cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/k69rvfp8wb
title: digital image analysis aided Adulteration Detection Data Set for turmeric powder adulterated with flour

description: Adulteration Detection of Turmeric Powder with Wheat Flour

1. About Turmeric Powder

Turmeric powder (Curcuma longa L.) is a widely used spice known for its flavor, color, and medicinal properties. It contains:

Dietary fiber

Vitamins: C, B6, K

Minerals: iron, potassium, manganese

Bioactive compound: curcumin, responsible for its yellow color and health-promoting effects

2. Adulteration Issue

Common adulterant: Wheat flour

Impact: Reduces purity, dilutes curcumin content, and lowers nutritional value. May also cause digestive issues in gluten-sensitive individuals.

Challenge: Adulterated turmeric powder appears similar to pure turmeric powder; visual detection is difficult.



---

3. Detection Methodology

3.1 Dataset Creation

Samples: Pure and adulterated turmeric powder with wheat flour at varying proportions.

Labeling: Conducted by experts for ground truth reference.


3.2 Image Preprocessing

Grayscale conversion

RGB and HSV analysis

Median smoothing

Adaptive thresholding

Edge detection


These enhance subtle differences between pure turmeric and adulterated samples.

3.3 Feature Extraction

Texture: Smoothness variations due to fine flour particles

Color: Wheat flour lightens the natural yellow hue of turmeric

Surface patterns: Subtle granule distribution changes



---

4. Sample Preparation

Turmeric rhizomes: Collected from Maliha, Malda, West Bengal, India

Moisture content after drying: 4–5% (dry weight basis)

Ash content of turmeric powder: typically ranges from 5–8%

Grinding: Mixer grinder (500 W, 0.5 L jar, hybrid motor, 18,000 rpm, 9 min total → 3 × 3 min cycles)

Sieving: Passed through 60-mesh sieve → undersized fraction used as pure turmeric powder


Wheat flour (adulterant): Collected from a local mill

Cleaned and oven-dried at 50 °C for 4 h

Passed through a 60-mesh sieve for uniform particle size


Mixtures prepared:

0% (pure turmeric), 5%,10%,15%, 20%, 25%,30%,35%, 40%, 45%,50%, and 100% wheat flour by weight




---

5. Image Acquisition

Sample setup:

24sets of samples prepared:

Pure turmeric (100%)

Pure wheat flour (100%)

Adulterated turmeric samples (5–50% wheat flour by weight, at 10% intervals)


White A4 sheet (210 × 297 mm, 80 gsm) divided into 20 equal cells

Thin layer of sample spread evenly in each cell

Total images: 24 × 20 = 480 images


Camera specifications:

Device: Redmi 12 5G smartphone

OS: Android 12

Processor: Octa-core MediaTek Helio P35

Camera:

Rear: 50 MP f/1.8 (wide), 2 MP f/2.4 (depth)

Front: 8 MP f/2.0


Sensor size: 1/3.06", 1.12 µm

Lighting: Natural, no flash
Lens-to-sample distance: 15 cm
DOI: 10.17632/k69rvfp8wb.1
title: digital image analysis aided Adulteration Detection Data Set for turmeric powder adulterated with flour

description: Adulteration Detection of Turmeric Powder with Wheat Flour

1. About Turmeric Powder

Turmeric powder (Curcuma longa L.) is a widely used spice known for its flavor, color, and medicinal properties. It contains:

Dietary fiber

Vitamins: C, B6, K

Minerals: iron, potassium, manganese

Bioactive compound: curcumin, responsible for its yellow color and health-promoting effects

2. Adulteration Issue

Common adulterant: Wheat flour

Impact: Reduces purity, dilutes curcumin content, and lowers nutritional value. May also cause digestive issues in gluten-sensitive individuals.

Challenge: Adulterated turmeric powder appears similar to pure turmeric powder; visual detection is difficult.



---

3. Detection Methodology

3.1 Dataset Creation

Samples: Pure and adulterated turmeric powder with wheat flour at varying proportions.

Labeling: Conducted by experts for ground truth reference.


3.2 Image Preprocessing

Grayscale conversion

RGB and HSV analysis

Median smoothing

Adaptive thresholding

Edge detection


These enhance subtle differences between pure turmeric and adulterated samples.

3.3 Feature Extraction

Texture: Smoothness variations due to fine flour particles

Color: Wheat flour lightens the natural yellow hue of turmeric

Surface patterns: Subtle granule distribution changes



---

4. Sample Preparation

Turmeric rhizomes: Collected from Maliha, Malda, West Bengal, India

Moisture content after drying: 4–5% (dry weight basis)

Ash content of turmeric powder: typically ranges from 5–8%

Grinding: Mixer grinder (500 W, 0.5 L jar, hybrid motor, 18,000 rpm, 9 min total → 3 × 3 min cycles)

Sieving: Passed through 60-mesh sieve → undersized fraction used as pure turmeric powder


Wheat flour (adulterant): Collected from a local mill

Cleaned and oven-dried at 50 °C for 4 h

Passed through a 60-mesh sieve for uniform particle size


Mixtures prepared:

0% (pure turmeric), 5%,10%,15%, 20%, 25%,30%,35%, 40%, 45%,50%, and 100% wheat flour by weight




---

5. Image Acquisition

Sample setup:

24sets of samples prepared:

Pure turmeric (100%)

Pure wheat flour (100%)

Adulterated turmeric samples (5–50% wheat flour by weight, at 10% intervals)


White A4 sheet (210 × 297 mm, 80 gsm) divided into 20 equal cells

Thin layer of sample spread evenly in each cell

Total images: 24 × 20 = 480 images


Camera specifications:

Device: Redmi 12 5G smartphone

OS: Android 12

Processor: Octa-core MediaTek Helio P35

Camera:

Rear: 50 MP f/1.8 (wide), 2 MP f/2.4 (depth)

Front: 8 MP f/2.0


Sensor size: 1/3.06", 1.12 µm

Lighting: Natural, no flash
Lens-to-sample distance: 15 cm
DOI: 10.17632/jrm8j6mzrh
title: Afrontamiento a la muerte en estudiantes latinoamericanos del área de la salud

description: Dataset sobre afrontamiento a la muerte en estudiantes de salud latinoamericanos mediante la escala de Buget
DOI: 10.17632/jrm8j6mzrh.1
title: Afrontamiento a la muerte en estudiantes latinoamericanos del área de la salud

description: Dataset sobre afrontamiento a la muerte en estudiantes de salud latinoamericanos mediante la escala de Buget
DOI: 10.17632/8dr5s47p25
title: data-physico-chemical-mecanical

description: data-physico-chemical-mecanical.xlsx: This dataset consists of 130 soil samples collected from five different provinces, with spectral, physical, chemical, and mechanical properties measured. The samples were analyzed in the laboratory, where physical, chemical, and mechanical soil properties were determined. Physical, chemical, and spectral features were used as model inputs, and mechanical properties were estimated using the model. These data can be used for analyzing the effects of various soil properties, developing predictive models for soil mechanical behavior, and supporting research in soil management and agriculture.. 
RAW-Spectra-400-2450 nm.rar: This file contains the spectral data preprocessing applied using Parles software. Five different preprocessing methods were applied to the spectral data, which were then used as model inputs. By combining these spectral features with soil properties, a total of 11 transfer functions were generated for estimating soil compaction and swelling indices.
Mean Random-RF-MR-RAW-COMPACTION: This file contains the analysis of the 11 transfer functions generated for predicting soil compaction and swelling indices. The analyses were performed using Random Forest and Multiple Linear Regression methods. Error statistics for the models were calculated to evaluate their predictive performance.
DOI: 10.17632/8dr5s47p25.1
title: data-physico-chemical-mecanical

description: data-physico-chemical-mecanical.xlsx: This dataset consists of 130 soil samples collected from five different provinces, with spectral, physical, chemical, and mechanical properties measured. The samples were analyzed in the laboratory, where physical, chemical, and mechanical soil properties were determined. Physical, chemical, and spectral features were used as model inputs, and mechanical properties were estimated using the model. These data can be used for analyzing the effects of various soil properties, developing predictive models for soil mechanical behavior, and supporting research in soil management and agriculture.. 
RAW-Spectra-400-2450 nm.rar: This file contains the spectral data preprocessing applied using Parles software. Five different preprocessing methods were applied to the spectral data, which were then used as model inputs. By combining these spectral features with soil properties, a total of 11 transfer functions were generated for estimating soil compaction and swelling indices.
Mean Random-RF-MR-RAW-COMPACTION: This file contains the analysis of the 11 transfer functions generated for predicting soil compaction and swelling indices. The analyses were performed using Random Forest and Multiple Linear Regression methods. Error statistics for the models were calculated to evaluate their predictive performance.
DOI: 10.17632/8gtbky7r9x
title: Human Trafficking Court Decisions (Indonesia) — Structured Dataset

description: This dataset contains structured extractions of 404 Indonesian criminal court decisions related to human trafficking. The decisions were downloaded from the Mahkamah Agung RI Decision Directory (putusan.mahkamahagung.go.id). Decisions were parsed into a normalized tabular schema following the official Attachment to the Special Criminal Decision Format (Lampiran Format Putusan Pidana Khusus) under Decree of the Chief Justice of the Supreme Court (Surat Keputusan Ketua Mahkamah Agung (SKKMA)) No. 359 concerning templates and guidelines for writing decisions/rulings of first instance and appellate courts in the four judicial bodies (template fields such as identitas pihak, dakwaan, tuntutan, pertimbangan hukum, amar, etc.). Extraction utilized AI-assisted parsing and was subsequently reviewed by expert legal scholars/practitioners. An accompanying evaluation file reports field-level completeness/quality checks by experts.

#Dataset Profile:
Contributors Affiliations: Faculty of Engineering and Faculty of Law, Universitas Muhammadiyah Malang (UMM) 
Year of Decision: 2023–2024 
Geographic coverage: Indonesia 
Units of observation: Court Decision (Putusan Pengadilan) at the District Court (Pengadilan Negeri) level in Human Trafficking Criminal Case (Tindak Pidana Perdagangan Orang)

#Files
The dataset consists of:
1. Human_Trafficking_Court_Decission_utf8.csv — main table with all extracted decision components (one row per decision). 
2. Human_Trafficking_Data_Evaluation_utf8.csv — per-field expert evaluation of completeness/consistency for each decision. 
3. Data_Dictionary.csv — machine-generated column inventory (dtype, null%, example value) to support curation.
4. Original Decision (PDF) — the official document as available on the Supreme Court website.

#Columns & Schema
See data_dictionary.csv for a comprehensive inventory. Key fields include:
- File Name — source PDF filename as downloaded. 
- Judul, Nomor putusan, Irah-irah, Nama pengadilan negeri, Keterangan perkara — head of decision. 
- Identitas terdakwa — identity consists of name, place of birth, age/ date of birth, gender, nationality, and address. 
- Penangkapan, Penahanan, Tuntutan, Dakwaan — case history 
- Saksi, Ahli, Terdakwa, Surat, Petunjuk/BB (Barang Bukti) — facts and evidence 
- Fakta Hukum, Pertimbangan Hukum, Amar Putusan — core legal reasoning and disposition. 
- Hari, Tanggal, Tahun, Siapa yang memutus, Panitera pengganti, Tanda tangan majelis — signature and dating blocks.

#Ethics & Sensitive Content
Decisions may include sensitive victim and child-related information. Must maintain publication ethics (e.g., use aggregation/masking).

# Acknowledgements
Thanks to the Ministry of Higher Education, Science, and Technology, Republic of Indonesia, for the regular fundamental research grant (0070/C3/AL.04/2025), Judicial Commission of the Republic of Indonesia, and the Supreme Court of the Republic of Indonesia.
DOI: 10.17632/8gtbky7r9x.1
title: Human Trafficking Court Decisions (Indonesia) — Structured Dataset

description: This dataset contains structured extractions of 404 Indonesian criminal court decisions related to human trafficking. The decisions were downloaded from the Mahkamah Agung RI Decision Directory (putusan.mahkamahagung.go.id). Decisions were parsed into a normalized tabular schema following the official Attachment to the Special Criminal Decision Format (Lampiran Format Putusan Pidana Khusus) under Decree of the Chief Justice of the Supreme Court (Surat Keputusan Ketua Mahkamah Agung (SKKMA)) No. 359 concerning templates and guidelines for writing decisions/rulings of first instance and appellate courts in the four judicial bodies (template fields such as identitas pihak, dakwaan, tuntutan, pertimbangan hukum, amar, etc.). Extraction utilized AI-assisted parsing and was subsequently reviewed by expert legal scholars/practitioners. An accompanying evaluation file reports field-level completeness/quality checks by experts.

#Dataset Profile:
Contributors Affiliations: Faculty of Engineering and Faculty of Law, Universitas Muhammadiyah Malang (UMM) 
Year of Decision: 2023–2024 
Geographic coverage: Indonesia 
Units of observation: Court Decision (Putusan Pengadilan) at the District Court (Pengadilan Negeri) level in Human Trafficking Criminal Case (Tindak Pidana Perdagangan Orang)

#Files
The dataset consists of:
1. Human_Trafficking_Court_Decission_utf8.csv — main table with all extracted decision components (one row per decision). 
2. Human_Trafficking_Data_Evaluation_utf8.csv — per-field expert evaluation of completeness/consistency for each decision. 
3. Data_Dictionary.csv — machine-generated column inventory (dtype, null%, example value) to support curation.
4. Original Decision (PDF) — the official document as available on the Supreme Court website.

#Columns & Schema
See data_dictionary.csv for a comprehensive inventory. Key fields include:
- File Name — source PDF filename as downloaded. 
- Judul, Nomor putusan, Irah-irah, Nama pengadilan negeri, Keterangan perkara — head of decision. 
- Identitas terdakwa — identity consists of name, place of birth, age/ date of birth, gender, nationality, and address. 
- Penangkapan, Penahanan, Tuntutan, Dakwaan — case history 
- Saksi, Ahli, Terdakwa, Surat, Petunjuk/BB (Barang Bukti) — facts and evidence 
- Fakta Hukum, Pertimbangan Hukum, Amar Putusan — core legal reasoning and disposition. 
- Hari, Tanggal, Tahun, Siapa yang memutus, Panitera pengganti, Tanda tangan majelis — signature and dating blocks.

#Ethics & Sensitive Content
Decisions may include sensitive victim and child-related information. Must maintain publication ethics (e.g., use aggregation/masking).

# Acknowledgements
Thanks to the Ministry of Higher Education, Science, and Technology, Republic of Indonesia, for the regular fundamental research grant (0070/C3/AL.04/2025), Judicial Commission of the Republic of Indonesia, and the Supreme Court of the Republic of Indonesia.
DOI: 10.5281/zenodo.17127642
title: Table 2 in Rise up, Rhizoctonia: moving to one fungus, one name in the Ceratobasidiaceae (Agaricomycetes, Cantharellales)

description: Table 2. Alignment statistics for datasets used for phylogenetic inference.
RegionNo. of seqsTotal sitesInformative sitesSingleton sitesInvariant sitesNamed taxa dataset (ITS + LSU + ATP6 + RPB2 + TEF1)ITS45957421240296LSU251105142354609ATP612713190204319RPB2151651332495824TEF1131062163222677Total5488124815152725Named taxa + UNITE species hypotheses (ITS only)ITS6041078768189121Named taxa + representative orchid mycorrhizal OTUs and pathogenic AG groups (ITS only)ITS1941006566243197
description: Published as part of O'Donnell, R. P., Linde, C. C. & May, T. W., 2025, Rise up, Rhizoctonia: moving to one fungus, one name in the Ceratobasidiaceae (Agaricomycetes, Cantharellales), pp. 291 in Persoonia 54 (1) on page 291, DOI: 10.3114/persoonia.2025.54.09, http://zenodo.org/record/17127619
DOI: 10.5281/zenodo.17127641
title: Table 2 in Rise up, Rhizoctonia: moving to one fungus, one name in the Ceratobasidiaceae (Agaricomycetes, Cantharellales)

description: Table 2. Alignment statistics for datasets used for phylogenetic inference.
RegionNo. of seqsTotal sitesInformative sitesSingleton sitesInvariant sitesNamed taxa dataset (ITS + LSU + ATP6 + RPB2 + TEF1)ITS45957421240296LSU251105142354609ATP612713190204319RPB2151651332495824TEF1131062163222677Total5488124815152725Named taxa + UNITE species hypotheses (ITS only)ITS6041078768189121Named taxa + representative orchid mycorrhizal OTUs and pathogenic AG groups (ITS only)ITS1941006566243197
description: Published as part of O'Donnell, R. P., Linde, C. C. & May, T. W., 2025, Rise up, Rhizoctonia: moving to one fungus, one name in the Ceratobasidiaceae (Agaricomycetes, Cantharellales), pp. 291 in Persoonia 54 (1) on page 291, DOI: 10.3114/persoonia.2025.54.09, http://zenodo.org/record/17127619
DOI: 10.15468/dl.ck3trn
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Micropterus dolomieu",
 "Country is Zambia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004766-250914085247600/datasets/export for details.
DOI: 10.15468/dl.zamcrd
title: Occurrence Download

description: A dataset containing 673583 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-73.44998 4.74249,-73.53895 4.71871,-73.5918 4.78654,-73.66975 4.83413,-73.78501 4.85511,-73.82656 4.861,-73.85744 4.85836,-73.87829 4.87252,-73.91382 4.85013,-73.99362 4.76595,-74.00758 4.73505,-74.00542 4.66772,-74.02528 4.66039,-74.02787 4.6496,-74.03261 4.63751,-74.01362 4.63277,-73.99765 4.61636,-73.98902 4.6319,-73.95733 4.65426,-73.94979 4.64056,-73.93541 4.64056,-73.94237 4.62448,-73.93061 4.61453,-73.91417 4.59466,-73.90595 4.51518,-73.90276 4.42626,-73.87193 4.38045,-73.84109 4.40688,-73.80938 4.39983,-73.79088 4.36019,-73.78208 4.29853,-73.76534 4.28003,-73.70984 4.28091,-73.65378 4.27117,-73.61311 4.2432,-73.4632 4.27915,-73.41211 4.34521,-73.36894 4.37957,-73.34692 4.37693,-73.29935 4.39454,-73.22007 4.45797,-73.18924 4.53196,-73.19717 4.56367,-73.22888 4.68876,-73.22095 4.73456,-73.35661 4.80151,-73.44998 4.74249))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 673583 records from 333 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004727-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rmpza4
title: Occurrence Download

description: A dataset containing 649 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Country is Norway",
 "Country is Finland",
 "Country is Sweden"
 ]
 },
 "TaxonKey is Rhingia borealis Ringdahl, 1928"
 ]
}

 The dataset includes 649 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0266532-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z2zhc6
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Allium orunbaii F.O.Khass. & R.M.Fritsch"
 ]
}

 The dataset includes 7 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0166270-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8butn2
title: Occurrence Download

description: A dataset containing 283967 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-180 59,180 59,180 61,-180 61,-180 59))",
 "HasCoordinate is true",
 {
 "or" : [
 "TaxonKey is Pinopsida",
 "TaxonKey is Cycadopsida",
 "TaxonKey is Gnetopsida",
 "TaxonKey is Ginkgoopsida"
 ]
 },
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 283967 records from 128 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034355-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pmgsfy
title: Occurrence Download

description: A dataset containing 230 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Castanopsis tungurrut (Blume) A.DC."
 ]
}

 The dataset includes 230 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0267744-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f8jad3
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Country is China",
 "Country is Chinese Taipei"
 ]
 },
 "TaxonKey is Anaphalis transnokoensis Sasaki"
 ]
}

 The dataset includes 12 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0332820-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bjg69j
title: Occurrence Download

description: A dataset containing 49 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dendrobium gerlandianum Kraenzl."
 ]
}

 The dataset includes 49 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017192-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.la2jta
title: Occurrence Download

description: A dataset containing 3356 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ficus microcarpa L.fil."
 ]
}

 The dataset includes 3356 records from 135 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006277-190307172214381/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xjnkzy
title: Occurrence Download

description: A dataset containing 3795 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Platygobio gracilis (Richardson, 1836)"
 ]
}

 The dataset includes 3795 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0332821-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s9j4yd
title: Occurrence Download

description: A dataset containing 188 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eonycteris robusta Miller, 1913"
 ]
}

 The dataset includes 188 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0267840-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mtrz9f
title: Occurrence Download

description: A dataset containing 354 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Euploca tenella (Torr.) Feuillet & Halse",
 "BasisOfRecord is one of (Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (United States of America, Canada)"
 ]
}

 The dataset includes 354 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0268989-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.