cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/26pw833vxb
title: Reproducibility package: PCA–SOM–KMeans clustering of LiDAR-derived structural predictors

description: This dataset supports the study “Unsupervised Machine Learning of LiDAR-Derived Forest Structure to Support Ecosystem Monitoring in Minnesota’s Kawishiwi Ranger District” (Salehnia et al., 2026; under review at Ecological Informatics). The working hypothesis of the study is that LiDAR-derived canopy structure metrics, summarized over a regular grid, can be used to objectively delineate spatially coherent forest structural types (clusters) that are meaningful for ecosystem monitoring and interpretation of forest heterogeneity.

The dataset includes (i) Python code to derive structural predictors from a Canopy Height Model (CHM) raster and (ii) code to perform an unsupervised learning workflow consisting of PCA-based dimensionality reduction, Self-Organizing Map (SOM) training, and K-means clustering. The main data file (predictors_with_latlon.csv) contains gridded observations with Latitude/Longitude (EPSG:4326) and corresponding LiDAR/CHM structural predictors that quantify canopy height distribution, variability, and vertical stratification (e.g., percentiles, variability metrics, and stratum-based measures). These predictors were computed from CHM values after applying a simple quality-control filter to reduce common artifacts (thresholds and justification are documented in the scripts and in the manuscript Methods).

The workflow produces diagnostic outputs (PCA scree/variance summaries and loading tables, SOM grid evaluation metrics and U-matrix visualization, and K-means cluster selection metrics) and assigns each grid cell/point to a structural cluster. The resulting cluster patterns can be interpreted as distinct forest structural regimes across the study area (e.g., differences in canopy height, structural complexity, and strata occupancy), providing a reproducible basis for mapping and monitoring forest structure. Methodological details and parameter settings are described in Salehnia et al. (2026), Sections 2.2–2.5. Users can apply the scripts to other CHM/LiDAR products by updating file paths and adapting the CHM quality-control thresholds to match local canopy height ranges and data characteristics.

Intended use: The code and example data can be reused to reproduce the results of the study and/or to apply the same PCA–SOM–K-means clustering approach to other LiDAR/CHM-derived structural datasets.
DOI: 10.57745/cm6w1g
title: point_spacing_test_2025_0723_091443.mat
DOI: 10.57745/pfhb7v
title: point_spacing_test_2025_0717_193047.mat
DOI: 10.57745/yua5ig
title: point_spacing_test_2025_0723_093431.mat
DOI: 10.17632/26pw833vxb.1
title: Reproducibility package: PCA–SOM–KMeans clustering of LiDAR-derived structural predictors

description: This dataset supports the study “Unsupervised Machine Learning of LiDAR-Derived Forest Structure to Support Ecosystem Monitoring in Minnesota’s Kawishiwi Ranger District” (Salehnia et al., 2026; under review at Ecological Informatics). The working hypothesis of the study is that LiDAR-derived canopy structure metrics, summarized over a regular grid, can be used to objectively delineate spatially coherent forest structural types (clusters) that are meaningful for ecosystem monitoring and interpretation of forest heterogeneity.

The dataset includes (i) Python code to derive structural predictors from a Canopy Height Model (CHM) raster and (ii) code to perform an unsupervised learning workflow consisting of PCA-based dimensionality reduction, Self-Organizing Map (SOM) training, and K-means clustering. The main data file (predictors_with_latlon.csv) contains gridded observations with Latitude/Longitude (EPSG:4326) and corresponding LiDAR/CHM structural predictors that quantify canopy height distribution, variability, and vertical stratification (e.g., percentiles, variability metrics, and stratum-based measures). These predictors were computed from CHM values after applying a simple quality-control filter to reduce common artifacts (thresholds and justification are documented in the scripts and in the manuscript Methods).

The workflow produces diagnostic outputs (PCA scree/variance summaries and loading tables, SOM grid evaluation metrics and U-matrix visualization, and K-means cluster selection metrics) and assigns each grid cell/point to a structural cluster. The resulting cluster patterns can be interpreted as distinct forest structural regimes across the study area (e.g., differences in canopy height, structural complexity, and strata occupancy), providing a reproducible basis for mapping and monitoring forest structure. Methodological details and parameter settings are described in Salehnia et al. (2026), Sections 2.2–2.5. Users can apply the scripts to other CHM/LiDAR products by updating file paths and adapting the CHM quality-control thresholds to match local canopy height ranges and data characteristics.

Intended use: The code and example data can be reused to reproduce the results of the study and/or to apply the same PCA–SOM–K-means clustering approach to other LiDAR/CHM-derived structural datasets.
DOI: 10.57745/e7fdz4
title: point_spacing_test_2025_0722_200000.mat
DOI: 10.57745/yyiscq
title: point_spacing_test_2025_0722_211608.mat
DOI: 10.57745/vvi3py
title: point_spacing_test_2025_0723_100926.mat
DOI: 10.57745/ydp8il
title: point_spacing_test_2025_0722_213105.mat
DOI: 10.17632/xndy94yjrm
title: Green finance and environmental life cycle assessment

description: This Mendeley Data deposit contains the dataset extracted from a literature review on the integration of Life Cycle Assessment (LCA) with green/sustainable finance, focusing on how environmental results are translated into metrics, rules, and financial decision mechanisms. The collection includes structured records by study (IDs) containing: characterization of the research design (empirical/modeling/conceptual), analysis scale (project, asset, portfolio, market), type of financial instrument/arrangement (e.g., green corporate bonds, carbon credits and carbon markets, regulatory metrics such as the Green Asset Ratio, project finance under the Equator Principles, and fintech-enabled mechanisms such as tokenization/crowdfunding), and the role played by LCA in the instrument’s governance (eligibility, pricing, risk management, monitoring, and verification). For each study, core LCA elements were coded: functional unit (when available), system boundaries (e.g., cradle-to-gate, cradle-to-grave, phase-specific cut-offs), main impact categories (e.g., climate/GWP, eutrophication, toxicity, water use, and resource depletion), databases and impact methods (when reported), and the mode of integration with economic variables (NPV/IRR, climate cost-effectiveness, avoided impact per unit of capital, conversion into credits/carbon price signals, and price-adjustment coefficients). Recurrent findings and constraints were also documented, including: sensitivity to scenarios and baseline choices, limited methodological standardization and project-level disclosure in post-issuance reporting, greenwashing risks linked to proprietary methodologies, and information asymmetries in cross-border contexts with diverging regulatory regimes. In addition, the deposit includes a similarity-analysis output (graph) and an associated interpretive text, highlighting the centrality of the “environmental–impact–assessment” core and two dominant LCA to finance translation regimes: (I) regulatory/classification pathways (taxonomies and indicators) and (II) market/pricing pathways (credits, trading, and carbon price signals). These data can be reused for meta-syntheses, instrument-by-instrument comparative analyses, and methodological advances in MRV and the standardization of green finance metrics.
DOI: 10.57745/0rw9de
title: point_spacing_test_2025_0723_102426.mat
DOI: 10.57745/swfox9
title: point_spacing_test_2025_0723_104522.mat
DOI: 10.17632/xndy94yjrm.1
title: Green finance and environmental life cycle assessment

description: This Mendeley Data deposit contains the dataset extracted from a literature review on the integration of Life Cycle Assessment (LCA) with green/sustainable finance, focusing on how environmental results are translated into metrics, rules, and financial decision mechanisms. The collection includes structured records by study (IDs) containing: characterization of the research design (empirical/modeling/conceptual), analysis scale (project, asset, portfolio, market), type of financial instrument/arrangement (e.g., green corporate bonds, carbon credits and carbon markets, regulatory metrics such as the Green Asset Ratio, project finance under the Equator Principles, and fintech-enabled mechanisms such as tokenization/crowdfunding), and the role played by LCA in the instrument’s governance (eligibility, pricing, risk management, monitoring, and verification). For each study, core LCA elements were coded: functional unit (when available), system boundaries (e.g., cradle-to-gate, cradle-to-grave, phase-specific cut-offs), main impact categories (e.g., climate/GWP, eutrophication, toxicity, water use, and resource depletion), databases and impact methods (when reported), and the mode of integration with economic variables (NPV/IRR, climate cost-effectiveness, avoided impact per unit of capital, conversion into credits/carbon price signals, and price-adjustment coefficients). Recurrent findings and constraints were also documented, including: sensitivity to scenarios and baseline choices, limited methodological standardization and project-level disclosure in post-issuance reporting, greenwashing risks linked to proprietary methodologies, and information asymmetries in cross-border contexts with diverging regulatory regimes. In addition, the deposit includes a similarity-analysis output (graph) and an associated interpretive text, highlighting the centrality of the “environmental–impact–assessment” core and two dominant LCA to finance translation regimes: (I) regulatory/classification pathways (taxonomies and indicators) and (II) market/pricing pathways (credits, trading, and carbon price signals). These data can be reused for meta-syntheses, instrument-by-instrument comparative analyses, and methodological advances in MRV and the standardization of green finance metrics.
DOI: 10.57745/dq7tyd
title: point_spacing_test_2025_0722_185507.mat
DOI: 10.57745/xsam89
title: point_spacing_test_2025_0722_203823.mat
DOI: 10.57745/whjqv5
title: point_spacing_test_2025_0722_181428.mat
DOI: 10.57745/5wylgo
title: point_spacing_test_2025_0722_192237.mat
DOI: 10.57745/m2pmrt
title: point_spacing_test_2025_0722_183422.mat
DOI: 10.57745/afkbde
title: point_spacing_test_2025_0722_194045.mat
DOI: 10.57745/wk7ycz
title: point_spacing_test_2025_0722_182549.mat
DOI: 10.57745/21ku2m
title: point_spacing_test_2025_0723_131405.mat
DOI: 10.17632/bv296vrzsn
title: NASOSINUSIAL DEDIFFERENTIATED LIPOSARCOMA: AN UNUSUAL DIAGNOSIS OF A NASOSINUSIAL MASS

description: Introduction :
Liposarcoma is a malignant tumour of mesenchymal origin. We report a case of liposarcoma of the nasosinus cavities in a female patient. The aim is to use this clinical case to highlight the diagnostic difficulties posed by nasosinus liposarcoma and to discuss the choice of surgical approach.
Observation: 
A 53-year-old female patient with a history of endonasal surgery for nasosinus tumour syndrome consulted for tumour recurrence. Tumour resection via paranasal rhinotomy was possible. Immunohistochemistry of the surgical specimen is consistent with dedifferentiated nasosinus liposarcoma. Radiotherapy sessions have begun and, after 8 months of follow-up, the outcome is satisfactory.
Conclusion: 
Undifferentiated nasosinus liposarcoma is a malignant tumour whose high potential for recurrence and low sensitivity to chemotherapy justify extensive surgical excision.
DOI: 10.57745/tkovhv
title: point_spacing_test_2025_0723_125749.mat
DOI: 10.57745/zibw95
title: point_spacing_test_2025_0723_122441.mat
DOI: 10.57745/ubneg8
title: point_spacing_test_2025_0724_112956.mat
DOI: 10.17632/bv296vrzsn.1
title: NASOSINUSIAL DEDIFFERENTIATED LIPOSARCOMA: AN UNUSUAL DIAGNOSIS OF A NASOSINUSIAL MASS

description: Introduction :
Liposarcoma is a malignant tumour of mesenchymal origin. We report a case of liposarcoma of the nasosinus cavities in a female patient. The aim is to use this clinical case to highlight the diagnostic difficulties posed by nasosinus liposarcoma and to discuss the choice of surgical approach.
Observation: 
A 53-year-old female patient with a history of endonasal surgery for nasosinus tumour syndrome consulted for tumour recurrence. Tumour resection via paranasal rhinotomy was possible. Immunohistochemistry of the surgical specimen is consistent with dedifferentiated nasosinus liposarcoma. Radiotherapy sessions have begun and, after 8 months of follow-up, the outcome is satisfactory.
Conclusion: 
Undifferentiated nasosinus liposarcoma is a malignant tumour whose high potential for recurrence and low sensitivity to chemotherapy justify extensive surgical excision.
DOI: 10.57745/ka6oje
title: point_spacing_test_2025_0724_111652.mat
DOI: 10.57745/q3r44a
title: point_spacing_test_2025_0723_134239.mat
DOI: 10.57745/p7r8xg
title: point_spacing_test_2025_0723_115255.mat
DOI: 10.57745/frsacc
title: point_spacing_test_2025_0723_142407.mat
DOI: 10.57745/wtyi2s
title: point_spacing_test_2025_0723_112034.mat
DOI: 10.57745/ndqp7n
title: point_spacing_test_2025_0724_101945.mat
DOI: 10.57745/szw2ok
title: point_spacing_test_2025_0724_100932.mat
DOI: 10.57745/322iqb
title: point_spacing_test_2025_0724_104722.mat
DOI: 10.57745/5uqdho
title: point_spacing_test_2025_0703_185556.mat
DOI: 10.57745/cfpc8o
title: point_spacing_test_2025_0703_182305.mat
DOI: 10.57745/1afg31
title: point_spacing_test_2025_0625_145220.mat
DOI: 10.6084/m9.figshare.30117175
title: Supplemental Tables S1-S2.docx

description: Details of antibodies used for electron transport chain (ETC) protein analysis by SDS-polyacrylamide gel electrophoresis.
DOI: 10.57745/vv7e9b
title: point_spacing_test_2025_0703_170552.mat
DOI: 10.6084/m9.figshare.30117175.v1
title: Supplemental Table S1.docx

description: Details of antibodies used for electron transport chain (ETC) protein analysis by SDS-polyacrylamide gel electrophoresis.
DOI: 10.6084/m9.figshare.30117175.v2
title: Supplemental Tables S1-S2.docx

description: Details of antibodies used for electron transport chain (ETC) protein analysis by SDS-polyacrylamide gel electrophoresis.
DOI: 10.57745/md8mw4
title: point_spacing_test_2025_0703_175912.mat
DOI: 10.57745/vahfmx
title: point_spacing_test_2025_0703_173312.mat
DOI: 10.57745/4d0ilx
title: point_spacing_test_2025_0703_172334.mat
DOI: 10.57745/5aac9q
title: point_spacing_test_2025_0703_151537.mat
DOI: 10.57745/cvy4br
title: point_spacing_test_2025_0703_200537.mat
DOI: 10.57745/b57jyt
title: point_spacing_test_2025_0709_143315.mat
DOI: 10.57745/ogyctr
title: point_spacing_test_2025_0703_192823.mat
DOI: 10.57745/qjv9ht
title: point_spacing_test_2025_0709_145240.mat
DOI: 10.57745/0biywq
title: point_spacing_test_2025_0703_204656.mat
DOI: 10.57745/0wqodj
title: point_spacing_test_2025_0625_191806.mat
DOI: 10.57745/mqdxed
title: point_spacing_test_2025_0625_183831.mat
DOI: 10.57745/r7xhci
title: point_spacing_test_2025_0625_175730.mat
DOI: 10.57745/uahxej
title: point_spacing_test_2025_0625_172659.mat
DOI: 10.17632/7jhycpbdpw.2
title: MirathQA: An Arabic Dataset for Islamic Inheritance Reasoning

description: The repository contains three processed files (train, val, and test) and (Mirath Dataset_final) the raw dataset, which includes the original collected cases used to generate the multiple-choice questions (MCQs). The scripts folder contains all the scripts that can be used to reproduce the dataset.
DOI: 10.57745/okgnuy
title: point_spacing_test_2025_0625_194824.mat
DOI: 10.57745/4guzpc
title: point_spacing_test_2025_0625_155818.mat
DOI: 10.57745/kt1lce
title: point_spacing_test_2025_0625_182139.mat
DOI: 10.57745/sildzq
title: point_spacing_test_2025_0627_203121.mat
DOI: 10.57745/lqalh5
title: point_spacing_test_2025_0710_151347.mat
DOI: 10.57745/l75fai
title: point_spacing_test_2025_0710_152602.mat
DOI: 10.57745/bn2elg
title: point_spacing_test_2025_0624_201729.mat
DOI: 10.57745/lu0em5
title: point_spacing_test_2025_0624_202723.mat
DOI: 10.57745/vza9wi
title: point_spacing_test_2025_0624_200637.mat
DOI: 10.57745/o2hkkg
title: point_spacing_test_2025_0624_205134.mat
DOI: 10.57745/5gmtkk
title: point_spacing_test_2025_0627_173645.mat
DOI: 10.17632/byxk8xbzty
title: Data for 'Rare Earth Elements and stable isotope signatures from hydrothermal alteration in Vargeão Dome with implications for impact-generated hydrothermal systems on Mars'

description: Dataset for manuscript 'Rare Earth Elements and stable isotope signatures from hydrothermal alteration in Vargeão Dome with implications for impact-generated hydrothermal systems on Mars'

Data on hydrothermal vein deposits and host rock (basalts, rhyodacites and sandstones) of Vargeão Dome impact structure, Brazil.

Data includes: 
- sample point locations (file formats compatible with GIS applications)
- SEM files (19 .xlsx files)
- clumped stable isotope data (1 .xlsx file)
- gasbench stable isotope data (3 .xlsx files)
DOI: 10.57745/jnplsf
title: point_spacing_test_2025_0627_175503.mat
DOI: 10.57745/yqlivb
title: point_spacing_test_2025_0624_195749.mat
DOI: 10.57745/ytevfk
title: point_spacing_test_2025_0627_190054.mat
DOI: 10.17632/byxk8xbzty.1
title: Data for 'Rare Earth Elements and stable isotope signatures from hydrothermal alteration in Vargeão Dome with implications for impact-generated hydrothermal systems on Mars'

description: Dataset for manuscript 'Rare Earth Elements and stable isotope signatures from hydrothermal alteration in Vargeão Dome with implications for impact-generated hydrothermal systems on Mars'

Data on hydrothermal vein deposits and host rock (basalts, rhyodacites and sandstones) of Vargeão Dome impact structure, Brazil.

Data includes: 
- sample point locations (file formats compatible with GIS applications)
- SEM files (19 .xlsx files)
- clumped stable isotope data (1 .xlsx file)
- gasbench stable isotope data (3 .xlsx files)
DOI: 10.57745/htqogo
title: point_spacing_test_2025_0627_182112.mat
DOI: 10.57745/orckmw
title: point_spacing_test_2025_0627_195403.mat
DOI: 10.57745/s9pohq
title: point_spacing_test_2025_0627_201138.mat
DOI: 10.57745/nbmgly
title: point_spacing_test_2025_0710_155300.mat
DOI: 10.57745/6j4jy3
title: point_spacing_test_2025_0710_162351.mat
DOI: 10.57745/hj0xfg
title: point_spacing_test_2025_0627_193251.mat
DOI: 10.57745/1sqsbv
title: point_spacing_test_2025_0708_163250.mat
DOI: 10.57745/qk2gwh
title: point_spacing_test_2025_0708_170427.mat
DOI: 10.57745/khagiv
title: point_spacing_test_2025_0708_173106.mat
DOI: 10.57745/xmcckp
title: point_spacing_test_2025_0626_142109.mat
DOI: 10.57745/fgbm2z
title: point_spacing_test_2025_0626_171006.mat
DOI: 10.57745/2t9eem
title: point_spacing_test_2025_0626_140611.mat
DOI: 10.57745/jmol3w
title: point_spacing_test_2025_0626_143859.mat
DOI: 10.57745/ydsrud
title: point_spacing_test_2025_0626_144946.mat
DOI: 10.57745/mzq4tk
title: point_spacing_test_2025_0626_173148.mat
DOI: 10.17632/yc8yhg5xjd
title: Induction Motor Vibration and Acoustic Cellular Device (IM-VACD)

description: The dataset consists of constant and variable speed data collected from 8 different D396 Marathon Electric 3-phase motors. Each motor has different types of faults artificially created by SpectraQuest. Data is collected using three cellphones sensors (accelerometers and a microphone) (the first column is acoustic data, the second, third and fourth columns are accelerometer data [x,y,z axis data]).
The labelling of data is as follows {Letter}-{Letter}-{Letter}-{Number}-{Number}:

The following values represent the possibilities for the first letter:
- A indicates iPhone 13
- B indicates Samsung Galaxy S6
- C indicates Samsung Galaxy A50

The following values represent the possibilities for the second letter:
- H indicates healthy
- R indicates rotor
- S indicates stator
- V indicates voltage
- B indicates bowed
- K indicates broken
- F indicates faulty

The third letter is represented by the following possible values:
- H indicates healthy
- U indicates unbalance
- M indicates misalignment
- W indicates winding
- R indicates rotor
- A indicates rotor bars
- B indicates bearing

The combination of both letters indicates the health state of the motor present in the data sample (e.g. H-H = a healthy motor, S-W = a stator winding fault).

The first number represents:
- 1 is for a constant speed of 15 Hz
- 2 is for a constant speed of 20 Hz
- 3 is for a constant speed of 25 Hz
- 4 is for a constant speed of 30 Hz

The second number represents:
- 0 for a no load condition
- 1 for a loaded condition

The duration of data collection was 10 seconds at a sampling frequency of iPhone 13 at 100 Hz, Samsung Galaxy S6 at 200 Hz, and Samsung Galaxy A50 at 490 Hz for accelerometer data. For acoustic data, all phones were collected at 42,000 Hz for a 10-second data duration. Each file contains 420,000 samples.
Raw data is provided in the folders as comma separated values files (.csv).
DOI: 10.57745/jgt1cy
title: point_spacing_test_2025_0626_155348.mat
DOI: 10.57745/2ivb41
title: point_spacing_test_2025_0626_163756.mat
DOI: 10.17632/yc8yhg5xjd.1
title: Induction Motor Vibration and Acoustic Cellular Device (IM-VACD)

description: The dataset consists of constant and variable speed data collected from 8 different D396 Marathon Electric 3-phase motors. Each motor has different types of faults artificially created by SpectraQuest. Data is collected using three cellphones sensors (accelerometers and a microphone) (the first column is acoustic data, the second, third and fourth columns are accelerometer data [x,y,z axis data]).
The labelling of data is as follows {Letter}-{Letter}-{Letter}-{Number}-{Number}:

The following values represent the possibilities for the first letter:
- A indicates iPhone 13
- B indicates Samsung Galaxy S6
- C indicates Samsung Galaxy A50

The following values represent the possibilities for the second letter:
- H indicates healthy
- R indicates rotor
- S indicates stator
- V indicates voltage
- B indicates bowed
- K indicates broken
- F indicates faulty

The third letter is represented by the following possible values:
- H indicates healthy
- U indicates unbalance
- M indicates misalignment
- W indicates winding
- R indicates rotor
- A indicates rotor bars
- B indicates bearing

The combination of both letters indicates the health state of the motor present in the data sample (e.g. H-H = a healthy motor, S-W = a stator winding fault).

The first number represents:
- 1 is for a constant speed of 15 Hz
- 2 is for a constant speed of 20 Hz
- 3 is for a constant speed of 25 Hz
- 4 is for a constant speed of 30 Hz

The second number represents:
- 0 for a no load condition
- 1 for a loaded condition

The duration of data collection was 10 seconds at a sampling frequency of iPhone 13 at 100 Hz, Samsung Galaxy S6 at 200 Hz, and Samsung Galaxy A50 at 490 Hz for accelerometer data. For acoustic data, all phones were collected at 42,000 Hz for a 10-second data duration. Each file contains 420,000 samples.
Raw data is provided in the folders as comma separated values files (.csv).
DOI: 10.57745/qvkrxu
title: point_spacing_test_2025_0708_145943.mat
DOI: 10.57745/uknohf
title: point_spacing_test_2025_0708_154303.mat
DOI: 10.57745/ryx5ui
title: point_spacing_test_2025_0708_160355.mat
DOI: 10.15468/dl.8rvmsz
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Dactylorhiza fuchsii"
 ]
}

 The dataset includes 17 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004726-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/jastoe
title: point_spacing_test_2025_0708_205712.mat