cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18221431
title: Raman Spectroscopy and Reflectance as Probes for Structural Evolution of Biochar

description: The production of biochar and its incorporation into soils have recently gained attention as a carbon dioxide sequestration pathway within negative emissions strategies. The long-term persistence of biochar in soil is governed by its intrinsic material properties, which are controlled by pyrolysis conditions and feedstock type, as well as by environmental factors. This study investigates the evolution of biochar structural properties derived from three biomass feedstocks—peanut shells, sycamore wood, and wheat straw—across a range of pyrolysis temperatures (300–700 °C) using a combined reflectance and Raman spectroscopic approach.

Raman spectra were analyzed using two deconvolution approaches: a 2-band model (D and G bands) and a 4-band model (D1, D3, D4, and G bands). The resulting Raman parameters effectively capture structural transformations in biochar as functions of pyrolysis temperature and biochar reflectance. Across all samples, consistent trends were observed, including systematic shifts in band positions, increasing band separation, and progressive band narrowing with increasing temperature. Several Raman parameters—specifically the D and D1 band positions, D-band full width at half maximum (FWHM), and Raman band separation—exhibit near-linear relationships with biochar reflectance, indicating their potential as spectroscopic proxies for reflectance.

Notably, several Raman parameters display inflection points near 500 °C and at reflectance values of approximately 2.0–2.5%, marking a transition from biomass decomposition–dominated reactions to the growth and coalescence of polyaromatic clusters. This convergence of Raman-derived inflection points with reflectance thresholds closely corresponds to the stage at which biochar acquires substantially enhanced permanence in soil. Overall, the strong agreement between Raman and reflectance trends demonstrates that Raman spectroscopy provides a valuable complementary tool for biochar characterization, offering molecular-scale insights that enhance assessments of biochar stability and carbon sequestration potential.
DOI: 10.5281/zenodo.18221430
title: Raman Spectroscopy and Reflectance as Probes for Structural Evolution of Biochar

description: The production of biochar and its incorporation into soils have recently gained attention as a carbon dioxide sequestration pathway within negative emissions strategies. The long-term persistence of biochar in soil is governed by its intrinsic material properties, which are controlled by pyrolysis conditions and feedstock type, as well as by environmental factors. This study investigates the evolution of biochar structural properties derived from three biomass feedstocks—peanut shells, sycamore wood, and wheat straw—across a range of pyrolysis temperatures (300–700 °C) using a combined reflectance and Raman spectroscopic approach.

Raman spectra were analyzed using two deconvolution approaches: a 2-band model (D and G bands) and a 4-band model (D1, D3, D4, and G bands). The resulting Raman parameters effectively capture structural transformations in biochar as functions of pyrolysis temperature and biochar reflectance. Across all samples, consistent trends were observed, including systematic shifts in band positions, increasing band separation, and progressive band narrowing with increasing temperature. Several Raman parameters—specifically the D and D1 band positions, D-band full width at half maximum (FWHM), and Raman band separation—exhibit near-linear relationships with biochar reflectance, indicating their potential as spectroscopic proxies for reflectance.

Notably, several Raman parameters display inflection points near 500 °C and at reflectance values of approximately 2.0–2.5%, marking a transition from biomass decomposition–dominated reactions to the growth and coalescence of polyaromatic clusters. This convergence of Raman-derived inflection points with reflectance thresholds closely corresponds to the stage at which biochar acquires substantially enhanced permanence in soil. Overall, the strong agreement between Raman and reflectance trends demonstrates that Raman spectroscopy provides a valuable complementary tool for biochar characterization, offering molecular-scale insights that enhance assessments of biochar stability and carbon sequestration potential.
DOI: 10.17632/xz26m9ypz9
title: SWF Financials: Virtual Company Data (1996-2025)

description: Overview This dataset provides a synthetic "Income Statement" (Profit & Loss) for a virtual mega-corporation, covering 30 years of business operations from 1996 to 2025. The data is designed to simulate a realistic corporate financial lifecycle, useful for financial time-series analysis, forecasting, and educational purposes.

Dataset Composition The dataset contains 120 quarters of financial data divided into two distinct periods:

1996–2011: Projected and modeled data (64 quarters) based on historical financial trends.
2012–2025: Aggregated financial data derived from real SEC EDGAR filings (56 quarters), representing a consolidated view of large-scale corporate performance.
Key Metrics Included Each record includes standard accounting metrics:

Revenue and Cost of Revenue
Gross Profit and Operating Expenses
Operating Income and Net Income
Calculated margins (Gross, Operating, and Net Margins)
Data Quality Flags indicating the source (Projected vs. Complete) and validity.
Files Included

swf_financials.csv: The primary dataset.
SWF_FINANCIALS_SCHEMA.md: Detailed column definitions and data types.
SWF_ASSUMPTIONS.md: Documentation of the methodology used for data aggregation and projection.
financial_preprocessing_pipeline.py: The Python script used to process and clean the raw data.
examples.ipynb: A Jupyter Notebook demonstrating basic analysis and visualization of the dataset.
Potential Use Cases

Training machine learning models for financial forecasting.
Backtesting algorithmic trading strategies.
Academic teaching of accounting principles and margin analysis.
Benchmarking corporate performance against synthetic "average" mega-corporations.
Important Research Note Please note that while the 2012–2025 data is based on real SEC filings, this is a synthetic aggregation and does not represent a single specific real-world company. It is intended for research and educational applications only.
DOI: 10.17632/xz26m9ypz9.1
title: SWF Financials: Virtual Company Data (1996-2025)

description: Overview This dataset provides a synthetic "Income Statement" (Profit & Loss) for a virtual mega-corporation, covering 30 years of business operations from 1996 to 2025. The data is designed to simulate a realistic corporate financial lifecycle, useful for financial time-series analysis, forecasting, and educational purposes.

Dataset Composition The dataset contains 120 quarters of financial data divided into two distinct periods:

1996–2011: Projected and modeled data (64 quarters) based on historical financial trends.
2012–2025: Aggregated financial data derived from real SEC EDGAR filings (56 quarters), representing a consolidated view of large-scale corporate performance.
Key Metrics Included Each record includes standard accounting metrics:

Revenue and Cost of Revenue
Gross Profit and Operating Expenses
Operating Income and Net Income
Calculated margins (Gross, Operating, and Net Margins)
Data Quality Flags indicating the source (Projected vs. Complete) and validity.
Files Included

swf_financials.csv: The primary dataset.
SWF_FINANCIALS_SCHEMA.md: Detailed column definitions and data types.
SWF_ASSUMPTIONS.md: Documentation of the methodology used for data aggregation and projection.
financial_preprocessing_pipeline.py: The Python script used to process and clean the raw data.
examples.ipynb: A Jupyter Notebook demonstrating basic analysis and visualization of the dataset.
Potential Use Cases

Training machine learning models for financial forecasting.
Backtesting algorithmic trading strategies.
Academic teaching of accounting principles and margin analysis.
Benchmarking corporate performance against synthetic "average" mega-corporations.
Important Research Note Please note that while the 2012–2025 data is based on real SEC filings, this is a synthetic aggregation and does not represent a single specific real-world company. It is intended for research and educational applications only.
DOI: 10.7910/dvn/fvqmjz
title: Replication Data and Code for “The Response of Mortgage Supply to Expected Flood Insurance Lapses”

description: The replication package contains the code and data to produce the main tables and figures of "The Response of Mortgage Supply to Expected Flood Insurance Lapses" by Zhongchen Hu
DOI: 10.17632/cygy9j9ty7.1
title: Tachykinin-related peptide signalling is important for the immune response of the mealworm beetle Tenebrio molitor L - dataset

description: The data associated with the figures presented in the article: "Tachykinin-related peptide signalling is important for the immune response of the mealworm beetle Tenebrio molitor L." 

Konopińska, N., Walkowiak-Nowicka, K., Nowicki, G., Keshavarz, M., Chowański, S., Rolff, J., & Urbański, A. (2025). Tachykinin-related peptide signalling is important for the immune response of the mealworm beetle Tenebrio molitor L. bioRxiv, 2025-08.
DOI: 10.17632/p4w367x9sj.1
title: Rheological, mechanical and microstructure properties of microfine particle alkaline-activated grouting material

description: This Mendeley repository contains partial datasets from the research entitled “Rheological, mechanical and microstructure properties of microfine particle alkaline-activated grouting material,” published in the European Journal of Environmental and Civil Engineering. The available datasets include:
1. Rheological and mechanical testing data
2. XRD data of PCIII and SF
3. XRD analysis of 1CT and 1SF6 at 7 and 28 days
4. SEM and EDS data of 1CT and 1SF6
5. Brunauer–Emmett–Teller (BET) analysis of 1CT and 1SF6
6. FTIR analysis of 1CT and 1SF6
Access to this repository is restricted to the journal editors and individuals authorized by the authors for research-related purposes.
DOI: 10.17632/s3nntgvxty
title: Seasonal regulation of sediment chlorophyll a in the tidal flats of China: new insights for benthic–pelagic coupling

description: This dataset compiles field measurements of surface-sediment chlorophyll a (Chl a) from unvegetated tidal flats along the coast of China, together with a suite of co-located environmental variables that characterize climatic forcing, hydrographic conditions, and sediment properties. Chl a in the surficial sediment is used as an index of microphytobenthos standing stock and near-bed primary producer biomass, and is provided here to support analyses of spatial (e.g., latitudinal) variability and seasonal contrasts in intertidal functioning.
DOI: 10.17632/s3nntgvxty.1
title: Seasonal regulation of sediment chlorophyll a in the tidal flats of China: new insights for benthic–pelagic coupling

description: This dataset compiles field measurements of surface-sediment chlorophyll a (Chl a) from unvegetated tidal flats along the coast of China, together with a suite of co-located environmental variables that characterize climatic forcing, hydrographic conditions, and sediment properties. Chl a in the surficial sediment is used as an index of microphytobenthos standing stock and near-bed primary producer biomass, and is provided here to support analyses of spatial (e.g., latitudinal) variability and seasonal contrasts in intertidal functioning.
DOI: 10.15468/dl.fsxmhb
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((109.58 1.496182, 109.509713 1.490614, 109.441158 1.474048, 109.376024 1.44689, 109.315914 1.409811, 109.26231 1.363724, 109.216531 1.309763, 109.179704 1.249259, 109.152736 1.183702, 109.136289 1.114705, 109.130768 1.043968, 109.136309 0.973232, 109.152773 0.90424, 109.179756 0.838689, 109.216592 0.7781940000000001, 109.262374 0.724243, 109.315975 0.678166, 109.376076 0.641096, 109.441196 0.613945, 109.509733 0.597383, 109.58 0.591816, 109.650267 0.597383, 109.718804 0.613945, 109.783924 0.641096, 109.844025 0.678166, 109.897626 0.724243, 109.943408 0.7781940000000001, 109.980244 0.838689, 110.007227 0.90424, 110.023691 0.973232, 110.029232 1.043968, 110.023711 1.114705, 110.007264 1.183702, 109.980296 1.249259, 109.943469 1.309763, 109.89769 1.363724, 109.844086 1.409811, 109.783976 1.44689, 109.718842 1.474048, 109.650287 1.490614, 109.58 1.496182))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002162-260120142942310/datasets/export for details.
DOI: 10.15468/dl.7h9j83
title: Occurrence Download

description: A dataset containing 535 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((118.475 5.787142, 118.40438 5.781571, 118.335503 5.764997, 118.270068 5.737827, 118.20969 5.700733, 118.155857 5.654629, 118.109894 5.600653, 118.072933 5.540136, 118.045882 5.474569, 118.029404 5.405569, 118.023901 5.334835, 118.029506 5.264108, 118.046076 5.195131, 118.0732 5.1296, 118.110208 5.069128, 118.156187 5.015202, 118.210004 4.969149, 118.270336 4.9321, 118.335697 4.904966, 118.404482 4.888414, 118.475 4.882851, 118.545518 4.888414, 118.614303 4.904966, 118.679664 4.9321, 118.739996 4.969149, 118.793813 5.015202, 118.839792 5.069128, 118.8768 5.1296, 118.903924 5.195131, 118.920494 5.264108, 118.926099 5.334835, 118.920596 5.405569, 118.904118 5.474569, 118.877067 5.540136, 118.840106 5.600653, 118.794143 5.654629, 118.74031 5.700733, 118.679932 5.737827, 118.614497 5.764997, 118.54562 5.781571, 118.475 5.787142))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 535 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002163-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/h3f8pjbw73.1
title: UFTBESD: University of Frontier Technology, Bangladesh-Bangla Emotional Speech Dataset

description: UFTBESD (University of Frontier Technology, Bangladesh - Bangla Emotional Speech Dataset) is a Bangla language-based speech emotion recognition dataset developed to capture realistic emotional speech in everyday acoustic conditions. The dataset consists of 1400 speech-audio recordings collected from 100 native Bangla speakers aged between 19 and 60 years, with a balanced gender distribution (50 male and 50 female).

Each participant uttered two selected Bangla sentences, with each sentence spoken once (single trial) in seven emotional states: angry, disgust, fear, happy, neutral, sad, and surprise. Thus, the dataset contains 2 sentences × 7 emotions × 100 speakers = 1400 audio clips.

All recordings were collected using smartphone microphones in both indoor and outdoor environments. A significant portion of the data includes natural background noise, making the dataset suitable for real-world speech emotion recognition research. The audio files are stored in WAV format (44.1 kHz, 16-bit, mono). This release focuses on providing raw audio data, and detailed metadata will be added in future versions.

UFTBESD is intended to support the development and evaluation of Bangla speech emotion recognition systems and can be used with common machine learning and deep learning architectures such as CNN, LSTM, BiLSTM, and transformer-based models.
DOI: 10.17632/khbvmkk39z
title: 2021 Malaysia SME Industry Risk Indicators

description: This is summarize dataset of financial risk indicators of Malaysia SMEs. There are 5 parameters including probability of default (PD), recovery rate, loss given default (LGD), loan share and expected return. There are also 5 sectors in the dataset, agriculture, manufacturing, construction, services and refining (others).
Probability of Default (PD): Extracted from Lee & Liang (2022). All the data extracted are for the purpose of estimation of the enterprise risk management or related research.
The main references are:
[1] Lee, L., & Liang, T. W. (2022, March). Better business sentiment amid robust SME financing growth. Strategic Credit & Economic Analytics, SME Bank Group. https://www.smebank.com.my/documents/d/guest/betterbusinesssentiment_march2022
Contribution: PD
[2] Galow, B., Georgescu, O.-M., & Ponte Marques, A. (2024). Loss-given-default and macroeconomic conditions (Working Paper Series No. 2954). European Central Bank. https://www.ecb.europa.eu//pub/pdf/scpwps/ecb.wp2954~1d1f8942c9.en.pdf
Contribution: recovery rate and LGD
[3] Kuriakose, S., & Tiew, H. (2022). Malaysian SME Program Efficiency Review. World Bank Group. http://documents.worldbank.org/curated/en/099255003152238688
Contribution: Loan Share
[4] Asian Development Bank. (2023). Asia SME Monitor 2023 - Malaysia [Data set]. ADB Data Library. https://data.adb.org/dataset/2023-asia-small-and-medium-sized-enterprise-monitor
Contribution: Expected Return
DOI: 10.17632/khbvmkk39z.1
title: 2021 Malaysia SME Industry Risk Indicators

description: This is summarize dataset of financial risk indicators of Malaysia SMEs. There are 5 parameters including probability of default (PD), recovery rate, loss given default (LGD), loan share and expected return. There are also 5 sectors in the dataset, agriculture, manufacturing, construction, services and refining (others).
Probability of Default (PD): Extracted from Lee & Liang (2022). All the data extracted are for the purpose of estimation of the enterprise risk management or related research.
The main references are:
[1] Lee, L., & Liang, T. W. (2022, March). Better business sentiment amid robust SME financing growth. Strategic Credit & Economic Analytics, SME Bank Group. https://www.smebank.com.my/documents/d/guest/betterbusinesssentiment_march2022
Contribution: PD
[2] Galow, B., Georgescu, O.-M., & Ponte Marques, A. (2024). Loss-given-default and macroeconomic conditions (Working Paper Series No. 2954). European Central Bank. https://www.ecb.europa.eu//pub/pdf/scpwps/ecb.wp2954~1d1f8942c9.en.pdf
Contribution: recovery rate and LGD
[3] Kuriakose, S., & Tiew, H. (2022). Malaysian SME Program Efficiency Review. World Bank Group. http://documents.worldbank.org/curated/en/099255003152238688
Contribution: Loan Share
[4] Asian Development Bank. (2023). Asia SME Monitor 2023 - Malaysia [Data set]. ADB Data Library. https://data.adb.org/dataset/2023-asia-small-and-medium-sized-enterprise-monitor
Contribution: Expected Return
DOI: 10.17632/3xdbrchxgw
title: 2022 Malaysia SME Industry Risk Indicators

description: This is summarize dataset of financial risk indicators of Malaysia SMEs. There are 5 parameters including probability of default (PD), recovery rate, loss given default (LGD), loan share and expected return. There are also 5 sectors in the dataset, agriculture, manufacturing, construction, services and refining (others).
Probability of Default (PD): Extracted from Lee & Liang (2022). All the data extracted are for the purpose of estimation of the enterprise risk management or related research.
The main references are:
[1] Lee, L., & Liang, T. W. (2022, March). Better business sentiment amid robust SME financing growth. Strategic Credit & Economic Analytics, SME Bank Group. https://www.smebank.com.my/documents/d/guest/betterbusinesssentiment_march2022
Contribution: PD
[2] Galow, B., Georgescu, O.-M., & Ponte Marques, A. (2024). Loss-given-default and macroeconomic conditions (Working Paper Series No. 2954). European Central Bank. https://www.ecb.europa.eu//pub/pdf/scpwps/ecb.wp2954~1d1f8942c9.en.pdf
Contribution: recovery rate and LGD
[3] Kuriakose, S., & Tiew, H. (2022). Malaysian SME Program Efficiency Review. World Bank Group. http://documents.worldbank.org/curated/en/099255003152238688
Contribution: Loan Share
[4] Asian Development Bank. (2023). Asia SME Monitor 2023 - Malaysia [Data set]. ADB Data Library. https://data.adb.org/dataset/2023-asia-small-and-medium-sized-enterprise-monitor
Contribution: Expected Return
DOI: 10.17632/3xdbrchxgw.1
title: 2022 Malaysia SME Industry Risk Indicators

description: This is summarize dataset of financial risk indicators of Malaysia SMEs. There are 5 parameters including probability of default (PD), recovery rate, loss given default (LGD), loan share and expected return. There are also 5 sectors in the dataset, agriculture, manufacturing, construction, services and refining (others).
Probability of Default (PD): Extracted from Lee & Liang (2022). All the data extracted are for the purpose of estimation of the enterprise risk management or related research.
The main references are:
[1] Lee, L., & Liang, T. W. (2022, March). Better business sentiment amid robust SME financing growth. Strategic Credit & Economic Analytics, SME Bank Group. https://www.smebank.com.my/documents/d/guest/betterbusinesssentiment_march2022
Contribution: PD
[2] Galow, B., Georgescu, O.-M., & Ponte Marques, A. (2024). Loss-given-default and macroeconomic conditions (Working Paper Series No. 2954). European Central Bank. https://www.ecb.europa.eu//pub/pdf/scpwps/ecb.wp2954~1d1f8942c9.en.pdf
Contribution: recovery rate and LGD
[3] Kuriakose, S., & Tiew, H. (2022). Malaysian SME Program Efficiency Review. World Bank Group. http://documents.worldbank.org/curated/en/099255003152238688
Contribution: Loan Share
[4] Asian Development Bank. (2023). Asia SME Monitor 2023 - Malaysia [Data set]. ADB Data Library. https://data.adb.org/dataset/2023-asia-small-and-medium-sized-enterprise-monitor
Contribution: Expected Return
DOI: 10.17632/zgczszvn6x
title: Mechanisms of Solid–Liquid Component Regulation of Hydrothermal Humification Productions on Straw Decomposition and Soil Carbon Sequestration under Freeze-Thaw Conditions

description: These are the experimental data collected from the indoor simulation of freeze-thaw cycles on the decomposition of paddy straw and the promotion of soil carbon fixation by the hydrothermal humification products (solid and liquid products). The experimental data include the rate of straw decomposition, soil carbon-related indicators (SOM/TOC/DOC/MBC/POC/MAOC), enzyme activities related to soil carbon transformation (hydrolytic enzymes and oxidase), as well as related microbial data.
DOI: 10.17632/zgczszvn6x.1
title: Mechanisms of Solid–Liquid Component Regulation of Hydrothermal Humification Productions on Straw Decomposition and Soil Carbon Sequestration under Freeze-Thaw Conditions

description: These are the experimental data collected from the indoor simulation of freeze-thaw cycles on the decomposition of paddy straw and the promotion of soil carbon fixation by the hydrothermal humification products (solid and liquid products). The experimental data include the rate of straw decomposition, soil carbon-related indicators (SOM/TOC/DOC/MBC/POC/MAOC), enzyme activities related to soil carbon transformation (hydrolytic enzymes and oxidase), as well as related microbial data.