cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.11394228
title: The spatial extent and the dispersal strategy of species shape the occupancy frequency distribution of stream insect assemblages

description: This is the datasets and R script for our  manuscript titled "The spatial extent and the dispersal strategy of species shape the occupancy frequency distribution of stream insect assemblages."


Study area and sampling of Mecsek_species_Szivaketal.xlsx data


The study area is situated in the Mecsek Mountains in Hungary. Stream insects were collected using the kick and sweep sampling technique. A hand net with a frame width of 25 cm and a mesh size of 1000 µm was employed. The collection involved disturbing an area of 0.25 × 0.25 m. We employed a stratified random multihabitat sampling procedure. We collected 20 sample units from the major microhabitats in proportion to their occurrence within the reaches. Each sample unit was individually processed and sorted in the laboratory to ensure thorough and accurate analysis.


Disperse_trait_Szivaketal.xlsx data is based on DISPERSE database (Sarremejane et al., 2020)


Methods for microhabitat_coverage_reach_transect.xlsx: Prior to sampling, we divided each 100 m long reach into 20 transects of comparable length and created microhabitat coverage maps. Using these maps, we assessed the proportionate coverage of different microhabitat types within each reach


References:


Sarremejane, R., Cid, N., Stubbington, R., Datry, T., Alp, M., Cañedo-Argüelles, M., Cordero-Rivera, A., Csabai, Z., Gutiérrez-Cánovas, C., Heino, J., Forcellini, M., Millán, A., Paillex, A., Pařil, P., Polášek, M., Tierno de Figueroa, J. M., Usseglio-Polatera, P., Zamora-Muñoz, C., & Bonada, N. (2020). DISPERSE, a trait database to assess the dispersal potential of European aquatic macroinvertebrates. Scientific Data, 7(1), 386. https://doi.org/10.1038/s41597-020-00732-7
DOI: 10.5281/zenodo.10610727
title: Identifying the interplay between protective measures and settings on the SARS-CoV-2 transmission using a Bayesian network [Dataset]

description: data07B.csv: dataset for the study of the SARS-CoV-2 transmission.

CPTNetica.txt: conditional probabilities tables of each variable given through Netica once the BN obtained in R code is loaded.

code01.R: code to learn structure and parameters of the SARS-CoV-2 BN model.
DOI: 10.5281/zenodo.10442171
title: Experimental and Theoretical Study into IdentifyingIdentifying Active Sites of Supported SnOx Nanoparticles for Electrochemical CO2 Reduction Using Machine-Learned Interatomic Potentials

description: SnO2 has received great attention for electrochemical CO2 reduction reaction (CO2RR), however, it still suffers from low activity. Moreover, SnO2 structure at the atomic level and the nature of active sites are still ambiguous due to the dynamism of surface structure and difficulty in structure characterization under electrochemical conditions. Herein, two common supports i.e. Vulcan carbon (C) and TiO2 were used to enhance the performance of SnO2. SnO2/C demonstrates over 90% selectivity for C1 products (HCOO− and CO) over a wide potential range while SnO2/TiO2 shows lower activities. Furthermore, our study outlines an explicit method for multiscale simulation to investigate SnO2 reduction and establish a correlation between SnOx structures and their CO2RR performance. For that reason, we used the Machine Learning interatomic potential method to generate SnOx nanoparticles to assess the possible synthesized active sites. Further, computational selectivity is analyzed with Density Functional Theory simulations to identify the key differences between the binding energies of *H and *CO2−, which are correlated with a surface oxygen amount. In addition, electrolysis of CO2 at various temperatures in a neutral electrolyte revealed that the application window for this catalyst is between 12 and 30 °C. This study offers an in-depth understanding and insight into the rational design and application of Sn-based electrocatalysts for CO2RR.
DOI: 10.15468/dl.v4dxms
title: Occurrence Download

description: A dataset containing 790 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Hirtella eriandra Benth.)"
 ]
}

 The dataset includes 790 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005335-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jm4dgm
title: Occurrence Download

description: A dataset containing 2037742 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 "StateProvince is one of (cundinamarca)"
 ]
}

 The dataset includes 2037742 records from 757 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005328-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hm7e8j
title: Occurrence Download

description: A dataset containing 54 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Launaea polydichotoma (Ostenf) Amin ex N.Kilian"
 ]
}

 The dataset includes 54 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037405-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17631/rd-2024-0008-ddat
title: Feasibilty data for MAST (Movement and storytelling) trial
DOI: 10.15468/dl.d36gvh
title: Occurrence Download

description: A dataset containing 36642 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "OccurrenceStatus is Present",
 "TaxonKey is Hydrocharis morsus-ranae L.",
 "Year 2000-2020"
 ]
}

 The dataset includes 36642 records from 299 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037407-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.25896577
title: <b>Comparative genomics uncovers molecular adaptatio</b><b>ns for cetacean deep-sea diving</b>

description: Cetaceans exhibit a remarkable diversity in their diving capabilities, associated with significant physiological adaptations to extreme marine environments. Although there is plenty of literature on cetacean aquatic adaptations, studies on the intraspecies differences (especially on the diving adaptations of exceptional divers) are scarce. A total of 5810 high-quality one-to-one gene homologous sequences were identified from the genomes of 20 cetaceans, which prepared for subsequent comparative genomic analysis to explore the diving ability of super-divers.
DOI: 10.34740/kaggle/dsv/8498501
title: Tulu Alphabet Dataset
DOI: 10.15468/dl.yd9vnz
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bulinus reticulatus Mandahl-Barth, 1954"
 ]
}

 The dataset includes 10 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037409-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.25897453
title: BAM files and outputs from NAGATA, StringTie2, Bambu, and Isoquant, Figure 6, NAGATA

description: BAM files and associated inputs used to generate data shown in Figure 6 of NAGATA manuscript. Outputs from NAGATA are also included
DOI: 10.15468/dl.wx23az
title: Occurrence Download

description: A dataset containing 143 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Leguminosae"
 ]
}

 The dataset includes 143 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037410-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uhn6kk
title: Occurrence Download

description: A dataset containing 89 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Myrcia carvalhoi NicLugh."
 ]
}

 The dataset includes 89 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037411-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10937408
title: Dataset for "LLM-aided explanations of EDA synthesis errors"

description: Dataset for "LLM-aided explanations of EDA synthesis errors"


Authors: Siyu Qiu, Benjamin Tan, Hammond Pearce

 

This Zenodo contains the open-source data used for the ISLAD submission "LLM-aided explanations of EDA synthesis errors" which aimed to use OpenAI LLMs for generating novice-focused explanations of common synthesis errors.

Error explanations for RTL and HDL Code with OpenAI's LLMs

Welcome to our error explanation tool for RTL and HDL code in Verilog and VHDL! This repository helps you generate error explanations for your code using OpenAI's models, making it easier to find and fix bugs.

Directory Structure




- `new_structure/`: Contains all bugs in separate files, along with labelling CSV files that match the LLM responses for each bug.

- `bug_id/`: Each bug has responses from gpt-3.5-turbo (40 responses), gpt-4 (4 responses), and gpt-4-turbo-preview (4 responses).


- `rtl/`: Includes RTL code used for bugs, compatible with both Quartus and Vivado.


- `Quartus/`: Contains Quartus project files (.qpf) and constraints files (.qsf).


- `Vivado/`: Holds Vivado project files (.xpr) and constraints files (.xdc).


- `llm_responses/`: Contains CSV files with records of the generated LLM responses for each bug.


- `labelling/`: Contains CSV files with manually scored metrics for evaluating the LLM responses.


- `error_list.csv`: This file lists all bugs with details like bug id, type, IDE, file name, language, and error message. main.py uses this file to process bugs.


- `main.py`: Use this script to generate error explanations for your RTL and HDL code with OpenAI's models. It reads bug information from error_list.csv, loads the buggy code, and interacts with the OpenAI API to get explanations for the bugs.


- `try.py`: This script defines dictionaries to store statistics about bug evaluations. It loads bug data from CSV files, processes each file to update the statistics, and prints a summary table using the tabulate library.


The labelling directories contains CSV files with metrics for evaluating the LLM responses. These metrics include Conceptual Accuracy, Inaccuracy, Relevance, Completeness, and Solution Provided. try.py processes these files to generate summary statistics.


Important note:
You need to create an API key for OpenAI and save it as a file called OPENAI_TOKEN in the root project directory. gitignore will ignore this file.
DOI: 10.5281/zenodo.10937409
title: Dataset for "LLM-aided explanations of EDA synthesis errors"

description: Dataset for "LLM-aided explanations of EDA synthesis errors"


Authors: Siyu Qiu, Benjamin Tan, Hammond Pearce

 

This Zenodo contains the open-source data used for the ISLAD submission "LLM-aided explanations of EDA synthesis errors" which aimed to use OpenAI LLMs for generating novice-focused explanations of common synthesis errors.

Error explanations for RTL and HDL Code with OpenAI's LLMs

Welcome to our error explanation tool for RTL and HDL code in Verilog and VHDL! This repository helps you generate error explanations for your code using OpenAI's models, making it easier to find and fix bugs.

Directory Structure




- `new_structure/`: Contains all bugs in separate files, along with labelling CSV files that match the LLM responses for each bug.

- `bug_id/`: Each bug has responses from gpt-3.5-turbo (40 responses), gpt-4 (4 responses), and gpt-4-turbo-preview (4 responses).


- `rtl/`: Includes RTL code used for bugs, compatible with both Quartus and Vivado.


- `Quartus/`: Contains Quartus project files (.qpf) and constraints files (.qsf).


- `Vivado/`: Holds Vivado project files (.xpr) and constraints files (.xdc).


- `llm_responses/`: Contains CSV files with records of the generated LLM responses for each bug.


- `labelling/`: Contains CSV files with manually scored metrics for evaluating the LLM responses.


- `error_list.csv`: This file lists all bugs with details like bug id, type, IDE, file name, language, and error message. main.py uses this file to process bugs.


- `main.py`: Use this script to generate error explanations for your RTL and HDL code with OpenAI's models. It reads bug information from error_list.csv, loads the buggy code, and interacts with the OpenAI API to get explanations for the bugs.


- `try.py`: This script defines dictionaries to store statistics about bug evaluations. It loads bug data from CSV files, processes each file to update the statistics, and prints a summary table using the tabulate library.


The labelling directories contains CSV files with metrics for evaluating the LLM responses. These metrics include Conceptual Accuracy, Inaccuracy, Relevance, Completeness, and Solution Provided. try.py processes these files to generate summary statistics.


Important note:
You need to create an API key for OpenAI and save it as a file called OPENAI_TOKEN in the root project directory. gitignore will ignore this file.
DOI: 10.15468/dl.zswxpe
title: Occurrence Download

description: A dataset containing 1448 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bulinus forskalii (Ehrenberg, 1831)"
 ]
}

 The dataset includes 1448 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037412-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.25897534
title: Untitled Item

description: BAM files and associated inputs used to generate data shown in Figure 7 of NAGATA manuscript. Outputs from NAGATA are also included
DOI: 10.6084/m9.figshare.25655245
title: Modeling and Learning on High-Dimensional Matrix-Variate Sequences

description: We propose a new matrix factor model, named RaDFaM, which is strictly derived from the general rank decomposition and assumes a high-dimensional vector factor model structure for each basis vector. RaDFaM contributes a novel class of low-rank latent structures that trade off between signal intensity and dimension reduction from a tensor subspace perspective. Based on the intrinsic separable covariance structure of RaDFaM, for a collection of matrix-valued observations, we derive a new class of PCA variants for estimating loading matrices, and sequentially the latent factor matrices. The peak signal-to-noise ratio of RaDFaM is proved to be superior in the category of PCA-type estimators. We also establish an asymptotic theory including the consistency, convergence rates, and asymptotic distributions for components in the signal part. Numerically, we demonstrate the performance of RaDFaM in applications such as matrix reconstruction, supervised learning, and clustering, on uncorrelated and correlated data, respectively. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.25605194
title: Dissecting Gene Expression Heterogeneity: Generalized Pearson Correlation Squares and the <i>K</i>-Lines Clustering Algorithm

description: Motivated by the pressing needs for dissecting heterogeneous relationships in gene expression data, here we generalize the squared Pearson correlation to capture a mixture of linear dependences between two real-valued variables, with or without an index variable that specifies the line memberships. We construct the generalized Pearson correlation squares by focusing on three aspects: variable exchangeability, no parametric model assumptions, and inference of population-level parameters. To compute the generalized Pearson correlation square from a sample without a line-membership specification, we develop a <i>K</i>-lines clustering algorithm to find <i>K</i> clusters that exhibit distinct linear dependences, where <i>K</i> can be chosen in a data-adaptive way. To infer the population-level generalized Pearson correlation squares, we derive the asymptotic distributions of the sample-level statistics to enable efficient statistical inference. Simulation studies verify the theoretical results and show the power advantage of the generalized Pearson correlation squares in capturing mixtures of linear dependences. Gene expression data analyses demonstrate the effectiveness of the generalized Pearson correlation squares and the <i>K</i>-lines clustering algorithm in dissecting complex but interpretable relationships. The estimation and inference procedures are implemented in the R package gR2 (https://github.com/lijy03/gR2). Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.18150/0vyvwz
title: dissolution of NM - fitting and extrapolating for PFO and KE eqautions

description: Data used for fitting and extrapolating dissolution of NM used in paper which can be found: https://doi.org/10.1016/j.csbj.2024.04.046 Abstract:Numerous processes such as solubility, agglomeration/aggregation, or protein corona formation may change over time and significantly affect engineered nanomaterial (ENM) structure, property, and availability, resulting in their reduced or increased toxicological activity. Therefore, understanding the dynamics of these processes is essential for assessing and managing the risks of ENMs during their lifecycle, ensuring safety by design. Of these processes, the importance of solubility (i.e., the ability to release ions from the surface) is undeniable. Thus, we propose a practical approach, the Kalapus equation (KEq), to determine ENMs' dissolution over time. As a proof-of-concept, the KEq was applied to determine the solubility of six commercially used metal and metal oxide nanoparticles over time. The KEq exhibited a higher coefficient of determination (R2 = 0.995–0.999) than the logarithmic equation (R2 = 0.835–0.986), and the pseudo-first-order equation (R2 = 0.915–0.994) over a range of experimental data. The newly introduced Kalapus equation outperformed the logarithmic and pseudo-first-order equations when extrapolating beyond the time range in which solubility was experimentally determined. The mean absolute error in solubility prediction for the KEq was 3.29 % and 4.28 % for the first and second data points, respectively, significantly lower than the 13.46 % and 18.05 % observed for the pseudo-first-order/first-order equation. The proposed equation can be used as a part of New Generation Risk Assessment (NGRA) methodology, especially new Integrated Approaches to Testing and Assessments (IATAs).
DOI: 10.15468/dl.42dz4z
title: Occurrence Download

description: A dataset containing 159 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "Country is one of (United States of America, United States Minor Outlying Islands, American Samoa, Micronesia (Federated States of), Guam, Marshall Islands, Northern Mariana Islands, Puerto Rico, Palau, Virgin Islands (U.S.))",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Stylurus potulentus (Needham, 1942), Ophiogomphus australis Carle, 1992)"
 ]
}

 The dataset includes 159 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037413-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9cw8qe
title: Occurrence Download

description: A dataset containing 108 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Taiwania flousiana Gaussen"
 ]
}

 The dataset includes 108 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037414-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jpx2sc
title: Occurrence Download

description: A dataset containing 98558 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Apera spica-venti (L.) P.Beauv.)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 98558 records from 629 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037394-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2656b9
title: Occurrence Download

description: A dataset containing 28 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is one of (China, Nauru, Micronesia (Federated States of), Palau, Fiji)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Euphorbia heterophylla var. cyathophora (Murray) Griseb., Euphorbia cyathophora Murray)"
 ]
}

 The dataset includes 28 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037415-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7hd9mc
title: Occurrence Download

description: A dataset containing 5837 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Apera interrupta (L.) P.Beauv.)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 5837 records from 265 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037416-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11104076
title: FAIROmeta

description: FAIRO meta data, to describe output from the FAIROpro processing tools.
DOI: 10.6084/m9.figshare.25897681
title: BAM files and outputs from NAGATA, Figure 8, NAGATA

description: BAM files and outputs from NAGATA, Figure 8, NAGATA
DOI: 10.15468/dl.2uhfxz
title: Occurrence Download

description: A dataset containing 5561 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Tridens flavus (L.) Hitchc.)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 5561 records from 118 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037419-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.25897654
title: All required data for running CHANCE

description: All required data for running CHANCE
DOI: 10.6084/m9.figshare.25897654.v1
title: All required data for running CHANCE

description: All required data for running CHANCE
DOI: 10.15468/dl.x4qwse
title: Occurrence Download

description: A dataset containing 2017 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Tridens albescens (Vasey) Wooton & Standl.)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2017 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037420-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.