cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/5xswr8yp34
title: Dataset Supporting the Study Predicting the Structural Composition and Higher Heating Value of Agricultural and Municipal Solid Organic Waste Using Conditional Generative Adversarial Networks and Ensemble Learning

description: The data set is a collection of 257 unique data points collected from different sources literature around the world. Each Data point is composed of proximate, ultimate, structural properties and the High Heating Value(HHV) of biomass (agricultural and municipal solid organic waste). Proximate properties include the Ash content(Ash), Volatile Matter(VM), and Fixed Carbon(FC) of the biomass. Ultimate properties include the elemental Carbon(C), Hydrogen(H), Oxygen(O) and Sulphur(S). Structural/lignocellulosic composition including Cellulose(Cel), Hemicellulose(Hemi) and Lignin(Lig) is also included together with the HHV(MJ/kg). All the compositional columns are presented as percentage (wt%) on dry basis while HHV is presented in MJ/kg. The column “Biomass” names the material as described in the source (for example, rice husk, sugarcane bagasse, paper sludge, mixed food waste). The column “Source” provides the bibliographic reference or identifier from which the observation was extracted. Random missing values were imputed with k-nearest-neighbours to yield a complete, analysis-ready table; no generated (cGAN-produced) samples are stored here. The accompanying manuscript tests the hypothesis that lignocellulosic composition and heating value can be predicted from standard fuel analyses even with a small, heterogeneous data set and that accuracy improves when conditional generative adversarial networks are used to increase the training distribution and when multi-output ensemble regressors using chained targets are used to capitalize on cross-property correlations. Users need to read totals accordingly: ash + volatile matter + fixed carbon is approximately 100 wt% (dry), elemental totals are slightly less than 100 wt% due to occasional by-difference oxygen, and cellulose + hemicellulose + lignin do not necessarily sum to 100 wt% because extractives or minor constituents are sometimes reported separately by some sources. The descriptive statistics cited in our article refer to a winsorised copy used for robustness checks; the file deposited here is the harmonized, literature-derived dataset.
DOI: 10.17632/5xswr8yp34.2
title: Dataset Supporting the Study Predicting the Structural Composition and Higher Heating Value of Agricultural and Municipal Solid Organic Waste Using Conditional Generative Adversarial Networks and Ensemble Learning

description: The data set is a collection of 257 unique data points collected from different sources literature around the world. Each Data point is composed of proximate, ultimate, structural properties and the High Heating Value(HHV) of biomass (agricultural and municipal solid organic waste). Proximate properties include the Ash content(Ash), Volatile Matter(VM), and Fixed Carbon(FC) of the biomass. Ultimate properties include the elemental Carbon(C), Hydrogen(H), Oxygen(O) and Sulphur(S). Structural/lignocellulosic composition including Cellulose(Cel), Hemicellulose(Hemi) and Lignin(Lig) is also included together with the HHV(MJ/kg). All the compositional columns are presented as percentage (wt%) on dry basis while HHV is presented in MJ/kg. The column “Biomass” names the material as described in the source (for example, rice husk, sugarcane bagasse, paper sludge, mixed food waste). The column “Source” provides the bibliographic reference or identifier from which the observation was extracted. Random missing values were imputed with k-nearest-neighbours to yield a complete, analysis-ready table; no generated (cGAN-produced) samples are stored here. The accompanying manuscript tests the hypothesis that lignocellulosic composition and heating value can be predicted from standard fuel analyses even with a small, heterogeneous data set and that accuracy improves when conditional generative adversarial networks are used to increase the training distribution and when multi-output ensemble regressors using chained targets are used to capitalize on cross-property correlations. Users need to read totals accordingly: ash + volatile matter + fixed carbon is approximately 100 wt% (dry), elemental totals are slightly less than 100 wt% due to occasional by-difference oxygen, and cellulose + hemicellulose + lignin do not necessarily sum to 100 wt% because extractives or minor constituents are sometimes reported separately by some sources. The descriptive statistics cited in our article refer to a winsorised copy used for robustness checks; the file deposited here is the harmonized, literature-derived dataset.
DOI: 10.17632/dnf632d5jd
title: Replication code for Inequality and transmission channels of monetary policy in a macro agent-based model

description: The following folder contains the ReadMe file and the code file needed to run the code and replicate the results of our work entitled "Inequality and transmission channels of monetary policy in a macro agent-based model".
DOI: 10.17632/dnf632d5jd.1
title: Replication code for Inequality and transmission channels of monetary policy in a macro agent-based model

description: The following folder contains the ReadMe file and the code file needed to run the code and replicate the results of our work entitled "Inequality and transmission channels of monetary policy in a macro agent-based model".
DOI: 10.17632/9hrzhs5z6f
title: PIV and AMS data by Kong

description: Table S1-S2: AMS measurements were carried out at the Paleomagnetism and Environmental Magnetism Laboratory, China University of Geosciences, Beijing, and conducted using a MFK1-FA Kappabridge (Agico Inc.) with a typical sensitivity of ∼2.0 × 10 −8 SI, (Jelinek, 1981). The AMS data were processed using the ANISOFT 5 software and presented by polar stereographic projection based on geographic directions in the model, with the movable baffle defined as East. Table S1. and Table S2. show the data obtained after bedding correction in Anisoft 5.0 software after measuring all the samples using the MFK1-FA Kappabridge. 
Figure S1-S12: This Supporting Information provides the original PIV camera photographs corresponding to the line-drawing images shown in Figure 6(a)–(g) and Figure 8(a)-(e) of the main text. These images were obtained during the deformation process of Model 1 and Model 2 using a Particle Image Velocimetry (PIV) system. Due to the relatively low resolution and high grayscale characteristics of the PIV camera, the raw images are not very clear. Therefore, for clarity and interpretability, line drawings were generated using CorelDraw and included in the main text. Here, Figures S1–S12 present the unprocessed photographs at each experimental stage as supplementary reference material.
DOI: 10.15468/dl.9789xa
title: Occurrence Download

description: A dataset containing 81 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gryllodes sigillatus",
 "Country is Netherlands",
 "HasCoordinate is true"
 ]
}

 The dataset includes 81 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004225-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/9hrzhs5z6f.1
title: PIV and AMS data by Kong

description: Table S1-S2: AMS measurements were carried out at the Paleomagnetism and Environmental Magnetism Laboratory, China University of Geosciences, Beijing, and conducted using a MFK1-FA Kappabridge (Agico Inc.) with a typical sensitivity of ∼2.0 × 10 −8 SI, (Jelinek, 1981). The AMS data were processed using the ANISOFT 5 software and presented by polar stereographic projection based on geographic directions in the model, with the movable baffle defined as East. Table S1. and Table S2. show the data obtained after bedding correction in Anisoft 5.0 software after measuring all the samples using the MFK1-FA Kappabridge. 
Figure S1-S12: This Supporting Information provides the original PIV camera photographs corresponding to the line-drawing images shown in Figure 6(a)–(g) and Figure 8(a)-(e) of the main text. These images were obtained during the deformation process of Model 1 and Model 2 using a Particle Image Velocimetry (PIV) system. Due to the relatively low resolution and high grayscale characteristics of the PIV camera, the raw images are not very clear. Therefore, for clarity and interpretability, line drawings were generated using CorelDraw and included in the main text. Here, Figures S1–S12 present the unprocessed photographs at each experimental stage as supplementary reference material.
DOI: 10.5886/n83nry
title: First record of Diphasiastrum complanatum (L.) Holub for Baffin Island (Nunavut)

description: Specimen data to accompany: Sokoloff P, Abujayyab S, Anilniliak J, Alivaktuk A, Melchers G, Meness D, Ottokie M, Sanguin M, Sanguin R, Sibley S, Sullivan S, Tucker A, Watsko DJ. 2025. ᓯᕗᓪᓕᖅᐹᒥᒃ ᑎᑎᕋᖅᑕᐅᖃᓯᐅᑎᕗᑦ ᑲᑭᓪᓚᓇᕐᑐᖅ ᕿᑭᖅᑖᓗᖕᒥ (ᓄᓇᕗᑦ) – First record of Diphasiastrum complanatum (L.) Holub for Baffin Island (Nunavut). Specimen 47. https://doi.org/10.56222/28166531.2025.47
DOI: 10.15468/dl.6pg5yp
title: Occurrence Download

description: A dataset containing 27072926 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Zenaida macroura)",
 "Country is one of (Bermuda, Canada, Greenland, Saint Pierre and Miquelon, United States of America)"
 ]
}

 The dataset includes 27072926 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004150-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/428ysdybvd
title: Field-captured dataset of banana plants across growth stages and early disease conditions

description: The dataset comprises of 1,090 high-resolution images of banana plants that were collected under natural lighting conditions in the field with a digital camera (smartphone) from a plantation in Solapur, Maharashtra, India. The underlying research hypothesis for the collection is that a sufficiently diverse collection of raw field images will provide a basis for developing reliable computational vision and machine learning tools for early detection of plant diseases and growth stage analysis of banana crops. The banana plants shown in these photo images were collected at different growth stages and quality, including saplings, raw bananas with flower, fully grown healthy plants, and plants showing early recognizable signs of disease with visible leaf spots, discoloration, or wilting. The photo images are provided in raw format, left unprocessed and are stored in JPG format with variable pixel resolutions. By purposefully leaving the dataset unlabeled and unfiltered, the full pre-processing, annotating, and structuring of the data for the user's research is at the users discretion, so as to limit curator bias. There are several unique features of this dataset including its authenticity (being collected directly in the field), size (over 1,000 images), and richness (different growth stages, healthy and diseased).The dataset can be interpreted and reused in multiple ways, such as for training deep learning models for disease classification, evaluating growth stages in crop monitoring systems, or serving as benchmark data for precision agriculture applications. Ultimately, this dataset is intended to support research that contributes to sustainable farming practices through timely disease detection and improved crop management.
DOI: 10.17632/428ysdybvd.1
title: Field-captured dataset of banana plants across growth stages and early disease conditions

description: The dataset comprises of 1,090 high-resolution images of banana plants that were collected under natural lighting conditions in the field with a digital camera (smartphone) from a plantation in Solapur, Maharashtra, India. The underlying research hypothesis for the collection is that a sufficiently diverse collection of raw field images will provide a basis for developing reliable computational vision and machine learning tools for early detection of plant diseases and growth stage analysis of banana crops. The banana plants shown in these photo images were collected at different growth stages and quality, including saplings, raw bananas with flower, fully grown healthy plants, and plants showing early recognizable signs of disease with visible leaf spots, discoloration, or wilting. The photo images are provided in raw format, left unprocessed and are stored in JPG format with variable pixel resolutions. By purposefully leaving the dataset unlabeled and unfiltered, the full pre-processing, annotating, and structuring of the data for the user's research is at the users discretion, so as to limit curator bias. There are several unique features of this dataset including its authenticity (being collected directly in the field), size (over 1,000 images), and richness (different growth stages, healthy and diseased).The dataset can be interpreted and reused in multiple ways, such as for training deep learning models for disease classification, evaluating growth stages in crop monitoring systems, or serving as benchmark data for precision agriculture applications. Ultimately, this dataset is intended to support research that contributes to sustainable farming practices through timely disease detection and improved crop management.
DOI: 10.17632/m268jsw339.2
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). After this normalization, the first value of the array is discarded because it is complete noise, which results in the final array (the published spectrum.csv) having 1X at the 3002 position and 2X at 6005. 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
DOI: 10.17632/46sggj2sdk
title: DUT/DCIS/507

description: The DUT/DCIS/507 database comprises of 482 compression piles and 25 tension piles under axial loading, respectively. The data are digitized from 38 published sources, including master and doctoral dissertations, technical brochures and reports, journal papers and conference proceedings. The DUT/DCIS/507 database contains (1) subsurface data at or around the test pile (e.g., site locaiton and layering, in-situ and laboratory testing), (2) test pile data (e.g., type, shaft and base diameters, length) and (3) load test data (e.g., loading procedure, combined load-displacement curve, a few tests with shaft and base load-displacement curves and load-strain records).
DOI: 10.17632/46sggj2sdk.1
title: DUT/DCIS/507

description: The DUT/DCIS/507 database comprises of 482 compression piles and 25 tension piles under axial loading, respectively. The data are digitized from 38 published sources, including master and doctoral dissertations, technical brochures and reports, journal papers and conference proceedings. The DUT/DCIS/507 database contains (1) subsurface data at or around the test pile (e.g., site locaiton and layering, in-situ and laboratory testing), (2) test pile data (e.g., type, shaft and base diameters, length) and (3) load test data (e.g., loading procedure, combined load-displacement curve, a few tests with shaft and base load-displacement curves and load-strain records).
DOI: 10.17632/4mtg877dpd
title: E region Drift Analysis Data Sets of ST Radar

description: Drift data plotted from ST radar in ionopsheric mode
DOI: 10.17632/4mtg877dpd.1
title: E region Drift Analysis Data Sets of ST Radar

description: Drift data plotted from ST radar in ionopsheric mode
DOI: 10.15468/dl.u2wvpg
title: Occurrence Download

description: A dataset containing 677 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Anura",
 "GadmGid is IDN.23_1",
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Material citation, Specimen, Occurrence evidence)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 677 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004227-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/p2chr3ntth
title: Evaluating the ability of satellite remote sensing to detect changes in actual crop evapotranspiration under contrasting field management practices in the Czech Republic

description: This dataset includes csv. and R data files used for the evaluation of TSEB and METRIC remote sensing models against eddy covariance (EC) measurements. The data were collected at the Polkovice experimental field site in the Czech Republic and include actual crop evapotranspiration (ETa) estimates, meteorological inputs, model outputs, and EC observations.
DOI: 10.17632/p2chr3ntth.2
title: Evaluating the ability of satellite remote sensing to detect changes in actual crop evapotranspiration under contrasting field management practices in the Czech Republic

description: This dataset includes csv. and R data files used for the evaluation of TSEB and METRIC remote sensing models against eddy covariance (EC) measurements. The data were collected at the Polkovice experimental field site in the Czech Republic and include actual crop evapotranspiration (ETa) estimates, meteorological inputs, model outputs, and EC observations.
DOI: 10.17632/mxcjwsyfw2
title: DataSet_The Impactof Territories on the Salaries of Young individuals

description: The paper used two databases. The first one (GénérationData for individuals) has confidentiality restrictions and it is available on query by researchers to the following plateform https://data.progedo.fr/studies/doi/10.13144/lil-1439. The second one, proposed by the "Observatoire des Territoires" (government institution), is given in the four excel folders, two for 2013 and two for 2017 years. All excel files give some variable informations by employment zones (EZ in the paper). EZ are defined in the first column of each folders named CA0AZE_YEAR. "Median Wages" file gives median wages for each EZ for both year in two separate sheets. The two others files detail the rest of macroeconomic variables of EZ following the given year.
DOI: 10.17632/mxcjwsyfw2.2
title: DataSet_The Impactof Territories on the Salaries of Young individuals

description: The paper used two databases. The first one (GénérationData for individuals) has confidentiality restrictions and it is available on query by researchers to the following plateform https://data.progedo.fr/studies/doi/10.13144/lil-1439. The second one, proposed by the "Observatoire des Territoires" (government institution), is given in the four excel folders, two for 2013 and two for 2017 years. All excel files give some variable informations by employment zones (EZ in the paper). EZ are defined in the first column of each folders named CA0AZE_YEAR. "Median Wages" file gives median wages for each EZ for both year in two separate sheets. The two others files detail the rest of macroeconomic variables of EZ following the given year.
DOI: 10.17632/hssshh2pbd
title: Enforcing a high success percentage in motor learning

description: Our research hypothesis was that performing a reward-based motor learning task at a high success percentage induces more motivation but less motor learning compared to performing the task at a moderate success percentage. The data show that participants in a high success group (SFgroup = 0.8) showed less motor learning compared to participants in a moderate success group (SFgroup = 0.5). There was no difference in motivation between groups.
Data were gathered with a circle drawing task performed with a wacom tablet and laptop and illustrated in a youtube video: https://youtu.be/KK9H-qQFIrA?si=5yy6DCYCFCOb-RMk

The variables are as follows: 
pp: participant number
block: block of five trials
error: deviation from the target circle size in the circle drawing task
SFgroup: success percentage condition
age: age in years
gender: gender
mot: mean score on a rating of enjoyment of the task and motivation to continue
learning: decrease in error in the circle drawing task
noncircular: trials defined as not drawing a circle
success_percentage: mean success in a block of 5 trials

Describe your research hypothesis, what your data shows, any notable findings and how the data can be interpreted. Please add sufficient description to enable others to understand what the data is, how it was gathered and how to interpret and use it.