cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/m268jsw339.3
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
DOI: 10.25583/2588622
title: Dataset from 3D FLASH Computation of National Ignition Facility Shot N210201-001

description: This data comes from a 3D FLASH [Fryxell et al. ApJS 131, 273 (2000); Tzeferacos et al. HEDP 17, 24-31(2015)] computation of a cylindrical implosion experiment performed at the National Ignition Facility (shot number N210201-001). FLASH is an open-access radiation-hydrodynamics code being used to simulate high-energy-density systems.
DOI: 10.17632/xn3ss7ghmm
title: BRD3 in sepsis

description: Raw data of WB
DOI: 10.17632/xn3ss7ghmm.1
title: BRD3 in sepsis

description: Raw data of WB
DOI: 10.17632/8fng4srkz3
title: Pearson and Spearman coefficients from a parametric study

description: This document contains figures with Pearson and Spearman coefficients obtained in a parametric study.
DOI: 10.17632/8fng4srkz3.2
title: Pearson and Spearman coefficients from a parametric study

description: This document contains figures with Pearson and Spearman coefficients obtained in a parametric study.
DOI: 10.15468/dl.tqzjek
title: Occurrence Download

description: A dataset containing 212 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chama macerophylla",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Occurrence evidence)",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is one of (Absent)"
 ]
 }
 }
 ]
}

 The dataset includes 212 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008450-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/cgrzvk5dmt.2
title: Evaluation of the Value Network Reference Model in 48 small restaurants in Bogotá (survey results)

description: This dataset shows the primary data obtained through the application of the Value Network Reference Model (VNRM) [1] survey in 48 small restaurants in Bogotá. This model allows determining the Value Network Level as a measure of business competitiveness in supply chain environments. The VNRM survey consists of 121 questions categorized on a five (5) point Likert scale. From these primary data, a neural network model was defined and trained to predict this indicator directly from the seven (7) most significant predictors.
[1] J. A. Acevedo-Suárez et al., "Integrated supply chain management model," Anales de la Academia de Ciencias de Cuba, Article vol. 5, no. 3, 2015. https://www.scopus.com/inward/record.uri?eid=2-s2.0- 5197238723&partnerID=40&md5=b41fedcad504df014852eba440a9dbb7
DOI: 10.17632/hgp48w7fyc
title: Thermal Comfort in Vernacular Architecture

description: En español
La arquitectura vernácula, como heredera de las tradiciones constructivas locales, se caracteriza por su capacidad de adaptación al entorno, ofreciendo valiosas lecciones para la arquitectura contemporánea. Sin embargo, en la República Dominicana muchas de estas tipologías se encuentran en riesgo de desaparecer, perdiéndose con ellas conocimientos esenciales sobre diseño climático y confort ambiental.
En este estudio se analizaron dos modelos de arquitectura vernácula, diferenciados principalmente por su materialidad, y se compararon con una vivienda de arquitectura popular. Para ello, se realizaron mediciones de variables ambientales clave —temperatura y humedad relativa— en condiciones reales de uso.
Los datos obtenidos muestran que las viviendas construidas en tejamaní alcanzan mayores niveles de confort térmico, registrando temperaturas interiores significativamente más bajas en comparación con las demás tipologías. Estos hallazgos confirman que la arquitectura vernácula, especialmente la de tejamaní, logra un desempeño ambiental sobresaliente mediante estrategias pasivas, superando en muchos casos a la arquitectura formal contemporánea.
El dataset permite comprender, interpretar y reproducir la forma en que estas viviendas responden al clima, aportando evidencia para investigaciones sobre sostenibilidad, confort y adaptación arquitectónica. 

 English
Vernacular architecture, as the heir of local constructive traditions, is distinguished by its strong adaptation to the environment, offering valuable lessons for contemporary design. In the Dominican Republic, however, many of these typologies are at risk of disappearing, along with their embedded knowledge on climatic design and environmental comfort.
This study analyzes two models of vernacular housing, differentiated mainly by materiality, and compares them with a popular housing unit. Environmental measurements were carried out under real use conditions, focusing on key comfort variables: indoor temperature and relative humidity.
The data show that tejamaní houses achieve higher thermal comfort, consistently recording lower indoor temperatures compared to other typologies. These findings confirm that vernacular housing, particularly tejamaní, attains remarkable environmental performance through passive strategies, often surpassing contemporary formal architecture.
The dataset provides a basis for understanding, interpreting, and replicating how these houses adapt to climate, contributing valuable evidence for research on sustainability, comfort, and architectural adaptation.
DOI: 10.17632/hgp48w7fyc.1
title: Thermal Comfort in Vernacular Architecture

description: En español
La arquitectura vernácula, como heredera de las tradiciones constructivas locales, se caracteriza por su capacidad de adaptación al entorno, ofreciendo valiosas lecciones para la arquitectura contemporánea. Sin embargo, en la República Dominicana muchas de estas tipologías se encuentran en riesgo de desaparecer, perdiéndose con ellas conocimientos esenciales sobre diseño climático y confort ambiental.
En este estudio se analizaron dos modelos de arquitectura vernácula, diferenciados principalmente por su materialidad, y se compararon con una vivienda de arquitectura popular. Para ello, se realizaron mediciones de variables ambientales clave —temperatura y humedad relativa— en condiciones reales de uso.
Los datos obtenidos muestran que las viviendas construidas en tejamaní alcanzan mayores niveles de confort térmico, registrando temperaturas interiores significativamente más bajas en comparación con las demás tipologías. Estos hallazgos confirman que la arquitectura vernácula, especialmente la de tejamaní, logra un desempeño ambiental sobresaliente mediante estrategias pasivas, superando en muchos casos a la arquitectura formal contemporánea.
El dataset permite comprender, interpretar y reproducir la forma en que estas viviendas responden al clima, aportando evidencia para investigaciones sobre sostenibilidad, confort y adaptación arquitectónica. 

 English
Vernacular architecture, as the heir of local constructive traditions, is distinguished by its strong adaptation to the environment, offering valuable lessons for contemporary design. In the Dominican Republic, however, many of these typologies are at risk of disappearing, along with their embedded knowledge on climatic design and environmental comfort.
This study analyzes two models of vernacular housing, differentiated mainly by materiality, and compares them with a popular housing unit. Environmental measurements were carried out under real use conditions, focusing on key comfort variables: indoor temperature and relative humidity.
The data show that tejamaní houses achieve higher thermal comfort, consistently recording lower indoor temperatures compared to other typologies. These findings confirm that vernacular housing, particularly tejamaní, attains remarkable environmental performance through passive strategies, often surpassing contemporary formal architecture.
The dataset provides a basis for understanding, interpreting, and replicating how these houses adapt to climate, contributing valuable evidence for research on sustainability, comfort, and architectural adaptation.
DOI: 10.17632/3p55r3yj3j
title: Riparian Data Analysis using GLMs and Sensitivity Analysis

description: Periphytic biomass

In each of the 30 catchments, a 150-m stream reach was selected for periphytic biomass experiments and partitioned into four sections of 30 meters. To estimate periphytic biomass, referred to as the total biological material residing on submerged surfaces in streams, acetate plates (10 cm x 15 cm) were installed in four pools per stream for approximately 45 days. The periphytic biomass was obtained by the sum of chlorophyll-a concentration, organic matter and inorganic matter. The extract was measured in a spectrophotometer at 750 and at 665 nm, according to Steinman, et al. (2017). Precombusted filters were used to determine biomass through the ash-free dry mass (AFDM). The remaining material after the filter combustion was considered the periphytic inorganic matter.

There were a total of 87 samples of periphytic biomass. Our aerial survey included samples of early-stage (n = 29), mid-stage (n = 16), edge-dominated forest classes (n = 30), and old-growth forest sites within a national park, which served as the control (n = 12).

LiDAR data collection

The aerial survey was conducted with a DJI Matrice 300 RTK Remotely Piloted Aircraft (RPA) equipped with a Zenmuse L1 LiDAR sensor. Data acquisition was performed between 9h and 16h, with the autonomous flight programmed to take place at a speed of 8 m/s at 60 meters above surface level (AGL), utilizing a "terrain follow" flight mode to maintain a consistent distance from the terrain's surface, configured in non-repetitive mode with 2-return. The collected data was exported to DJI Terra, version 3.15.24 (DJI Enterprise, Shenzhen, China), software was used to generate the 3D point cloud. The transects were executed alongside the streamline, producing a high-density point cloud (~100 ± 50 pts/m2). A D-RTK base station provided real-time kinematic (RTK) corrections for precise georeferencing, with a RMSE of 9.6 cm. The output from this procedure was a series of files containing xyzi (easting, northing, elevation, intensity) information for each laser pulse return collected from the sampled riparian ecosystem.

The four reach sections and their limits were geo-located using a pair of Spectra Geospatial SP-60 GNSS receivers in RTK mode and used to assess the relationship between periphyton and forest structure. We emphasize that, although we aimed for a 30-m reach section.

The fitted generalized linear model for periphytic biomass was analysed to consider the relative importance of each forest structure variable on the model output. The relative importance of each forest structure variable on the model’s output was then conducted using the eFAST analysis. The eFAST sensitivity analysis was conducted using 1,000 sampling points. First-order indices were computed based on variance decomposition, enabling the identification of each parameter’s contribution to output variability.
DOI: 10.17632/3p55r3yj3j.1
title: Riparian Data Analysis using GLMs and Sensitivity Analysis

description: Periphytic biomass

In each of the 30 catchments, a 150-m stream reach was selected for periphytic biomass experiments and partitioned into four sections of 30 meters. To estimate periphytic biomass, referred to as the total biological material residing on submerged surfaces in streams, acetate plates (10 cm x 15 cm) were installed in four pools per stream for approximately 45 days. The periphytic biomass was obtained by the sum of chlorophyll-a concentration, organic matter and inorganic matter. The extract was measured in a spectrophotometer at 750 and at 665 nm, according to Steinman, et al. (2017). Precombusted filters were used to determine biomass through the ash-free dry mass (AFDM). The remaining material after the filter combustion was considered the periphytic inorganic matter.

There were a total of 87 samples of periphytic biomass. Our aerial survey included samples of early-stage (n = 29), mid-stage (n = 16), edge-dominated forest classes (n = 30), and old-growth forest sites within a national park, which served as the control (n = 12).

LiDAR data collection

The aerial survey was conducted with a DJI Matrice 300 RTK Remotely Piloted Aircraft (RPA) equipped with a Zenmuse L1 LiDAR sensor. Data acquisition was performed between 9h and 16h, with the autonomous flight programmed to take place at a speed of 8 m/s at 60 meters above surface level (AGL), utilizing a "terrain follow" flight mode to maintain a consistent distance from the terrain's surface, configured in non-repetitive mode with 2-return. The collected data was exported to DJI Terra, version 3.15.24 (DJI Enterprise, Shenzhen, China), software was used to generate the 3D point cloud. The transects were executed alongside the streamline, producing a high-density point cloud (~100 ± 50 pts/m2). A D-RTK base station provided real-time kinematic (RTK) corrections for precise georeferencing, with a RMSE of 9.6 cm. The output from this procedure was a series of files containing xyzi (easting, northing, elevation, intensity) information for each laser pulse return collected from the sampled riparian ecosystem.

The four reach sections and their limits were geo-located using a pair of Spectra Geospatial SP-60 GNSS receivers in RTK mode and used to assess the relationship between periphyton and forest structure. We emphasize that, although we aimed for a 30-m reach section.

The fitted generalized linear model for periphytic biomass was analysed to consider the relative importance of each forest structure variable on the model output. The relative importance of each forest structure variable on the model’s output was then conducted using the eFAST analysis. The eFAST sensitivity analysis was conducted using 1,000 sampling points. First-order indices were computed based on variance decomposition, enabling the identification of each parameter’s contribution to output variability.
DOI: 10.17632/848s7stwcy.2
title: The Fiscal Policy Blend and its Impact on Sectoral Growth: The Case of Greece

description: Data and programs for replication of the paper with the same title as above
DOI: 10.17632/rtg4wdspdt
title: Comparison of Clinical Characteristics According to Etiological Classification of Pressure Injuries in Pediatric Patients: A Retrospective Cohort Study

description: a total of 283 pressure injuries were identified in 149 pediatric patients hospitalised in our hospital between August 2020 and December 2024.
DOI: 10.17632/rtg4wdspdt.1
title: Comparison of Clinical Characteristics According to Etiological Classification of Pressure Injuries in Pediatric Patients: A Retrospective Cohort Study

description: a total of 283 pressure injuries were identified in 149 pediatric patients hospitalised in our hospital between August 2020 and December 2024.
DOI: 10.15468/dl.mmhnku
title: Occurrence Download

description: A dataset containing 41 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phleum paniculatum",
 "Country is Austria",
 "HasCoordinate is true"
 ]
}

 The dataset includes 41 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008451-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57992/nmrxiv.p130
title: Caffeic Acid (CENAPT IMPs Series)

description: CENAPT@UIC Invalid Metabolic Panaceas (IMPs)
DOI: 10.57992/nmrxiv.p130.s1348
title: CaffeicAcid_NMRdata_400MHz_DMSOd6_JDX

description: This dataset contains NMR spectra obtained for the sample -CaffeicAcid_NMRdata_400MHz_DMSOd6_JDX date: 2016/12/19 05:45:00.000 isFt: true name: CaffeicAcid_3080ug200uL_DMSOd6_13CNMR_400MHz_JDX type: NMR SPECTRUM isFid: false lastX: -2573.73229374608 title: CaffeicAcid_CAFF01_3100u200u firstX: 22678.792958779202 nucleus: 13C solvent: DMSO-D6 dataClass: XYDATA dimension: 1 isComplex: false experiment: null pulseSequence: carbon.jxp numberOfPoints: 52430 acquisitionMode: SIMULTANEOUS originFrequency: 100.525303325165 acquisitionScheme: undefined date: 2016/12/15 04:45:38.000 isFt: true name: CaffeicAcid_3080ug200uL_DMSOd6_COSY_400MHz_JDX type: nD NMR SPECTRUM isFid: false title: CaffeicAcid_CAFF01_3100u200u nucleus: 1H,1H solvent: DMSO-D6 dataClass: NTUPLES dimension: 2 isComplex: false experiment: cosy spectrumSize: 1640,2048 pulseSequence: cosy_pfg.jxp acquisitionMode: SIMULTANEOUS originFrequency: 399.78219837825,399.78219837825 acquisitionScheme: notPhaseSensitive date: 2016/12/19 09:13:26.000 isFt: true name: CaffeicAcid_3080ug200uL_DMSOd6_HMBC_400MHz_JDX type: nD NMR SPECTRUM isFid: false title: CaffeicAcid_CAFF01_3100u200u nucleus: 1H,13C solvent: DMSO-D6 dataClass: NTUPLES dimension: 2 isComplex: false experiment: hmbc spectrumSize: 3278,2048 pulseSequence: hmbc_pfg.jxp acquisitionMode: SIMULTANEOUS originFrequency: 399.78219837825,100.54673201401232 acquisitionScheme: notPhaseSensitive date: 2016/12/15 05:47:26.000 isFt: true name: CaffeicAcid_3080ug200uL_DMSOd6_HSQC_400MHz_JDX type: nD NMR SPECTRUM isFid: false title: CaffeicAcid_CAFF01_3100u200u nucleus: 1H,13C solvent: DMSO-D6 dataClass: NTUPLES dimension: 2 isComplex: false experiment: hsqc spectrumSize: 1640,2048 pulseSequence: hsqc.jxp acquisitionMode: SIMULTANEOUS (DQD) originFrequency: 399.78219837825,100.54673201401232 acquisitionScheme: Echo-antiecho date: 2016/12/19 11:58:42.000 isFt: true name: CaffeicAcid_3080ug200uL_DMSOd6_qHNMR_400MHz_JDX type: NMR SPECTRUM isFid: false lastX: -1207.93486776251 title: CaffeicAcid_CAFF01_3100u200u firstX: 6804.885645057971 nucleus: 1H solvent: DMSO-D6 dataClass: XYDATA dimension: 1 isComplex: false experiment: null pulseSequence: proton.jxp numberOfPoints: 52430 acquisitionMode: SIMULTANEOUS originFrequency: 399.78219837825 acquisitionScheme: undefined
DOI: 10.57992/nmrxiv.p130.s1348.d5951
title: CaffeicAcid_NMRdata_400MHz_DMSOd6_JDX[CaffeicAcid_3080ug200uL_DMSOd6_13CNMR_400MHz_JDX.jdx]

description: CaffeicAcid_3080ug200uL_DMSOd6_13CNMR_400MHz_JDX.jdx
DOI: 10.15468/dl.8t376m
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phleum paniculatum",
 "Country is Belgium",
 "HasCoordinate is true"
 ]
}

 The dataset includes 27 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008452-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/yk4dfrxwhm
title: Digital image analysis aided adulteration detection data set for milk powder adulterated with flour

description: Adulteration Detection of Milk Powder with All Purpose Flour (Maida)

Location: Mahila, Malda, West Bengal, India
Time: 10 AM to 5 PM

About Milk Powder:

Milk powder is a dairy product made by evaporating milk to dryness, which increases its shelf life and makes it easier to store and transport. It contains essential nutrients like proteins, calcium, and vitamins, and is widely used in beverages, baking, and confectionery products. High-quality milk powder should be pure, free from contaminants, and have a characteristic milky taste.

About All Purpose Flour (Maida):

All-purpose (AP) flour is a milled wheat flour with a moderate protein content (9-12%) suitable for various culinary applications, such as making breads, cakes, and thickening sauces.

Adulteration Concern:

All Purpose Flour (Maida) is sometimes fraudulently mixed into Milk Powder to increase the product’s weight and profits. This adulteration significantly lowers the nutritional value of the milk powder and poses serious health risks such as digestive discomfort, gastrointestinal issues, and other harmful diseases.


---

Mobile Details:

Mobile Name: Moto G85

Camera Sensor: Sony LYT600 with OIS (Optical Image Stabilization)

RAM & Storage: 4 GB RAM, 128 GB ROM

Camera: Rear Camera – Dual; Front Camera – 16 MP

Display: 6.5-inch Full HD+

Battery: 5000 mAh



---

Process:

1. Sample Preparation:
First, buy pure milk powder and all-purpose flour (maida) from the local market.


2. Precautions:

Wear masks and gloves, as fine powders can cause respiratory discomfort.

Conduct the experiment in a clean, dry place with proper ventilation.



3. Weighing Samples:

Measure pure milk powder using a precision weighing balance.

Divide the sample into 20 equal portions.



4. Adulteration Proportions:
Prepare adulterated samples by mixing milk powder with All Purpose Flour at different proportions by weight:

0% (Pure milk powder), 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, and 100% (pure flour).



5. Background Setup:
Use two types of backgrounds:

White background

Black background
(For clearer identification of adulteration levels during image analysis).



6. Sample Display:

On a white paper and black paper (210 mm x 297 mm, 80 gsm), draw 20 equal-sized cells.

Spread each sample thinly and evenly within the cells.



7. Image Capture:

Click photos of each sample using Moto G85 (Sony LYT600 sensor with OIS) for high clarity and stability.

For each background, 12 sets of samples are prepared:

1 set of 100% pure milk powder

1 set of 100% All Purpose Flour

10 sets of adulterated samples (with varying proportions of flour).




8. Total Image Count:

Each background sheet: 12 sets × 20 samples = 240 images.

Two backgrounds → Total images = 240 × 2 = 480 images.
DOI: 10.57992/nmrxiv.p130.s1348.d5952
title: CaffeicAcid_NMRdata_400MHz_DMSOd6_JDX[CaffeicAcid_3080ug200uL_DMSOd6_COSY_400MHz_JDX.jdx]

description: CaffeicAcid_3080ug200uL_DMSOd6_COSY_400MHz_JDX.jdx
DOI: 10.57992/nmrxiv.p130.s1348.d5953
title: CaffeicAcid_NMRdata_400MHz_DMSOd6_JDX[CaffeicAcid_3080ug200uL_DMSOd6_HMBC_400MHz_JDX.jdx]

description: CaffeicAcid_3080ug200uL_DMSOd6_HMBC_400MHz_JDX.jdx