cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/gbc8czckgj.1
title: F, Cl, and H2O in nominally anhydrous minerals and hydrous minerals from lithospheric mantle xenoliths

description: These datasets include nominally anhydrous mineral (NAM; clinopyroxene, orthopyroxene, and olivine) major and minor element compositions, as well as F, Cl, and H2O concentrations in NAMs and F and Cl concentrations in hydrous minerals. Samples analyzed are peridotite xenoliths of the North American sub-continental lithospheric mantle (SCLM) from the Navajo Volcanic Field (NVF) and Cerro Chato on the Colorado Plateau and Elephant Butte and Kilbourne Hole along the southern Rio Grande Rift, as well as one peridotite xenolith from O'ahu, Hawaii. Previous studies have suggested that the North American SCLM has been metasomatized by fluids derived from the subducted Farallon plate. In the modally hydrated NVF xenoliths, hydrous minerals contain the vast majority of water and halogens. Pyroxenes in these xenoliths are F-poor but variably enriched in H2O and Cl, suggesting metasomatism via a hydrous fluid. Additionally, samples with anomalously high water contents have variations in Cl content across the grains that resemble diffusion profiles but do not have such variations in H2O or F. This suggests recent volatile enrichment leading to Cl heterogeneity in a subset of samples. 

In the modally anhydrous xenoliths, F is enriched in the NAMs and correlates with indices of melt depletion, whereas Cl is depleted and does not correlate with indices of melt depletion or metasomatism.
DOI: 10.17632/5t67mjc936
title: NodulesData2025

description: Field pea plants were grown in winter (July to August) and spring (October to November) of 2023, outdoor under a rain-out shelter at the National University of Entre Ríos, Oro Verde, Argentina (31°49'50.0"S 60°31'14.6"W). Spring type, yellow-seed field pea cv Reussite was inoculated with Rhizobium leguminosarum biovar viceae (Rizobacter, Argentina) at 4 ml per kg seed and treated with systemic fungicide (Fludioxonil 2.5 g, Metalaxil-M 2.0 g and Tiabendazol 15 g) at 1 ml per kg seed. Starting N-NO3- content of the soil was 70.8 mg N kg⁻¹ soil in winter and 30.3 mg N kg⁻¹ soil in spring.
Seeds were sown in 5-l pots filled with a mixture of local top-soil Acquic and Vertic Argiudoll of the Asociación Oro Verde serie (Plan Mapa de Suelos, 1998, Soil Survey Staff, 2014). Upper and lower limits of water holding capacity were assessed in a soil sample (Wilson et al., 2013).
Water regimes were established targeting three levels of plant available water (PAW) in soil: 60%, 85%, and 100%; the premise is that 60 % PAW would cause drought stress, 100 % PAW may cause hypoxia stress but not anoxia, and 85 % PAW would allow for potential plant growth (Sadras and Milroy, 1996). At the V3 stage, treatments were initiated adjusting irrigation with a programmed controller (Rain Bird ESP-RZXe, USA). Irrigation frequencies were periodically monitored gravimetrically to keep actual soil moisture at ±5% of the target treatments. Water treatments were allocated to a completely randomized design with four replicates and each replicate consisted of two plants per pot.
DOI: 10.17632/5t67mjc936.1
title: NodulesData2025

description: Field pea plants were grown in winter (July to August) and spring (October to November) of 2023, outdoor under a rain-out shelter at the National University of Entre Ríos, Oro Verde, Argentina (31°49'50.0"S 60°31'14.6"W). Spring type, yellow-seed field pea cv Reussite was inoculated with Rhizobium leguminosarum biovar viceae (Rizobacter, Argentina) at 4 ml per kg seed and treated with systemic fungicide (Fludioxonil 2.5 g, Metalaxil-M 2.0 g and Tiabendazol 15 g) at 1 ml per kg seed. Starting N-NO3- content of the soil was 70.8 mg N kg⁻¹ soil in winter and 30.3 mg N kg⁻¹ soil in spring.
Seeds were sown in 5-l pots filled with a mixture of local top-soil Acquic and Vertic Argiudoll of the Asociación Oro Verde serie (Plan Mapa de Suelos, 1998, Soil Survey Staff, 2014). Upper and lower limits of water holding capacity were assessed in a soil sample (Wilson et al., 2013).
Water regimes were established targeting three levels of plant available water (PAW) in soil: 60%, 85%, and 100%; the premise is that 60 % PAW would cause drought stress, 100 % PAW may cause hypoxia stress but not anoxia, and 85 % PAW would allow for potential plant growth (Sadras and Milroy, 1996). At the V3 stage, treatments were initiated adjusting irrigation with a programmed controller (Rain Bird ESP-RZXe, USA). Irrigation frequencies were periodically monitored gravimetrically to keep actual soil moisture at ±5% of the target treatments. Water treatments were allocated to a completely randomized design with four replicates and each replicate consisted of two plants per pot.
DOI: 10.15468/dl.5bnu23
title: Occurrence Download

description: A dataset containing 108 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chama lazarus",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Occurrence evidence)",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is one of (Absent)"
 ]
 }
 }
 ]
}

 The dataset includes 108 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008448-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/2mjfh8zjzd
title: Integrated Google Traffic and Field Speed Dataset for Flood-Prone Urban Areas: Loja, Ecuador (2025)

description: Research Hypothesis
This comprehensive dataset was developed to test whether integrating Google Traffic data with field measurements can improve urban traffic modeling accuracy in medium-sized cities with incomplete digital coverage.

Dataset Composition
The dataset comprises two complementary components: (1) Field Speed Measurements: 1,501 georeferenced vehicle speed observations collected via Survey123 mobile application between July 7-18, 2025 (07:00-24:00), and (2) Google Traffic Observations: typical traffic condition records collected across a complete weekly cycle using Google Traffic's color-coded system. Combined, these datasets provide comprehensive spatial and temporal coverage of traffic patterns in Loja, Ecuador's flood-vulnerable areas.

Data Collection Methodology
Field data collection involved GPS-enabled smartphones recording vehicle speeds, coordinates, and timestamps while operators traveled through flood-prone streets in taxis and personal vehicles. Google Traffic data acquisition used systematic manual observation of color-coded traffic states (Green, Yellow, Orange, Red, None) at predetermined coordinates across all days and hourly intervals. Both datasets employed WGS84 coordinate system and covered the same geographic study area defined by flood vulnerability and incomplete Google Traffic coverage.

Data Quality
Field measurements underwent quality control, removing six invalid records. The remaining validated speeds, ranging from 0 to 70 km/h. Google Traffic observations were successfully assigned to specific road networks. An integration analysis revealed that adjustment factors for Google Traffic data systematically either undershoot or overshoot the ground speed data. These adjustments varied by road type and time period, highlighting the need for local calibration of data from digital platforms.

Notable Findings
The dataset reveals significant coverage gaps in Google Traffic for medium-sized cities. A speed distribution analysis shows that most field measurements fall between 10 and 40 km/h, which is consistent with typical urban traffic patterns. Temporal analysis identified periods of peak data density.

Applications and Interpretation
This hybrid dataset allows for the development of calibrated traffic models for emergency planning, particularly for flood scenario analysis. Researchers can calculate platform-specific bias corrections, develop integrated routing algorithms, and validate commercial traffic platforms in similar urban contexts. The comprehensive temporal coverage supports the analysis of congestion patterns, while the spatial precision enables the integration of infrastructure mapping. The data structure is designed to be easily replicated in other medium-sized cities that face similar digital coverage limitations and climate-related mobility challenges.
DOI: 10.17632/2mjfh8zjzd.1
title: Integrated Google Traffic and Field Speed Dataset for Flood-Prone Urban Areas: Loja, Ecuador (2025)

description: Research Hypothesis
This comprehensive dataset was developed to test whether integrating Google Traffic data with field measurements can improve urban traffic modeling accuracy in medium-sized cities with incomplete digital coverage.

Dataset Composition
The dataset comprises two complementary components: (1) Field Speed Measurements: 1,501 georeferenced vehicle speed observations collected via Survey123 mobile application between July 7-18, 2025 (07:00-24:00), and (2) Google Traffic Observations: typical traffic condition records collected across a complete weekly cycle using Google Traffic's color-coded system. Combined, these datasets provide comprehensive spatial and temporal coverage of traffic patterns in Loja, Ecuador's flood-vulnerable areas.

Data Collection Methodology
Field data collection involved GPS-enabled smartphones recording vehicle speeds, coordinates, and timestamps while operators traveled through flood-prone streets in taxis and personal vehicles. Google Traffic data acquisition used systematic manual observation of color-coded traffic states (Green, Yellow, Orange, Red, None) at predetermined coordinates across all days and hourly intervals. Both datasets employed WGS84 coordinate system and covered the same geographic study area defined by flood vulnerability and incomplete Google Traffic coverage.

Data Quality
Field measurements underwent quality control, removing six invalid records. The remaining validated speeds, ranging from 0 to 70 km/h. Google Traffic observations were successfully assigned to specific road networks. An integration analysis revealed that adjustment factors for Google Traffic data systematically either undershoot or overshoot the ground speed data. These adjustments varied by road type and time period, highlighting the need for local calibration of data from digital platforms.

Notable Findings
The dataset reveals significant coverage gaps in Google Traffic for medium-sized cities. A speed distribution analysis shows that most field measurements fall between 10 and 40 km/h, which is consistent with typical urban traffic patterns. Temporal analysis identified periods of peak data density.

Applications and Interpretation
This hybrid dataset allows for the development of calibrated traffic models for emergency planning, particularly for flood scenario analysis. Researchers can calculate platform-specific bias corrections, develop integrated routing algorithms, and validate commercial traffic platforms in similar urban contexts. The comprehensive temporal coverage supports the analysis of congestion patterns, while the spatial precision enables the integration of infrastructure mapping. The data structure is designed to be easily replicated in other medium-sized cities that face similar digital coverage limitations and climate-related mobility challenges.
DOI: 10.15468/dl.j97cpv
title: Occurrence Download

description: A dataset containing 112 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phalaris minor",
 "Country is Netherlands",
 "HasCoordinate is true"
 ]
}

 The dataset includes 112 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008449-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/yrvjhj284s
title: Study on the acidification mitigation mechanism of anaerobic digestion under different temperature conditions

description: This data is obtained from the methane production process via anaerobic digestion at different temperatures, including data on biogas yield, volatile fatty acids (VFAs), enzyme activity, and thermodynamics.
DOI: 10.17632/yrvjhj284s.1
title: Study on the acidification mitigation mechanism of anaerobic digestion under different temperature conditions

description: This data is obtained from the methane production process via anaerobic digestion at different temperatures, including data on biogas yield, volatile fatty acids (VFAs), enzyme activity, and thermodynamics.
DOI: 10.17632/7dg667fkrd
title: Lahar Seismic Monitoring Dataset for Volcan de Fuego, Guatemala

description: This dataset contains the materials accompanying the first publication of this study on lahar activity at Volcán de Fuego, Guatemala. It includes three main components:
 1. Processing Scripts: Python scripts that implement the preprocessing and analysis steps used in the study. The code covers reading raw seismic data, detrending, tapering, resampling, filtering, extracting features relevant to lahar detection, and training and application of KNN-based classification models
 2. Seismic Data: Waveform files (in miniSEED format) representing individual lahar events recorded at multiple seismic stations. Each event is organized by station, with continuous three-component or vertical-only streams depending on sensor availability. These data provide the basis for the time- and frequency-domain analyses presented in the publication.
 3. Multimedia: A small set of short video clips illustrating representative lahar events in the field, aligned with the seismic records to provide visual context for flow behavior and signal characteristics.
Access the README.md file for more detailed information about the contents of this repository.
DOI: 10.17632/7dg667fkrd.1
title: Lahar Seismic Monitoring Dataset for Volcan de Fuego, Guatemala

description: This dataset contains the materials accompanying the first publication of this study on lahar activity at Volcán de Fuego, Guatemala. It includes three main components:
 1. Processing Scripts: Python scripts that implement the preprocessing and analysis steps used in the study. The code covers reading raw seismic data, detrending, tapering, resampling, filtering, extracting features relevant to lahar detection, and training and application of KNN-based classification models
 2. Seismic Data: Waveform files (in miniSEED format) representing individual lahar events recorded at multiple seismic stations. Each event is organized by station, with continuous three-component or vertical-only streams depending on sensor availability. These data provide the basis for the time- and frequency-domain analyses presented in the publication.
 3. Multimedia: A small set of short video clips illustrating representative lahar events in the field, aligned with the seismic records to provide visual context for flow behavior and signal characteristics.
Access the README.md file for more detailed information about the contents of this repository.
DOI: 10.17632/9g6ghsh8ws
title: Amino acids activate parallel chemosensory pathways in Drosophila

description: Data accompanying the publication, "Amino acids activate parallel chemosensory pathways in Drosophila"
DOI: 10.17632/9g6ghsh8ws.1
title: Amino acids activate parallel chemosensory pathways in Drosophila

description: Data accompanying the publication, "Amino acids activate parallel chemosensory pathways in Drosophila"
DOI: 10.17632/bfhr7jsnv6
title: Relationships between physical activity, self-acceptance, and body perception in Peruvian university students

description: The dataset corresponds to the study “Relationships between physical activity, self-acceptance, and body perception in Peruvian university students: an analysis using PLS-SEM structural equations” and includes an Excel file with the questionnaire administered and the results obtained from 317 university students. The data were collected using validated instruments such as the IPAQ (International Physical Activity Questionnaire), the MBSRQ-M (Multidimensional Body Self Relations Questionnaire), and the EBP-M (Psychological Well-Being Scale), and are coded on a Likert scale from 1 to 5. The file contains both the complete questionnaire and the database with the responses organized by dimensions of analysis: self-acceptance, personal growth, subjective importance of physical appearance, and subjective importance of physical condition. The dataset is anonymized and does not contain any personally identifiable information, ensuring the confidentiality of the participants.
DOI: 10.17632/bfhr7jsnv6.1
title: Relationships between physical activity, self-acceptance, and body perception in Peruvian university students

description: The dataset corresponds to the study “Relationships between physical activity, self-acceptance, and body perception in Peruvian university students: an analysis using PLS-SEM structural equations” and includes an Excel file with the questionnaire administered and the results obtained from 317 university students. The data were collected using validated instruments such as the IPAQ (International Physical Activity Questionnaire), the MBSRQ-M (Multidimensional Body Self Relations Questionnaire), and the EBP-M (Psychological Well-Being Scale), and are coded on a Likert scale from 1 to 5. The file contains both the complete questionnaire and the database with the responses organized by dimensions of analysis: self-acceptance, personal growth, subjective importance of physical appearance, and subjective importance of physical condition. The dataset is anonymized and does not contain any personally identifiable information, ensuring the confidentiality of the participants.
DOI: 10.17632/vv3jxvyfvh
title: LS-MAX-QAP Testing Data

description: Test instance results for the corresponding manuscript, with abstract as follows. "This research examines a new variant of the maximal quadratic assignment problem, motivated by the challenge of assigning students to a series of project groups of fixed sizes over the duration of an academic course. The primary objective is to maximize the number of unique collaboration experiences, as measured by pairwise student interactions, subject to minor restrictions to prevent group homogeneity. A secondary objective is to minimize the maximum number of repeated interactions between any pair of students, as a method to balance such experiences. This research models this problem as a lexicographic, mixed-integer linear program, and it proposes three exact solution methods and three corresponding heuristics based on model decomposition to improve scalability. Testing assessed each method’s relative efficacy and computational efficiency using 900 synthetic instances and a larger, real-world case study. Results show that, whereas exact methods struggle with larger instances, the heuristic approaches deliver stronger empirical performance. In particular, the preemptive weighting approach yields the best outcomes for lexicographic optimization across the two objectives. Notably, each heuristic produced high-quality solutions for the large-scale instances within 4 hours of computational effort, whereas exact methods performed poorly, even with up to 16 hours of effort."
DOI: 10.17632/vv3jxvyfvh.1
title: LS-MAX-QAP Testing Data

description: Test instance results for the corresponding manuscript, with abstract as follows. "This research examines a new variant of the maximal quadratic assignment problem, motivated by the challenge of assigning students to a series of project groups of fixed sizes over the duration of an academic course. The primary objective is to maximize the number of unique collaboration experiences, as measured by pairwise student interactions, subject to minor restrictions to prevent group homogeneity. A secondary objective is to minimize the maximum number of repeated interactions between any pair of students, as a method to balance such experiences. This research models this problem as a lexicographic, mixed-integer linear program, and it proposes three exact solution methods and three corresponding heuristics based on model decomposition to improve scalability. Testing assessed each method’s relative efficacy and computational efficiency using 900 synthetic instances and a larger, real-world case study. Results show that, whereas exact methods struggle with larger instances, the heuristic approaches deliver stronger empirical performance. In particular, the preemptive weighting approach yields the best outcomes for lexicographic optimization across the two objectives. Notably, each heuristic produced high-quality solutions for the large-scale instances within 4 hours of computational effort, whereas exact methods performed poorly, even with up to 16 hours of effort."
DOI: 10.17632/m268jsw339
title: ESPset

description: A real-world dataset for vibration-based fault diagnosis of electric submersible pumps used on offshore oil exploration. The ESPset dataset is a collection of vibration signals acquired from accelerometers strategically attached onto the components of Electrical Submersible Centrifugal Pumps (ESP). An ESP belong to a class of equipment used in the extraction and exploration of oil and gas subject to severe working conditions. An ESP system consists of a coupled set of one or more electric motors, pumps and protectors.

# spectrum.csv
This csv file is a matrix of 6032 lines and 12103 columns, whose values are float numbers separated by a ';'. Each line of this file contains the spectrum of a single vibration signal collected from a sensor at a specific test condition of the ESP. Each value is the amplitude in inches per second (velocity) at a specific frequency. Each signal is normalized by the rotation frequency at which the ESP operates, in such a way that the amplitude with respect to the rotation frequency is always at the same position for all signal arrays. For instance, the normalization is made such that the 3003rd position of the array is always the 1x harmonic. Consequently, the 6006th is the second harmonic (2X). Hence, the frequency resolution is `1/3003~0.000333`

# features.csv
This csv file of 6033 lines (one line for each signal + a header) contains some features and the labels for all signals:
- esp_id: The id of the ESP.
- label: The classification label.
Let X be defined as the rotation frequency of the ESP. Each feature is defined as:

- median(8,13): Median of the amplitudes in the interval (8% X, 13% X);
- median(98,102): Median of the amplitudes in the interval (98% X, 102% X);
- a: Coefficient a of the exponential regression of type e^(a*A+b) where A is an array of equally separated relative frequencies up to 0.4, excluding zero. Example: A=(0.01, 0.02, ..., 0.39, 0.4).
- b: Coefficient b of the exponential regression of type e^(a*A+b) where A is an array of equally separated relative frequencies up to 0.4, excluding zero. Example: A=(0.01, 0.02, ..., 0.39, 0.4).
- peak1x: Amplitude in X;
- peak2x: Amplitude in 2X;
- rms(98,102): Root mean square of the amplitudes in the interval (98% X, 102% X).

For more information and code, take a look at https://github.com/NINFA-UFES/ESPset