cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8186920
title: PHYAFB: A new database of the analysis of the physiological needs in amateur female basketball during official matches

description: The Physiological Analysis of Female Basketball players (PHYAFB) database is a valuable resource for studying the physiological demands placed on female amateur basketball players during official games. This database includes heart rate data from ten female players aged between 18 and 26 years, which were collected during ten high-stress matches played during a crucial relegation phase. In total, more than 50000 heart rate samples have been reported. The data is stored in CSV format, facilitating easy access and analysis, and the database comes with \textit{Python} source code to support initial examination. The primary aim of the PHYAFB database is to provide a useful reference for other teams facing similar situations. Furthermore, the database represents a unique and valuable resource for sports scientists, coaches, and trainers seeking to comprehend the physiological demands of female basketball players during official competitions. Through the analysis of heart rate data, coaches and trainers can identify the intensity and duration of physical activity during games, enabling the development of more effective training programs. Additionally, the database can be used to compare the physiological demands placed on male and female basketball players or to investigate the impact of different game strategies on player performance.
DOI: 10.5281/zenodo.8188566
title: PHYAFB: A new database of the analysis of the physiological needs in amateur female basketball during official matches

description: The Physiological Analysis of Female Basketball players (PHYAFB) database is a valuable resource for studying the physiological demands placed on female amateur basketball players during official games. This database includes heart rate data from ten female players aged between 18 and 26 years, which were collected during ten high-stress matches played during a crucial relegation phase. In total, more than 50000 heart rate samples have been reported. The data is stored in CSV format, facilitating easy access and analysis, and the database comes with Python source code to support initial examination. The primary aim of the PHYAFB database is to provide a useful reference for other teams facing similar situations. Furthermore, the database represents a unique and valuable resource for sports scientists, coaches, and trainers seeking to comprehend the physiological demands of female basketball players during official competitions. Through the analysis of heart rate data, coaches and trainers can identify the intensity and duration of physical activity during games, enabling the development of more effective training programs. Additionally, the database can be used to compare the physiological demands placed on male and female basketball players or to investigate the impact of different game strategies on player performance.
DOI: 10.5281/zenodo.10054915
title: PHYAFB: A new database of the analysis of the physiological needs in amateur female basketball during official matches

description: The Physiological Analysis of Female Basketball players (PHYAFB) database is a valuable resource for studying the physiological demands placed on female amateur basketball players during official games. This database includes heart rate data from ten female players aged between 18 and 26 years, which were collected during ten high-stress matches played during a crucial relegation phase. In total, almost 350,000 heart rate samples have been reported. The data is stored in CSV format, facilitating easy access and analysis, and the database comes with Python source code to support initial examination.
The primary aim of the PHYAFB database is to provide a useful reference for other teams facing similar situations. Furthermore, the database represents a unique and valuable resource for sports scientists, coaches, and trainers seeking to comprehend the physiological demands of female basketball players during official competitions. Through the analysis of heart rate data, coaches and trainers can identify the intensity and duration of physical activity during games, enabling the development of more effective training programs. Additionally, the database can be used to compare the physiological demands placed on male and female basketball players or to investigate the impact of different game strategies on player performance.
DOI: 10.5281/zenodo.8186919
title: PHYAFB: A new database of the analysis of the physiological needs in amateur female basketball during official matches

description: The Physiological Analysis of Female Basketball players (PHYAFB) database is a valuable resource for studying the physiological demands placed on female amateur basketball players during official games. This database includes heart rate data from ten female players aged between 18 and 26 years, which were collected during ten high-stress matches played during a crucial relegation phase. In total, almost 350,000 heart rate samples have been reported. The data is stored in CSV format, facilitating easy access and analysis, and the database comes with Python source code to support initial examination.
The primary aim of the PHYAFB database is to provide a useful reference for other teams facing similar situations. Furthermore, the database represents a unique and valuable resource for sports scientists, coaches, and trainers seeking to comprehend the physiological demands of female basketball players during official competitions. Through the analysis of heart rate data, coaches and trainers can identify the intensity and duration of physical activity during games, enabling the development of more effective training programs. Additionally, the database can be used to compare the physiological demands placed on male and female basketball players or to investigate the impact of different game strategies on player performance.
DOI: 10.6084/m9.figshare.24961617
title: <b>Association between </b><b><i>ACE</i></b><b> (I/D) polymorphism and playing position in Brazilian junior handball players</b>

description: Handball is team sport a characterized by fast-paced, dynamic play, involving a combination of physical strength, agility, and tactical skills. Sports performance can be influenced by various factors that can have effects on athletes and the influence of genetic polymorphisms on athletic performance has been widely explored. This study investigated the interactions between the polymorphisms ACE (I/D) and their association with endurance and strength phenotypes in Brazilian handball junior players considering their playing position. 90 junior handball players and 72 age-matched controls were evaluated. DNA was extracted using a QIAamp DNA Blood Mini kit and ACE I/D polymorphism was genotyped by conventional PCR followed by electrophorese in agarose gel. All participants were anthropometrically evaluated and performed the standing long jump (SLJ) and medicine ball throw (MBT) tests. Handball players presented superior performance in the SLJ and MBT tests compared to the control group in all playing positions (p &lt; 0.01). No significant differences observed in ACE genotype or allele frequencies when comparing handball athletes and controls or among playing positions. However, a significant difference was observed when comparing all athletes to controls using the over-dominant model (p = 0.035). Additionally, athletes with II + ID and II + DD genotypes exhibited superior performance in the standing long jump test (p = 0.04) and ID athletes demonstrated increased height compared to II + DD counterparts (p = 0.04). In conclusion, our data showed a potential association between ACE (I/D) polymorphism and the overall athletic predisposition in handball players, with no association with playing position.
DOI: 10.7924/r4rn3c061
title: Spatially-dependent material uncertainties in anisotropic nonlinear elasticity: Stochastic modeling, identification, and propagation

description: This paper develops a stochastic model for the spatially-dependent material parameters parameterizing anisotropic strain energy density functions. The construction is cast within the framework of information theory, which is invoked to derive a least-informative model while ensuring consistency with theoretical requirements in finite elasticity. Specifically, almost sure polyconvexity and uniform growth conditions are enforced through proper repulsion constraints and regularization, hence making the forward problem of uncertainty propagation well posed. In addition, transformations arising from the linearization procedure are introduced for consistency and induce statistical dependencies in the primary variables. The latter include material moduli, a weight balancing between the isotropic and anisotropic contributions, and the angle defining the structural tensors. The identification of the model is subsequently performed, using an existing database on human arterial walls. Maximum likelihood estimators are obtained and provided for the adventitia, media, and intima layers, which enables the use of the proposed model as a generative surrogate for, e.g., training and classification in data-driven approaches integrating inter-patient variability. Finally, uncertainty propagation on a realistic, patient-specific geometry is conducted to demonstrate the efficiency of the stochastic modeling framework.
DOI: 10.17632/3h8347w6d9.2
title: SDesti: An R package for the analysis of aquatic benthos environmental studies’ data

description: Data analysis is one of the most relevant steps of aquatic benthic environmental monitoring and research studies, and should be a fundamental consideration in both the planning (i.e., defining appropriate sampling design strategies) and implementation phases (application of appropriate standardized sampling procedures). A common objective of these studies is to identify relationships between environmental stressors and benthic bioindicator metrics. However, assessing these relationships is a complex process. Multivariate regression model adjustment coupled with forward and backward model selection routines is an appropriate complementary statistical analysis tool to test for the existence of statistically significant associations between a non-autocorrelated biological response and each variable within a group of environmental covariates included in a model. With this in mind, we developed SDesti, a user-friendly R package to analyze benthos data (number of individuals, biomass, chlorophyll concentration, or biological indices, excluding beta diversity metrics). SDesti contains four user accessible functions. AnalysisDescriptives() and Estimation() give information on the quality, homogeneity and representativeness of the data for one sampling campaign for one site. TimeLineAnalysisDescriptives() performs the descriptive analysis that usually precedes the adjustment of a regression model. TimeLineAnalysis() automatically adjusts an adequate regression model (linear, Poisson, quasipoisson, or negative binomial) and also returns the necessary measures and graphics to evaluate the quality of the adjustment and verify the model assumptions. SDesti greatly simplifies the process of data analysis and can be easily used by non-statisticians. The analytical package includes a complete manual that provides detailed information: on the data structure requirements, on the variable nomenclature rules and program operating procedures, on the data analysis (complemented with examples) and on the interpretation of the results (type ??SDesti on R console). SDesti eliminates redundancy, reduces human error and, coupled with a suitable sampling design, standard sampling and sample treatment procedures, it contributes to improve the consistency of the results in environmental studies. SDesti binary for windows users and installation instructions can be found below.
DOI: 10.17632/3h8347w6d9.3
title: SDesti: An R package for the analysis of aquatic benthos environmental studies’ data

description: Data analysis is one of the most relevant steps of aquatic benthic environmental monitoring and research studies, and should be a fundamental consideration in both the planning (i.e.,  defining appropriate sampling design strategies) and implementation phases (application of appropriate standardized sampling procedures). A common objective of these studies is to identify relationships between environmental stressors and benthic bioindicator metrics. However, assessing these relationships is a complex process. Multivariate regression model adjustment coupled with forward and backward model selection routines is an appropriate complementary statistical analysis tool to test for the existence of statistically significant associations between a non-autocorrelated biological response and each variable within a group of environmental covariates included in a model. With this in mind, we developed SDesti, a user-friendly R package to analyze benthos data (number of individuals, biomass, chlorophyll concentration, or biological indices, excluding beta diversity metrics). SDesti contains four user accessible functions. AnalysisDescriptives() and Estimation() give information on the quality, homogeneity and representativeness of the data for one sampling campaign for one site. TimeLineAnalysisDescriptives() performs the descriptive analysis that usually precedes the adjustment of a regression model. TimeLineAnalysis() automatically adjusts an adequate regression model (linear, Poisson, quasipoisson, or negative binomial) and also returns the necessary measures and graphics to evaluate the quality of the adjustment and verify the model assumptions.
SDesti greatly simplifies the process of data analysis and can be easily used by non-statisticians. The analytical package includes a complete manual that provides detailed information: on the data structure requirements, on the variable nomenclature rules and program operating procedures, on the data analysis (complemented with examples) and on the interpretation of the results (type ??SDesti on R console). SDesti eliminates redundancy, reduces human error and, coupled with a suitable sampling design, standard sampling and sample treatment procedures, it contributes to improve the consistency of the results in environmental studies.
SDesti binary for windows users and installation instructions can be found below.
DOI: 10.17632/3h8347w6d9.4
title: SDesti: An R package for the analysis of aquatic benthos environmental studies’ data

description: Data analysis is one of the most relevant steps of aquatic benthic environmental monitoring and research studies, and should be a fundamental consideration in both the planning (i.e.,  defining appropriate sampling design strategies) and implementation phases (application of appropriate standardized sampling procedures). A common objective of these studies is to identify relationships between environmental stressors and benthic bioindicator metrics. However, assessing these relationships is a complex process. Multivariate regression model adjustment coupled with forward and backward model selection routines is an appropriate complementary statistical analysis tool to test for the existence of statistically significant associations between a non-autocorrelated biological response and each variable within a group of environmental covariates included in a model. With this in mind, we developed SDesti, a user-friendly R package to analyze benthos data (number of individuals, biomass, chlorophyll concentration, or biological indices, excluding beta diversity metrics). SDesti contains four user accessible functions. AnalysisDescriptives() and Estimation() give information on the quality, homogeneity and representativeness of the data for one sampling campaign for one site. TimeLineAnalysisDescriptives() performs the descriptive analysis that usually precedes the adjustment of a regression model. TimeLineAnalysis() automatically adjusts an adequate regression model (linear, Poisson, quasipoisson, or negative binomial) and also returns the necessary measures and graphics to evaluate the quality of the adjustment and verify the model assumptions.
SDesti greatly simplifies the process of data analysis and can be easily used by non-statisticians. The analytical package includes a complete manual that provides detailed information: on the data structure requirements, on the variable nomenclature rules and program operating procedures, on the data analysis (complemented with examples) and on the interpretation of the results (type ??SDesti on R console). SDesti eliminates redundancy, reduces human error and, coupled with a suitable sampling design, standard sampling and sample treatment procedures, it contributes to improve the consistency of the results in environmental studies.
SDesti binary for windows users and installation instructions can be found below.
DOI: 10.17632/3h8347w6d9.5
title: SDesti: An R package for the analysis of aquatic benthos environmental studies’ data

description: Data analysis is one of the most relevant steps of aquatic benthic environmental monitoring and research studies, and should be a fundamental consideration in both the planning (i.e., defining appropriate sampling design strategies) and implementation phases (application of appropriate standardized sampling procedures). A common objective of these studies is to identify relationships between environmental stressors and benthic bioindicator metrics. However, assessing these relationships is a complex process. Multivariate regression model adjustment coupled with forward and backward model selection routines is an appropriate complementary statistical analysis tool to test for the existence of statistically significant associations between a non-autocorrelated biological response and each variable within a group of environmental covariates included in a model. With this in mind, we developed SDesti, a user-friendly R package to analyze benthos data (number of individuals, biomass, chlorophyll concentration, or biological indices, excluding beta diversity metrics). SDesti contains four user accessible functions. AnalysisDescriptives() and Estimation() give information on the quality, homogeneity and representativeness of the data for one sampling campaign for one site. TimeLineAnalysisDescriptives() performs the descriptive analysis that usually precedes the adjustment of a regression model. TimeLineAnalysis() automatically adjusts an adequate regression model (linear, Poisson, quasipoisson, or negative binomial) and also returns the necessary measures and graphics to evaluate the quality of the adjustment and verify the model assumptions.
SDesti greatly simplifies the process of data analysis and can be easily used by non-statisticians. The analytical package includes a complete manual that provides detailed information: on the data structure requirements, on the variable nomenclature rules and program operating procedures, on the data analysis (complemented with examples) and on the interpretation of the results (type ??SDesti on R console). SDesti eliminates redundancy, reduces human error and, coupled with a suitable sampling design, standard sampling and sample treatment procedures, it contributes to improve the consistency of the results in environmental studies.
SDesti binary for windows users and installation instructions can be found below.
Compiled for R 4.3.2 version.
Refer to the program PDF manual for a detailed description of the data structures, functions, data analyses and interpretation of results. Type ??SDesti on R, or RStudio consoles and select the PDF file.
Note: RStudio 2023.09.1 has a bug that delivers an error message when trying to open PDF vignettes (program manuals). Use R 4.3.2 console to open SDesti's PDF manual.
DOI: 10.17632/3h8347w6d9.1
title: SDesti: An R package for the analysis of aquatic benthos environmental studies’ data

description: Data analysis is one of the most relevant steps of aquatic benthic environmental monitoring and research studies, and should be a fundamental consideration in both the planning (i.e., defining appropriate sampling design strategies) and implementation phases (application of appropriate standardized sampling procedures). A common objective of these studies is to identify relationships between environmental stressors and benthic bioindicator metrics. However, assessing these relationships is a complex process. Multivariate regression model adjustment coupled with forward and backward model selection routines is an appropriate complementary statistical analysis tool to test for the existence of statistically significant associations between a non-autocorrelated biological response and each variable within a group of environmental covariates included in a model. With this in mind, we developed SDesti, a user-friendly R package to analyze benthos data (number of individuals, biomass, chlorophyll concentration, or biological indices, excluding beta diversity metrics). SDesti contains four user accessible functions. AnalysisDescriptives() and Estimation() give information on the quality, homogeneity and representativeness of the data for one sampling campaign for one site. TimeLineAnalysisDescriptives() performs the descriptive analysis that usually precedes the adjustment of a regression model. TimeLineAnalysis() automatically adjusts an adequate regression model (linear, Poisson, quasipoisson, or negative binomial) and also returns the necessary measures and graphics to evaluate the quality of the adjustment and verify the model assumptions. SDesti greatly simplifies the process of data analysis and can be easily used by non-statisticians. The analytical package includes a complete manual that provides detailed information: on the data structure requirements, on the variable nomenclature rules and program operating procedures, on the data analysis (complemented with examples) and on the interpretation of the results (type ??SDesti on R console). SDesti eliminates redundancy, reduces human error and, coupled with a suitable sampling design, standard sampling and sample treatment procedures, it contributes to improve the consistency of the results in environmental studies. SDesti binary for windows users and installation instructions can be found below.
DOI: 10.5281/zenodo.10470739
title: Data for "Tailored and Externally Corrected Coupled Cluster with Quantum Inputs"

description: This data set accompanies the manuscript "Tailored and Externally Corrected Coupled Cluster with Quantum Inputs", which can be found on arXiv.

The environment.yml file contains specifications for a conda environment that (hopefully) sets up all dependencies to run the notebooks contained herein.


- matchgate_shadow_statistics contains raw data for the 8-qubit shadow recorded on a 22-layer QNP-VQE circuit of N2/cc-pVDZ. The notebook `shadow_estimation.ipynb` contains the code to produce the plots for Figures C1 and C2.


- error_model contains the raw data from noisy TCCSD calculations on 13 molecules with different basis sets as a `pandas.DataFrame` in `error_model_noise_tccsd.h5`.

A script `test_diagnostics.py`, including the molecular geometries, computes the CC diagonstics using `pyscf` and can be run with `pytest` using the `pytest-harvest` plugin,

and writes them to `diagnostics_data_general.h5`.

The notebook `error_model_resources.ipynb` includes the following tasks:

    1. Read in the data and create a new `DataFrame` which contains the average TCCSD energy error (over 30 samples)

    2. Power law fit of the whole data set to extract the exponents $\beta$ and $\gamma$ (eq 9, Table C3)

    3. Bootstrap estimation of the exponents' standard errors

    4. The per-molecule power law fit with fixed exponents to obtain the molecule-specific prefactor $a$

    5. Read-in of diagnostics data and creation of diagnostic->prefactor linear fit (Figure 4, eq C7)

    6. Shot budget estimation for the N2 dissociation curve (requires `pyscf`)