cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/knvtw3hg8p.3
title: Reprodution Folder"Kinship Intensity and Innovation Ambidexterity in Family Businesses: Venturing into the Unknown"

description: This dataset contains the replication materials for the article "Kinship Intensity and Innovation Ambidexterity in Family Businesses: Venturing into the Unknown" submitted in Economic Modelling.
The sample consists of 7,989 firm-year observations of Chinese A-share listed family businesses from 2012 to 2020. The data are primarily sourced from the China Stock Market & Accounting Research (CSMAR) database, with supplementary manual collection for kinship relationship information.
Key variables include:

Exploratory innovation (Expr): Measured by the number of exploratory patent applications (invention patents and design patents) plus one, followed by a natural logarithm transformation. Source: CSMAR patent database.
Exploitative innovation (Expi): Measured by the number of exploitative patent applications (utility model patents) plus one, followed by a natural logarithm transformation. Source: CSMAR patent database.
Kinship intensity (Kinship): The primary independent variable, calculated as the sum of kinship coefficients between the de facto controller and all family members on the board of directors. Kinship coefficients follow standard genealogical weighting (e.g., 0.5 for parent-child or spouses, 0.25 for siblings or grandchildren, etc.).
Control variables: Firm size, age, leverage, ROA, cash flow, board size, independent director ratio, ownership concentration, industry and year dummies, and other standard firm-level controls (full list available in the manuscript).
Instrumental variable: Per capita rice cultivation area in 1978 in the birthplace of the ultimate controller (used for IV estimation).

Important Privacy Notice:
The variables related to kinship relationships (e.g., Kinship, Kinship_core, Kinship_noncore, Kinship_blood, Kinship_inlaw, and family member identifiers) were manually collected from multiple sources including annual reports, prospectuses, listing announcements, executive supervision reports, news articles, and other public and semi-public channels. These data involve detailed familial relationships of entrepreneurs and controlling families, which are considered highly sensitive personal information.
Due to privacy and ethical considerations, researchers interested in accessing or replicating results using the kinship variables are kindly requested to contact the corresponding author or the first author directly for collaboration or controlled access under appropriate data use agreements.
The remaining variables (financial, patent, governance, and instrumental variable data) are derived from public sources (primarily CSMAR) and can be independently reconstructed by licensed users of the database using the variable definitions and sample selection criteria provided in the manuscript.
DOI: 10.3535/vc4-923-9yg
title: Epilobium palustre L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.15156/bio/sh0013300.10fu
title: SH0013300.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.17632/bp8svtfbsz
title: Chinese adolescents
DOI: 10.3535/t7t-pjk-x77
title: Elatine orthosperma Düben

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.5281/zenodo.18043760
title: Data and codes for TA
DOI: 10.15156/bio/sh0013301.10fu
title: SH0013301.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.17632/bp8svtfbsz.2
title: Chinese adolescents
DOI: 10.17632/psk2k6td6p
title: Automated Colony-Forming Unit (CFU) Counting of Bacteria Using Digital Image Analysis Through Computer Vision with Python - Supplementary Data

description: This supplementary material corresponds to an Excel file (.xlsx) that documents key information associated with the development and validation of an automated bacterial colony counter based on computer vision, implemented in Python. The file contains two main sheets. Sheet 1 (Table S1) describes the photographic devices used to acquire images from the database, including device type, model, and technical specifications relevant to image analysis. Sheet 2 presents the results of automatic image processing, including the number of colonies detected by the algorithm, the manual reference count, true positives, false positives, and false negatives. Based on these data, the system performance metrics (precision, recall, and F-measure) are reported, as well as the processing time per image. 
This dataset supports the quantitative evaluation of the performance and computational efficiency of the proposed colony counter.
DOI: 10.3535/qjs-0vn-q7r
title: Elatine triandra Schkuhr

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.5281/zenodo.18043759
title: Data and codes for TA
DOI: 10.15156/bio/sh0013302.10fu
title: SH0013302.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.17632/psk2k6td6p.1
title: Automated Colony-Forming Unit (CFU) Counting of Bacteria Using Digital Image Analysis Through Computer Vision with Python - Supplementary Data

description: This supplementary material corresponds to an Excel file (.xlsx) that documents key information associated with the development and validation of an automated bacterial colony counter based on computer vision, implemented in Python. The file contains two main sheets. Sheet 1 (Table S1) describes the photographic devices used to acquire images from the database, including device type, model, and technical specifications relevant to image analysis. Sheet 2 presents the results of automatic image processing, including the number of colonies detected by the algorithm, the manual reference count, true positives, false positives, and false negatives. Based on these data, the system performance metrics (precision, recall, and F-measure) are reported, as well as the processing time per image. 
This dataset supports the quantitative evaluation of the performance and computational efficiency of the proposed colony counter.
DOI: 10.3535/hkv-63y-j5g
title: Tilia × europaea L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.15156/bio/sh0013303.10fu
title: SH0013303.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.17632/gcfgxwdscw
title: Seattle collision dataset

description: the Seattle collision dataset 2015-2020
DOI: 10.25505/fiz.icsd.cc2k8916
title: CSD 2361457: Experimental Crystal Structure Determination

description: Related Article: Lei Jia, Yi-Xin Liu, Xin-Xiong Li, Cai Sun, Shou-Tian Zheng|2024|Inorg.Chem.|63|14308|doi:10.1021/acs.inorgchem.4c02447
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.17632/gcfgxwdscw.1
title: Seattle collision dataset

description: the Seattle collision dataset 2015-2020
DOI: 10.3535/3d9-b6y-q5l
title: Tilia × europaea L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.15156/bio/sh0013304.10fu
title: SH0013304.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0013305.10fu
title: SH0013305.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.3535/a25-zt6-57s
title: Impatiens noli-tangere L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.15156/bio/sh0013306.10fu
title: SH0013306.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.17632/x5r4tshp4c
title: Predictive capabilities of novel deep neural networks learning for long-term streamflow prediction: insights from the barandouz chay river

description: Dams and reservoirs are essential for public health, food security, economic growth, and flood protection, especially as climate change challenges sustainable water management. This study explores regression-based deep learning models for long-term prediction of Barandouz River flow. Four models were employed: Continuous Weighted Residual Method (CWERM), Physics-Informed Koopman Networks (PI-Koopman), Convolutional Neural Network (CNN), and Implicit Neural Representations (INRs). Forty years (1980–2020) of daily time-series data were used, including temperature, precipitation, relative humidity, wind speed, and discharge. To improve prediction accuracy, lagged discharge (up to three days) was incorporated. Data preprocessing included missing value reconstruction (KNN), outlier removal, and normalization to mitigate overfitting risks. Feature selection and scenario construction were guided by the Minimum Redundancy Maximum Relevance (MRMR) method, resulting in five predictive scenarios. Model evaluation employed multiple criteria (R², MAE, RMSE, MAD, NSE, and KGE). Results show that Scenario 4 excluding historical flow variables exhibited severe predictive failure across all models in both training and testing phases, with testing R² ranging from 0.087 (CWERM) to 0.189 (PI-Koopman) and NSE remaining below 0.23, highlighting the critical role of antecedent discharge in modeling hydrological memory. In contrast, CNN consistently outperformed all other models in both phases. During testing, CNN achieved R² values of 0.991 (SN1), 0.992 (SN3), and 0.981 (SN5), with corresponding NSE &gt; 0.910 and RMSE as low as 4.157 m³/s (SN5). Its average testing R² (0.977) was 14.0–18.3% higher than those of ANN-OA (0.857), INRs (0.855), and PI-Koopman (0.826), while its average RMSE (7.25 m³/s) was 34.5–44.1% lower. PI-Koopman, despite moderate performance (best testing R² = 0.858 in SN3), maintained predictions within physically reasonable bounds, particularly in degraded scenarios like SN4. Overall, the results confirm CNN’s exceptional ability to capture complex, nonlinear streamflow dynamics under historical conditions, while also underscoring the limitations of purely data-driven approaches when hydrological stationarity is violated providing crucial insights for deploying deep learning models in semi-arid, human-influenced basins under climate uncertainty.
DOI: 10.15156/bio/sh0013307.10fu
title: SH0013307.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.17632/x5r4tshp4c.1
title: Predictive capabilities of novel deep neural networks learning for long-term streamflow prediction: insights from the barandouz chay river

description: Dams and reservoirs are essential for public health, food security, economic growth, and flood protection, especially as climate change challenges sustainable water management. This study explores regression-based deep learning models for long-term prediction of Barandouz River flow. Four models were employed: Continuous Weighted Residual Method (CWERM), Physics-Informed Koopman Networks (PI-Koopman), Convolutional Neural Network (CNN), and Implicit Neural Representations (INRs). Forty years (1980–2020) of daily time-series data were used, including temperature, precipitation, relative humidity, wind speed, and discharge. To improve prediction accuracy, lagged discharge (up to three days) was incorporated. Data preprocessing included missing value reconstruction (KNN), outlier removal, and normalization to mitigate overfitting risks. Feature selection and scenario construction were guided by the Minimum Redundancy Maximum Relevance (MRMR) method, resulting in five predictive scenarios. Model evaluation employed multiple criteria (R², MAE, RMSE, MAD, NSE, and KGE). Results show that Scenario 4 excluding historical flow variables exhibited severe predictive failure across all models in both training and testing phases, with testing R² ranging from 0.087 (CWERM) to 0.189 (PI-Koopman) and NSE remaining below 0.23, highlighting the critical role of antecedent discharge in modeling hydrological memory. In contrast, CNN consistently outperformed all other models in both phases. During testing, CNN achieved R² values of 0.991 (SN1), 0.992 (SN3), and 0.981 (SN5), with corresponding NSE &gt; 0.910 and RMSE as low as 4.157 m³/s (SN5). Its average testing R² (0.977) was 14.0–18.3% higher than those of ANN-OA (0.857), INRs (0.855), and PI-Koopman (0.826), while its average RMSE (7.25 m³/s) was 34.5–44.1% lower. PI-Koopman, despite moderate performance (best testing R² = 0.858 in SN3), maintained predictions within physically reasonable bounds, particularly in degraded scenarios like SN4. Overall, the results confirm CNN’s exceptional ability to capture complex, nonlinear streamflow dynamics under historical conditions, while also underscoring the limitations of purely data-driven approaches when hydrological stationarity is violated providing crucial insights for deploying deep learning models in semi-arid, human-influenced basins under climate uncertainty.
DOI: 10.15156/bio/sh0013308.10fu
title: SH0013308.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.