1821, Presbytis natunae (Thomas & Hartert, 1894), Presbytis percura Lyon, 1908, Presbytis potenziani (Bonaparte, 1856), Presbytis rubicunda (Müller, 1838), Presbytis sabana (Thomas, 1893), Presbytis siamensis (Müller & Schlegel, 1841), Presbytis siberu (Chasen & Kloss, 1928), Presbytis sumatrana (Müller & Schlegel, 1841), Presbytis thomasi (Collett, 1893), Simias concolor Miller, 1903, Trachypithecus auratus (É.Geoffroy Saint-Hilaire, 1812), Trachypithecus cristatus (Raffles, 1821))"
 ]
}

 The dataset includes 5141 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066950-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4rmcgu
title: Occurrence Download

description: A dataset containing 1228 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Macrochelys temminckii Troost, 1835",
 "Year 1000-2010"
 ]
}

 The dataset includes 1228 records from 52 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066952-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gcmrza
title: Occurrence Download

description: A dataset containing 938 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Neophilaenus exclamationis (Thunberg, 1874)",
 "Year 1970-2025"
 ]
}

 The dataset includes 938 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066954-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/12252099
title: LGOS_WLLD_HE
DOI: 10.34740/kaggle/dsv/12266702
title: World Stock Prices ( Daily Updating )
DOI: 10.15468/dl.npasze
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is CO1 Amplicon Sequence Variants of soil and leaf litter arthropod communities collected at Malaise traps from the Insect Biome Atlas project in Sweden",
 "TaxonKey is BOLD:ADV1281"
 ]
}

 The dataset includes 24 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066956-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jxbcjy
title: Occurrence Download

description: A dataset containing 198 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Ophiocordyceps sinensis (Berk.) G.H.Sung, J.M.Sung, Hywel-Jones & Spatafora"
 ]
}

 The dataset includes 198 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066958-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.12751/g-node.zkzj6f
title: Structural and Connectivity Markers for Resilience to Social Stress

description: Structural and diffusion MRI in a rodent model of chronic social stress
description: IsSupplementTo: Ayash S et al.: Structural and Connectivity Markers for Resilience to Social Stress. In preparation.
DOI: 10.25401/cardiffmet.29377310
title: Literature Review of AI and Robotics in Speech Therapy

description: The systematic review's analysis of 70 studies identified clinical, social, and technological challenges and opportunities in CLP research.
DOI: 10.25401/cardiffmet.29377310.v1
title: Literature Review of AI and Robotics in Speech Therapy

description: The systematic review's analysis of 70 studies identified clinical, social, and technological challenges and opportunities in CLP research.
DOI: 10.15468/dl.5ye8n8
title: Occurrence Download

description: A dataset containing 188 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "TaxonKey is Gonystylus Teijsm. & Binn.",
 "Year 1984-2024"
 ]
}

 The dataset includes 188 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066963-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.11583/dtu.28237124
title: High fidelity, aeroelastic stability simulations

description: This dataset contains the setup and result files for (high fidelity) aeroelastic simulations of the IEA 10MW wind turbine. A doublet forcing is applid at 100s. The data has been used for stability analysis by system identification in the related publications.The dataset contains results from three different simulation codes:<br>- standard HAWC2 simulations (called BEM)- advanced HAWC2 simulations featuring vortex cylinder and nearwake modeling (called BEVC)<br>- FSI simulations coupling EllipSys3D and HAWC2 structural solver (called FSI)<br>In each subfolder, there is<br>- a setup folder to re-create the simulations. Please not that valid licenses are required to run HAWC2/EllipSys3D- timeseries_power_curve, containing hdf5 result files for 0 degree yaw calculations from 4 to 25 m/s.<br>- timeseries_yaw, containing hdf5 result files for simulations at +/- 8, 16 and 30 degree yaw misalignment, 5, 10, 15, 20 and 25m/s wind speed.<br>The naming of the timeseries is:<br>BEM: std_y0_wsp1000prefix "std_" can be ignored<br>"y0" describes the yaw miasalignment, 0 degress in this case. y30 for +30 degrees, ym30 for -30 degrees"wsp1000" describes the wind speed, 10m/s in this case (multiplied by 100 to have integer values).BEVC: std_bevc_y0_wsp1000<br>the tag "_bevc" is added to indicate the advanced setup<br>FSI: 10_y0<br>the first number, "10", indicates the wind speedthe second part, "_y0", the yaw misalignment, same as for BEM/BEVC
DOI: 10.11583/dtu.28237124.v1
title: High fidelity, aeroelastic stability simulations

description: This dataset contains the setup and result files for (high fidelity) aeroelastic simulations of the IEA 10MW wind turbine. A doublet forcing is applid at 100s. The data has been used for stability analysis by system identification in the related publications.The dataset contains results from three different simulation codes:<br>- standard HAWC2 simulations (called BEM)- advanced HAWC2 simulations featuring vortex cylinder and nearwake modeling (called BEVC)<br>- FSI simulations coupling EllipSys3D and HAWC2 structural solver (called FSI)<br>In each subfolder, there is<br>- a setup folder to re-create the simulations. Please not that valid licenses are required to run HAWC2/EllipSys3D- timeseries_power_curve, containing hdf5 result files for 0 degree yaw calculations from 4 to 25 m/s.<br>- timeseries_yaw, containing hdf5 result files for simulations at +/- 8, 16 and 30 degree yaw misalignment, 5, 10, 15, 20 and 25m/s wind speed.<br>The naming of the timeseries is:<br>BEM: std_y0_wsp1000prefix "std_" can be ignored<br>"y0" describes the yaw miasalignment, 0 degress in this case. y30 for +30 degrees, ym30 for -30 degrees"wsp1000" describes the wind speed, 10m/s in this case (multiplied by 100 to have integer values).BEVC: std_bevc_y0_wsp1000<br>the tag "_bevc" is added to indicate the advanced setup<br>FSI: 10_y0<br>the first number, "10", indicates the wind speedthe second part, "_y0", the yaw misalignment, same as for BEM/BEVC
DOI: 10.5281/zenodo.15727813
title: Spanish Medical Residency Entrance Examination with LLMs
DOI: 10.15468/dl.wbm5vu
title: Occurrence Download

description: A dataset containing 846 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Inventaire des poissons de la lagune d'Ambavarano/Fort dauphin"
 ]
}

 The dataset includes 846 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066965-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15727825
title: fiocom current monthly data
DOI: 10.5281/zenodo.15727824
title: fiocom current monthly data
DOI: 10.15468/bfbkut
title: Historical occurrences of Punctelia subrudecta and Punctelia jeckeri in Czechia (Central Europe) based on specimens deposited at the Slovak National Museum - Natural History Museum (BRA)

description: The dataset portrays historical occurrences of Punctelia subrudecta and Punctelia jeckeri (lichens) in Czechia (Central Europe) based on specimens deposited at the Slovak National Museum - Natural History Museum (BRA). The specimens were collected between 1905 and 1978. The collectors were J. Suza, A. Vězda, J. Nádvorník, and J. Liška. All specimens were revised by Jiří Malíček (ORCID: 0000-0002-3119-8967) in 2024. The complementary footage (publicly available on ZENODO) was captured by Viktória Krajanová (ORCID: 0000-0002-0110-1415).
DOI: 10.34740/kaggle/dsv/12266776
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.26165/juelich-data/dngqoq
title: Replication Data for the study on polar HCl depletion

description: This data set is part of the study by Grooß et al. (2025). In this study, a discrepancy between model simulations and observations of HCl in the dark polar stratosphere (see here) is further investigated. Adding futher reactions to the standard model chemistry, especially the heterogeneous reaction of chlorine peroxide (Cl2O2) with hydrogen chloride (HCl) helps reducing this reported discrepancy significantly.
DOI: 10.15468/dl.g7845g
title: Occurrence Download

description: A dataset containing 12380 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mytilus galloprovincialis Lamarck, 1819"
 ]
}

 The dataset includes 12380 records from 228 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066967-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uaeccc
title: Occurrence Download

description: A dataset containing 397 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CHN.26.20.2_1",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 397 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066968-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15704312
title: Dataset for Motion Artifact Removal in GSR Signals from Smart Cane

description: Dataset for Motion Artifact Removal in GSR Signals from Smart Cane

This dataset accompanies the publication:



Villalba-Bravo, R., Trujillo-León, A., & Vidal-Verdú, F.Adaptive Filter to Remove Motion Artifacts from GSR Sensor Embedded on Handle CaneIEEE SENSORS 2024, Kobe, Japan, pp. 1–4.DOI: 10.1109/SENSORS60989.2024.10784798


Description

This dataset includes Galvanic Skin Response (GSR) signals recorded in an experiment involving a single participant walking on a treadmill while using a smart cane. The cane was equipped with both a galvanic skin response (GSR) sensor and a force-sensing resistor (FSR) . Motion artifacts caused by walking were recorded and filtered using an adaptive Notch filter whose center frequency was dynamically adjusted based on the FSR signal.

The dataset contains 4 sessions, each stored in its own folder.

Folder Structure

Each folder corresponds to a measurement session (e.g., Session_01/, Session_02/, etc.) and contains the following files:

Session_1/├── cane_FSR.txt├── noisy_cane_GSR.txt├── filtered_cane_GSR.txt└── ref_GSR.txt

...

Session_4/├── cane_FSR.txt├── noisy_cane_GSR.txt├── filtered_cane_GSR.txt└── ref_GSR.txt

File Descriptions

1. ref_GSR.txt





Source: Stationary reference sensor




Description: Clean GSR signal used as ground truth for motion-free skin conductance.




Format: CSV (2 columns, no headers)





timestamp: Unix timestamp (e.g., 1718353970.03125)




gsr_value: Reference GSR signal (artifact-free)





2. noisy_cane_GSR.txt





Source: GSR sensor embedded on the smart cane




Description: GSR signal contaminated by motion artifacts during walking.




Format: CSV (2 columns, no headers)





timestamp: Unix timestamp




gsr_value: Noisy GSR signal





3. cane_FSR.txt





Source: Force-sensing resistor (FSR) under GSR sensor




Description: Pressure data from the cane's handle, used to detect steps and model motion artifacts.




Format: CSV (2 columns, no headers)





timestamp: Unix timestamp




fsr_value: Force signal amplitude





4. filtered_cane_GSR.txt





Source: Filtered output from adaptive Notch filter




Description: GSR signal from the cane after motion artifact removal.




Format: CSV (2 columns, no headers)





timestamp: Unix timestamp




gsr_value: Filtered GSR signal





 

Data Format and Notes





All time values are recorded in Unix POSIX format with fractional seconds.



 

Citation

If you use this dataset, please cite:



R. Villalba-Bravo, A. Trujillo-León and F. Vidal-Verdú,Adaptive Filter to Remove Motion Artifacts from GSR Sensor Embedded on Handle Cane,2024 IEEE SENSORS, Kobe, Japan, pp. 1–4.DOI: 10.1109/SENSORS60989.2024.10784798
DOI: 10.5281/zenodo.15704313
title: Dataset for Motion Artifact Removal in GSR Signals from Smart Cane

description: Dataset for Motion Artifact Removal in GSR Signals from Smart Cane

This dataset accompanies the publication:



Villalba-Bravo, R., Trujillo-León, A., & Vidal-Verdú, F.Adaptive Filter to Remove Motion Artifacts from GSR Sensor Embedded on Handle CaneIEEE SENSORS 2024, Kobe, Japan, pp. 1–4.DOI: 10.1109/SENSORS60989.2024.10784798


Description

This dataset includes Galvanic Skin Response (GSR) signals recorded in an experiment involving a single participant walking on a treadmill while using a smart cane. The cane was equipped with both a galvanic skin response (GSR) sensor and a force-sensing resistor (FSR) . Motion artifacts caused by walking were recorded and filtered using an adaptive Notch filter whose center frequency was dynamically adjusted based on the FSR signal.

The dataset contains 4 sessions, each stored in its own folder.

Folder Structure

Each folder corresponds to a measurement session (e.g., Session_01/, Session_02/, etc.) and contains the following files:

Session_1/├── cane_FSR.txt├── noisy_cane_GSR.txt├── filtered_cane_GSR.txt└── ref_GSR.txt

...

Session_4/├── cane_FSR.txt├── noisy_cane_GSR.txt├── filtered_cane_GSR.txt└── ref_GSR.txt

File Descriptions

1. ref_GSR.txt





Source: Stationary reference sensor




Description: Clean GSR signal used as ground truth for motion-free skin conductance.




Format: CSV (2 columns, no headers)





timestamp: Unix timestamp (e.g., 1718353970.03125)




gsr_value: Reference GSR signal (artifact-free)





2. noisy_cane_GSR.txt





Source: GSR sensor embedded on the smart cane




Description: GSR signal contaminated by motion artifacts during walking.




Format: CSV (2 columns, no headers)





timestamp: Unix timestamp




gsr_value: Noisy GSR signal





3. cane_FSR.txt





Source: Force-sensing resistor (FSR) under GSR sensor




Description: Pressure data from the cane's handle, used to detect steps and model motion artifacts.




Format: CSV (2 columns, no headers)





timestamp: Unix timestamp




fsr_value: Force signal amplitude





4. filtered_cane_GSR.txt





Source: Filtered output from adaptive Notch filter




Description: GSR signal from the cane after motion artifact removal.




Format: CSV (2 columns, no headers)





timestamp: Unix timestamp




gsr_value: Filtered GSR signal





 

Data Format and Notes





All time values are recorded in Unix POSIX format with fractional seconds.



 

Citation

If you use this dataset, please cite:



R. Villalba-Bravo, A. Trujillo-León and F. Vidal-Verdú,Adaptive Filter to Remove Motion Artifacts from GSR Sensor Embedded on Handle Cane,2024 IEEE SENSORS, Kobe, Japan, pp. 1–4.DOI: 10.1109/SENSORS60989.2024.10784798
DOI: 10.5281/zenodo.15727826
title: Globally Convergent Accelerated Block Proximal Method with Adaptive Momentum for Nonconvex Optimization

description: This paper considers a class of multi-block nonconvex nonsmooth optimization problems, and this class of problems covers many applications of signal processing and

machine learning applications. We propose an accelerated block proximal linear method with adaptive momentum (ABPL+) to effectively tackle these challenges. The method evaluates both a proximal gradient step and a linear extrapolation step for updating each block of variables, opting for the one with the lower function value to maintain a monotonic decrease. The advantages of our method compared to previous approaches include: (1) allowing the extrapolation parameter to be independent of other parameters while utilizing an adaptive extrapolation parameter strategy, thereby improving stability and enhancing acceleration; (2) ensuring convergence and global

convergence while establishing the convergence rate, even when the extrapolation parameter is independent of other parameters; and (3) permitting the random selection of variable blocks for updates while maintaining global convergence. We evaluate our method by applying it to solve the ℓ0-norm constrained multilayer nonnegative matrix factorization and sparse nonnegative CP decomposition problems, which are known to be NP-hard in general. Numerical results demonstrate that our method outperforms state-of-the-art algorithms, highlighting its effectiveness and potential for broader applications.
DOI: 10.5281/zenodo.15727827
title: Globally Convergent Accelerated Block Proximal Method with Adaptive Momentum for Nonconvex Optimization

description: This paper considers a class of multi-block nonconvex nonsmooth optimization problems, and this class of problems covers many applications of signal processing and

machine learning applications. We propose an accelerated block proximal linear method with adaptive momentum (ABPL+) to effectively tackle these challenges. The method evaluates both a proximal gradient step and a linear extrapolation step for updating each block of variables, opting for the one with the lower function value to maintain a monotonic decrease. The advantages of our method compared to previous approaches include: (1) allowing the extrapolation parameter to be independent of other parameters while utilizing an adaptive extrapolation parameter strategy, thereby improving stability and enhancing acceleration; (2) ensuring convergence and global

convergence while establishing the convergence rate, even when the extrapolation parameter is independent of other parameters; and (3) permitting the random selection of variable blocks for updates while maintaining global convergence. We evaluate our method by applying it to solve the ℓ0-norm constrained multilayer nonnegative matrix factorization and sparse nonnegative CP decomposition problems, which are known to be NP-hard in general. Numerical results demonstrate that our method outperforms state-of-the-art algorithms, highlighting its effectiveness and potential for broader applications.
DOI: 10.15468/dl.sfh87v
title: Occurrence Download

description: A dataset containing 923 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((174.06785 -39.38417,174.13155 -39.36725,174.16539 -39.33633,174.17781 -39.29588,174.16803 -39.26417,174.14133 -39.23456,174.06541 -39.20634,174.01067 -39.19093,173.99525 -39.16701,173.99632 -39.14097,173.98409 -39.13406,173.97559 -39.13831,173.95486 -39.1314,173.93732 -39.15053,173.94795 -39.16967,173.96549 -39.19571,173.96655 -39.22388,173.9538 -39.2866,173.96655 -39.33922,174.00535 -39.37164,174.06785 -39.38417))",
 "TaxonKey is Coprosma J.R.Forst. & G.Forst.",
 "Year 1980-2025"
 ]
}

 The dataset includes 923 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066970-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25326/dmng-3592
title: Atmospheric simulation chamber study: Biomass Burning + NO3 - Aerosol study - physical properties - 2019-04-26
DOI: 10.15468/dl.bqz5e8
title: Occurrence Download

description: A dataset containing 135 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((174.11541 -39.36994,174.16788 -39.3271,174.16981 -39.26981,174.14285 -39.23804,174.05283 -39.2029,174.01288 -39.19519,173.99458 -39.1822,173.99458 -39.16053,173.99218 -39.13646,173.95126 -39.14898,173.95078 -39.17449,173.9604 -39.18797,173.97099 -39.20675,173.96762 -39.2467,173.95511 -39.27607,173.95703 -39.32036,174.00084 -39.36801,174.05379 -39.38149,174.11541 -39.36994))",
 "TaxonKey is Gaultheria Kalm ex L.",
 "Year 1980-2025"
 ]
}

 The dataset includes 135 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066971-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.