cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.71929/rubin/2570311
title: Legacy Survey of Space and Time Data Preview 1: visit_image dataset type

description: The Legacy Survey of Space and Time Data Preview 1 (DP1) is the first release of data from the NSF-DOE Vera C. Rubin Observatory. It consists of raw and calibrated single-epoch images, co-adds, difference images, detection catalogs, and other derived data products. DP1 is based on 1792 science-grade optical/near-infrared exposures acquired over 48 distinct nights by the Rubin Commissioning Camera, LSSTComCam, on the Simonyi Survey Telescope at the Summit Facility on Cerro Pachón, Chile during the first on-sky commissioning campaign in late 2024. DP1 covers a total of approximately 15 sq. deg. over seven roughly equally-sized non-contiguous fields, each independently observed in six broad photometric bands, ugrizy, spanning a range of stellar densities and latitudes and overlapping with external reference datasets.

This dataset is a subset of the full data release consisting of the visit_image dataset type. These are individual processed and calibrated sky images obtained from a single observation with a single filter. This release contains 15,972 datasets of this type.
DOI: 10.17632/sv2jsvfmnz
title: Effects of Chitosan Addition on Scum Layer Formation and Biogas Production during Anaerobic Digestion of Corn Straw

description: This study investigated the regulatory role of chitosan (CTS) in anaerobic digestion (AD) of corn straw (CS) and the related underlying mechanisms. Batch fermentation experiments with a CTS concentration gradient of 0–50 mg/g total suspended solids (TSS) were conducted in conjunction with low-frequency nuclear magnetic resonance (LF-NMR), X-ray diffractometer (XRD), high-throughput sequencing, and metabolic pathway analyses. The results showed that low-concentration CTS (≤ 30 mg/g TSS) enhanced straw particle cohesion, increasing the scum layer volume by 14.81%–28.89%. This structural modification reduced the density of the scum layer and enhanced its porosity, facilitating the penetration of mechanically bound moisture and free moisture to prevent crust formation and improve mass transfer efficiency. At the same time, CTS activated methanogenic metabolism by increasing coenzyme F420 activity and selectively enriching key methanogenic archaea, such as norank_c_Bathyarchaeia. CTS concentrations exceeding 30 mg/g TSS inhibited methane production. At the optimal dosage of 30 mg/g TSS, cumulative methane production increased by 43.3%, from 257.9 ± 15.39 mL/g VS in the control group to 369.82 ± 24.62 mL/g VS. These findings reveal that CTS optimizes AD performance through a dual mechanism involving the modification of the physical structure of CS and the activation of microbial metabolism, providing a theoretical foundation for its application in agricultural waste treatment via AD.
DOI: 10.17632/sv2jsvfmnz.1
title: Effects of Chitosan Addition on Scum Layer Formation and Biogas Production during Anaerobic Digestion of Corn Straw

description: This study investigated the regulatory role of chitosan (CTS) in anaerobic digestion (AD) of corn straw (CS) and the related underlying mechanisms. Batch fermentation experiments with a CTS concentration gradient of 0–50 mg/g total suspended solids (TSS) were conducted in conjunction with low-frequency nuclear magnetic resonance (LF-NMR), X-ray diffractometer (XRD), high-throughput sequencing, and metabolic pathway analyses. The results showed that low-concentration CTS (≤ 30 mg/g TSS) enhanced straw particle cohesion, increasing the scum layer volume by 14.81%–28.89%. This structural modification reduced the density of the scum layer and enhanced its porosity, facilitating the penetration of mechanically bound moisture and free moisture to prevent crust formation and improve mass transfer efficiency. At the same time, CTS activated methanogenic metabolism by increasing coenzyme F420 activity and selectively enriching key methanogenic archaea, such as norank_c_Bathyarchaeia. CTS concentrations exceeding 30 mg/g TSS inhibited methane production. At the optimal dosage of 30 mg/g TSS, cumulative methane production increased by 43.3%, from 257.9 ± 15.39 mL/g VS in the control group to 369.82 ± 24.62 mL/g VS. These findings reveal that CTS optimizes AD performance through a dual mechanism involving the modification of the physical structure of CS and the activation of microbial metabolism, providing a theoretical foundation for its application in agricultural waste treatment via AD.
DOI: 10.15468/dl.wvjhhz
title: Occurrence Download

description: A dataset containing 147 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Costa Rica",
 "TaxonKey is Diphysa americana (Mill.) M.Sousa"
 ]
}

 The dataset includes 147 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081705-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/fdkbrgt6f7
title: Primary Energy Demand of India

description: The Data consists of primary energy consumption metrics of India from 1965 to 2023. It provides the foundation for demand forecasts of the nation, taking primary energy as a whole, rather than just the electricity sector. The GDP Percapita acts as an crucial factor for the projection as it will account for growth of population and demand over the period.
DOI: 10.17632/fdkbrgt6f7.1
title: Primary Energy Demand of India

description: The Data consists of primary energy consumption metrics of India from 1965 to 2023. It provides the foundation for demand forecasts of the nation, taking primary energy as a whole, rather than just the electricity sector. The GDP Percapita acts as an crucial factor for the projection as it will account for growth of population and demand over the period.
DOI: 10.17632/bvvpwb9b85
title: BD-Engg-MDD-Predictors

description: The dataset on 803 engineering students from 34 universities of Bangladesh provides a comprehensive insight into various aspects of their lives, encompassing demographic information, lifestyle choices, social media habits, educational concerns, family dynamics, personal relationships, past experiences, and mental health (Major Depressive Disorder) indicators. Here is a breakdown of the key variables:
Demographic Information: 
Age, Gender, Department, Relationship/Marital status, Area of living, Socio-economic status (family), Current living arrangement, Year of study
Lifestyle: 
Religious practices, Exercise habits, Sleep-related issues, Satisfaction with sleep quality, Smoking habits, Drinking (alcohol) habits
Social Media Addiction: 
Eagerness to use social media, Morning routine involving social media, Productivity impact due to social media, Quest for internet connectivity for social media, Purposeless use of social media, Late-night social media activity
Educational Issue: 
Lack of concentration during classes, Worries about academic performance, Concerns about job prospects after graduation, Preoccupation with future after university life, Lack of self-confidence
Family Issue: 
Parental misunderstandings, Belonging to a broken family, Relationship status with family members, Sharing feelings of depression with family
Personal Relationship: 
Current status of the romantic relationship, Relationship-induced stress, Relief when partner isn't around Diminished joy in favorite activities due to the relationship, Consideration of breaking up for mental health preservation
Past Experience: 
History of mental disorder diagnosis, Previous consultation with therapists/psychologists, Childhood problems (abuse, early loss of parent), Past experiences of bullying
Major Depressive Disorder: 
The dataset concludes with a series of questions (following PHQ-9) related to the diagnostic criteria for Major Depressive Disorder, encompassing feelings of sadness, loss of interest, sleep disturbances, fatigue, changes in appetite, negative self-perception, concentration issues, psychomotor changes, and suicidal thoughts.

Overall, this dataset provides a holistic understanding of the students' lives, shedding light on both everyday aspects and potential mental health challenges. Researchers and analysts can use this information to explore correlations, patterns, and potential intervention strategies in the context of engineering students in Bangladesh.
DOI: 10.71929/rubin/2570312
title: Legacy Survey of Space and Time Data Preview 1: difference_image dataset type

description: The Legacy Survey of Space and Time Data Preview 1 (DP1) is the first release of data from the NSF-DOE Vera C. Rubin Observatory. It consists of raw and calibrated single-epoch images, co-adds, difference images, detection catalogs, and other derived data products. DP1 is based on 1792 science-grade optical/near-infrared exposures acquired over 48 distinct nights by the Rubin Commissioning Camera, LSSTComCam, on the Simonyi Survey Telescope at the Summit Facility on Cerro Pachón, Chile during the first on-sky commissioning campaign in late 2024. DP1 covers a total of approximately 15 sq. deg. over seven roughly equally-sized non-contiguous fields, each independently observed in six broad photometric bands, ugrizy, spanning a range of stellar densities and latitudes and overlapping with external reference datasets.

This dataset is a subset of the full data release consisting of the difference_image dataset type. These are images created by subtracting a template image from a visit image. This release contains 15,972 datasets of this type.
DOI: 10.17632/bvvpwb9b85.3
title: BD-Engg-MDD-Predictors

description: The dataset on 803 engineering students from 34 universities of Bangladesh provides a comprehensive insight into various aspects of their lives, encompassing demographic information, lifestyle choices, social media habits, educational concerns, family dynamics, personal relationships, past experiences, and mental health (Major Depressive Disorder) indicators. Here is a breakdown of the key variables:
Demographic Information: 
Age, Gender, Department, Relationship/Marital status, Area of living, Socio-economic status (family), Current living arrangement, Year of study
Lifestyle: 
Religious practices, Exercise habits, Sleep-related issues, Satisfaction with sleep quality, Smoking habits, Drinking (alcohol) habits
Social Media Addiction: 
Eagerness to use social media, Morning routine involving social media, Productivity impact due to social media, Quest for internet connectivity for social media, Purposeless use of social media, Late-night social media activity
Educational Issue: 
Lack of concentration during classes, Worries about academic performance, Concerns about job prospects after graduation, Preoccupation with future after university life, Lack of self-confidence
Family Issue: 
Parental misunderstandings, Belonging to a broken family, Relationship status with family members, Sharing feelings of depression with family
Personal Relationship: 
Current status of the romantic relationship, Relationship-induced stress, Relief when partner isn't around Diminished joy in favorite activities due to the relationship, Consideration of breaking up for mental health preservation
Past Experience: 
History of mental disorder diagnosis, Previous consultation with therapists/psychologists, Childhood problems (abuse, early loss of parent), Past experiences of bullying
Major Depressive Disorder: 
The dataset concludes with a series of questions (following PHQ-9) related to the diagnostic criteria for Major Depressive Disorder, encompassing feelings of sadness, loss of interest, sleep disturbances, fatigue, changes in appetite, negative self-perception, concentration issues, psychomotor changes, and suicidal thoughts.

Overall, this dataset provides a holistic understanding of the students' lives, shedding light on both everyday aspects and potential mental health challenges. Researchers and analysts can use this information to explore correlations, patterns, and potential intervention strategies in the context of engineering students in Bangladesh.
DOI: 10.15468/dl.6ncxwm
title: Occurrence Download

description: A dataset containing 7169 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Broussonetia papyrifera (L.) L'Hér. ex Vent.",
 "Country is United States of America",
 "HasCoordinate is true"
 ]
}

 The dataset includes 7169 records from 86 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081704-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/bnk6w9s7j9
title: Served or Exploited? Perceived Exploitation in AI Data Capture Experiences

description: Dataset used for the research present in the article "Served or Exploited? Perceived Exploitation in AI Data Capture Experiences".
DOI: 10.17632/bnk6w9s7j9.1
title: Served or Exploited? Perceived Exploitation in AI Data Capture Experiences

description: Dataset used for the research present in the article "Served or Exploited? Perceived Exploitation in AI Data Capture Experiences".
DOI: 10.17632/ts5h8hxgy2
title: Clinical and Histopathological Dataset of 170 Colorectal Cancer Patients from RSUD Abdoel Wahab Sjahranie, Indonesia (2020-2024)

description: Overview

This dataset contains de-identified clinical records of 170 colorectal cancer (CRC) patients, including:
•⁠ ⁠Demographic data (age, sex)
•⁠ ⁠CEA biomarker levels (ng/ml)
•⁠ ⁠Tumor histopathology (WHO subtypes)
•⁠ ⁠TNM and Dukes staging
•⁠ ⁠3D tumor measurements (length/width/height)

Key Features
•⁠ ⁠Population: Indonesian CRC patients from East Kalimantan (underrepresented in global databases)
•⁠ ⁠Variables: 9 clinical + 5 histopathological parameters
•⁠ ⁠Rare Subtypes: Medullary (85 cases), Signet-ring cell (11 cases)
•⁠ ⁠Staging: Full TNM (33 subtypes) and Dukes classifications

Potential Applications
1.⁠ ⁠Biomarker validation for Southeast Asian populations
2.⁠ ⁠Machine learning in cancer staging
3.⁠ ⁠Epidemiological studies of CRC subtypes

Files Included
1.⁠ ⁠⁠ clinical_records.csv ⁠ - Primary clinical data
2.⁠ ⁠Data dictionary (PDF)
3.⁠ ⁠Python scripts for analysis
DOI: 10.15468/dl.s3ndes
title: Occurrence Download

description: A dataset containing 976 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mosannona depressa (Baill.) Chatrou"
 ]
}

 The dataset includes 976 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081706-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/ts5h8hxgy2.1
title: Clinical and Histopathological Dataset of 170 Colorectal Cancer Patients from RSUD Abdoel Wahab Sjahranie, Indonesia (2020-2024)

description: Overview

This dataset contains de-identified clinical records of 170 colorectal cancer (CRC) patients, including:
•⁠ ⁠Demographic data (age, sex)
•⁠ ⁠CEA biomarker levels (ng/ml)
•⁠ ⁠Tumor histopathology (WHO subtypes)
•⁠ ⁠TNM and Dukes staging
•⁠ ⁠3D tumor measurements (length/width/height)

Key Features
•⁠ ⁠Population: Indonesian CRC patients from East Kalimantan (underrepresented in global databases)
•⁠ ⁠Variables: 9 clinical + 5 histopathological parameters
•⁠ ⁠Rare Subtypes: Medullary (85 cases), Signet-ring cell (11 cases)
•⁠ ⁠Staging: Full TNM (33 subtypes) and Dukes classifications

Potential Applications
1.⁠ ⁠Biomarker validation for Southeast Asian populations
2.⁠ ⁠Machine learning in cancer staging
3.⁠ ⁠Epidemiological studies of CRC subtypes

Files Included
1.⁠ ⁠⁠ clinical_records.csv ⁠ - Primary clinical data
2.⁠ ⁠Data dictionary (PDF)
3.⁠ ⁠Python scripts for analysis
DOI: 10.71929/rubin/2570313
title: Legacy Survey of Space and Time Data Preview 1: deep_coadd dataset type

description: The Legacy Survey of Space and Time Data Preview 1 (DP1) is the first release of data from the NSF-DOE Vera C. Rubin Observatory. It consists of raw and calibrated single-epoch images, co-adds, difference images, detection catalogs, and other derived data products. DP1 is based on 1792 science-grade optical/near-infrared exposures acquired over 48 distinct nights by the Rubin Commissioning Camera, LSSTComCam, on the Simonyi Survey Telescope at the Summit Facility on Cerro Pachón, Chile during the first on-sky commissioning campaign in late 2024. DP1 covers a total of approximately 15 sq. deg. over seven roughly equally-sized non-contiguous fields, each independently observed in six broad photometric bands, ugrizy, spanning a range of stellar densities and latitudes and overlapping with external reference datasets.

This dataset is a subset of the full data release consisting of the deep_coadd dataset type. These are the combination of multiple processed, calibrated, and background- subtracted images, for a patch of sky, for each of the six filters. This release contains 2,644 datasets of this type.
DOI: 10.15468/dl.7zua2b
title: Occurrence Download

description: A dataset containing 509 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Oxandra lanceolata (Sw.) Baill."
 ]
}

 The dataset includes 509 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081707-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/d8s5byx96r
title: An efficient algorithm for computing entanglement entropy in systems with a restricted Hilbert space or U(1) symmetry

description: We present an efficient algorithm for computing entanglement entropies in systems with a restricted Hilbert space or U(1) symmetry. For the case of a restricted Hilbert space, the algorithm is straightforward in that only a map table from physical states to indices of an intermediate matrix is needed. In systems with a U(1) symmetry, the reduced density matrix can be put into a block-diagonal form by properly grouping matrix elements according to the total charge in the subsystem, leading to a significant boost in the efficiency of entanglement entropy calculation.
DOI: 10.17632/d8s5byx96r.1
title: An efficient algorithm for computing entanglement entropy in systems with a restricted Hilbert space or U(1) symmetry

description: We present an efficient algorithm for computing entanglement entropies in systems with a restricted Hilbert space or U(1) symmetry. For the case of a restricted Hilbert space, the algorithm is straightforward in that only a map table from physical states to indices of an intermediate matrix is needed. In systems with a U(1) symmetry, the reduced density matrix can be put into a block-diagonal form by properly grouping matrix elements according to the total charge in the subsystem, leading to a significant boost in the efficiency of entanglement entropy calculation.
DOI: 10.17632/p4frbmr74y
title: Research area

description: Forest area data for the Loess Plateau in square kilometres.
DOI: 10.17632/p4frbmr74y.1
title: Research area

description: Forest area data for the Loess Plateau in square kilometres.
DOI: 10.17632/t4ynk47yv5
title: First-order control of basaltic sources on the diversity of Archean TTG magmatism

description: These are the supplementary tables, including mainly whole rock geochemical, zircon U-Pb-Lu-Hf isotopic and thermodynamic-trace element modeling data, which are used to support a first-order control of basaltic sources on the Archean TTG magmatism
DOI: 10.17632/t4ynk47yv5.1
title: First-order control of basaltic sources on the diversity of Archean TTG magmatism

description: These are the supplementary tables, including mainly whole rock geochemical, zircon U-Pb-Lu-Hf isotopic and thermodynamic-trace element modeling data, which are used to support a first-order control of basaltic sources on the Archean TTG magmatism
DOI: 10.71929/rubin/2570314
title: Legacy Survey of Space and Time Data Preview 1: template_coadd dataset type

description: The Legacy Survey of Space and Time Data Preview 1 (DP1) is the first release of data from the NSF-DOE Vera C. Rubin Observatory. It consists of raw and calibrated single-epoch images, co-adds, difference images, detection catalogs, and other derived data products. DP1 is based on 1792 science-grade optical/near-infrared exposures acquired over 48 distinct nights by the Rubin Commissioning Camera, LSSTComCam, on the Simonyi Survey Telescope at the Summit Facility on Cerro Pachón, Chile during the first on-sky commissioning campaign in late 2024. DP1 covers a total of approximately 15 sq. deg. over seven roughly equally-sized non-contiguous fields, each independently observed in six broad photometric bands, ugrizy, spanning a range of stellar densities and latitudes and overlapping with external reference datasets.

This dataset is a subset of the full data release consisting of the template_coadd dataset type. These are the combination of processed images with the best seeing, for a patch of sky and for each of the six LSST filters. Used to create difference images. This release contains 2,730 datasets of this type.
DOI: 10.15468/dl.dtwhzf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Broussonetia papyrifera (L.) L'Hér. ex Vent.",
 "Country is Zambia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081708-250525065834625/datasets/export for details.
DOI: 10.15468/dl.665af2
title: Occurrence Download

description: A dataset containing 1718 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Merops boehmi Reichenow, 1882",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 1718 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081709-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/s6xh86fkcm
title: quTARANG: A high-performance computing Python package to study turbulence using the Gross-Pitaevskii equation

description: We present quTARANG, a robust GPU-accelerated Python package developed for a comprehensive study of turbulence problems in Bose-Einstein condensates (BECs). It solves the mean-field Gross-Pitaevskii equation (GPE) using a Time-splitting pseudo-spectral (TSSP) scheme and ground state calculations are performed using a Backward Euler spectral (BESP) scheme. quTARANG also has post-processing tools that can compute different statistical properties of turbulent Bose-Einstein condensates, such as kinetic energy spectra, particle number spectrum and corresponding fluxes. This paper provides detailed descriptions of the code, along with specific examples for calculating the ground state and turbulent state of the condensate under different initial conditions for both 2-D and 3-D cases. We also present results on the dynamics of the GPE in 2-D and 3-D used to validate our code. Finally, we compare the performance of quTARANG on different GPUs to its performance on a CPU, demonstrating the speedup achieved on various GPU architectures.