cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3510434
title: Supplement 2. Community assembly simulation code for use in the R programming language.

description: File List community_assembly_simulations.txt Description A set of functions in the R programming language for simulating community assembly randomly (with or without weighting species by abundance), by trait-based competition, or by trait-based habitat filtering. The linked text file contains a series of R functions, a brief demonstration of the functions, commentary on the algorithms, and references. There are three key functions, described below, and a series of smaller functions that support them. More detailed descriptions can be found as in the file itself. random_assembly(pool, final_richness, abund=NULL)<br> Randomly samples final_richness number of species from a vector of species names given in pool. Sampling is occurence weighted if a vector of abundances is specified in the abund argument- default is no abundance weighting. compete_until(nfinal, community)<br> Takes a data frame community with species names in column 1 and traits in subsequent columns and runs a competition algorithm to cull the community unitl nfinal taxa remain. At each step, the algorithm identifies the most similar pair of species based on trait similarity and randomly removes one. Ties are broken randomly. filter_until(nfinal, community, optima) Takes a data frame community with species names in column 1 and traits in subsequent columns and runs a habitat filtering algorithm to cull the community unitl nfinal taxa remain. At each step, the algorithm identifies the species that is farthest from the trait optima and removes it. Ties are broken randomly. The community can have from 1 to many traits, but the optima vector must have the same number of elements as the number of traits in the community dataframe.
DOI: 10.6084/m9.figshare.3510434.v1
title: Supplement 2. Community assembly simulation code for use in the R programming language.

description: File List community_assembly_simulations.txt Description A set of functions in the R programming language for simulating community assembly randomly (with or without weighting species by abundance), by trait-based competition, or by trait-based habitat filtering. The linked text file contains a series of R functions, a brief demonstration of the functions, commentary on the algorithms, and references. There are three key functions, described below, and a series of smaller functions that support them. More detailed descriptions can be found as in the file itself. random_assembly(pool, final_richness, abund=NULL)<br> Randomly samples final_richness number of species from a vector of species names given in pool. Sampling is occurence weighted if a vector of abundances is specified in the abund argument- default is no abundance weighting. compete_until(nfinal, community)<br> Takes a data frame community with species names in column 1 and traits in subsequent columns and runs a competition algorithm to cull the community unitl nfinal taxa remain. At each step, the algorithm identifies the most similar pair of species based on trait similarity and randomly removes one. Ties are broken randomly. filter_until(nfinal, community, optima) Takes a data frame community with species names in column 1 and traits in subsequent columns and runs a habitat filtering algorithm to cull the community unitl nfinal taxa remain. At each step, the algorithm identifies the species that is farthest from the trait optima and removes it. Ties are broken randomly. The community can have from 1 to many traits, but the optima vector must have the same number of elements as the number of traits in the community dataframe.
DOI: 10.6084/m9.figshare.3510423
title: Supplement 2. Community assembly simulation code for use in the R programming language.

description: File List community_assembly_simulations.txt Description A set of functions in the R programming language for simulating community assembly randomly (with or without weighting species by abundance), by trait-based competition, or by trait-based habitat filtering. The linked text file contains a series of R functions, a brief demonstration of the functions, commentary on the algorithms, and references. There are three key functions, described below, and a series of smaller functions that support them. More detailed descriptions can be found as in the file itself. random_assembly(pool, final_richness, abund=NULL)<br> Randomly samples final_richness number of species from a vector of species names given in pool. Sampling is occurence weighted if a vector of abundances is specified in the abund argument- default is no abundance weighting. compete_until(nfinal, community)<br> Takes a data frame community with species names in column 1 and traits in subsequent columns and runs a competition algorithm to cull the community unitl nfinal taxa remain. At each step, the algorithm identifies the most similar pair of species based on trait similarity and randomly removes one. Ties are broken randomly. filter_until(nfinal, community, optima) Takes a data frame community with species names in column 1 and traits in subsequent columns and runs a habitat filtering algorithm to cull the community unitl nfinal taxa remain. At each step, the algorithm identifies the species that is farthest from the trait optima and removes it. Ties are broken randomly. The community can have from 1 to many traits, but the optima vector must have the same number of elements as the number of traits in the community dataframe.
DOI: 10.6084/m9.figshare.3510423.v1
title: Supplement 2. Community assembly simulation code for use in the R programming language.

description: File List community_assembly_simulations.txt Description A set of functions in the R programming language for simulating community assembly randomly (with or without weighting species by abundance), by trait-based competition, or by trait-based habitat filtering. The linked text file contains a series of R functions, a brief demonstration of the functions, commentary on the algorithms, and references. There are three key functions, described below, and a series of smaller functions that support them. More detailed descriptions can be found as in the file itself. random_assembly(pool, final_richness, abund=NULL)<br> Randomly samples final_richness number of species from a vector of species names given in pool. Sampling is occurence weighted if a vector of abundances is specified in the abund argument- default is no abundance weighting. compete_until(nfinal, community)<br> Takes a data frame community with species names in column 1 and traits in subsequent columns and runs a competition algorithm to cull the community unitl nfinal taxa remain. At each step, the algorithm identifies the most similar pair of species based on trait similarity and randomly removes one. Ties are broken randomly. filter_until(nfinal, community, optima) Takes a data frame community with species names in column 1 and traits in subsequent columns and runs a habitat filtering algorithm to cull the community unitl nfinal taxa remain. At each step, the algorithm identifies the species that is farthest from the trait optima and removes it. Ties are broken randomly. The community can have from 1 to many traits, but the optima vector must have the same number of elements as the number of traits in the community dataframe.
DOI: 10.6084/m9.figshare.3510422
title: Appendix A. Shannon's diversity index (SHDI).

description: Shannon's diversity index (SHDI).
DOI: 10.6084/m9.figshare.3510421
title: Appendix B. Generation of the total values of ecosystems.

description: Generation of the total values of ecosystems.
DOI: 10.6084/m9.figshare.3510422.v1
title: Appendix A. Shannon's diversity index (SHDI).

description: Shannon's diversity index (SHDI).
DOI: 10.6084/m9.figshare.3510421.v1
title: Appendix B. Generation of the total values of ecosystems.

description: Generation of the total values of ecosystems.
DOI: 10.6084/m9.figshare.3510417
title: Appendix A. A table showing equations for estimating aboveground biomass in subtropical forests, a figure showing Trace plot and density plot for each parameter in model I.1, and a figure showing within-chain variation for each parameter in model I.1.

description: A table showing equations for estimating aboveground biomass in subtropical forests, a figure showing Trace plot and density plot for each parameter in model I.1, and a figure showing within-chain variation for each parameter in model I.1.
DOI: 10.6084/m9.figshare.3510417.v1
title: Appendix A. A table showing equations for estimating aboveground biomass in subtropical forests, a figure showing Trace plot and density plot for each parameter in model I.1, and a figure showing within-chain variation for each parameter in model I.1.

description: A table showing equations for estimating aboveground biomass in subtropical forests, a figure showing Trace plot and density plot for each parameter in model I.1, and a figure showing within-chain variation for each parameter in model I.1.
DOI: 10.6084/m9.figshare.3510416
title: Supplement 1. Data files used to develop allometry models in the main text.

description: File List data.csv (MD5: 7c7fdea693fcf4010735192c2588db9b)Description The data.csv file is a csv file, it contains the raw data of diameter at breast height, tree height, wood specific gravity, crown radius, above-ground biomass and below-ground biomass. Column definitions 1 DBH: diameter at breast height 2 H: tree height 3 WSG:wood specific gravity 4 CR:crown radius 5 AGB: above-ground biomass 6 BGB: below-ground biomass
DOI: 10.6084/m9.figshare.3510416.v1
title: Supplement 1. Data files used to develop allometry models in the main text.

description: File List data.csv (MD5: 7c7fdea693fcf4010735192c2588db9b)Description The data.csv file is a csv file, it contains the raw data of diameter at breast height, tree height, wood specific gravity, crown radius, above-ground biomass and below-ground biomass. Column definitions 1 DBH: diameter at breast height 2 H: tree height 3 WSG:wood specific gravity 4 CR:crown radius 5 AGB: above-ground biomass 6 BGB: below-ground biomass
DOI: 10.6084/m9.figshare.3510412
title: Full Archive

description: Full Archive
DOI: 10.6084/m9.figshare.3510400
title: Supplement 1. Programs, user manual, and sample input and output files.

description: File List AllFoldersFiles_Example.txt AllPictures_Example.txt Harris_Thompson_Childs_Sanderson_CamTrapProgram_DataAnalyze.f90 Harris_Thompson_Childs_Sanderson_CamTrapProgram_DataOrganize.f90 Harris_Thompson_Childs_Sanderson_CamTrap_UserManual.pdf Input_Example.txt Output_Example.txt Description AllFoldersFiles_Example.txt file is generated in the command prompt window. It is a structured text file listing all folder names, locations, and the imagery files within them. Harris_Thompson_Childs_Sanderson_CamTrapProgram_DataOrganize.f90 is the data creation program in Fortran. This program reads "AllFoldersFiles_Example.txt" and generates "Input.txt" and "AllPictures.txt" AllPictures_Example.txt is a list of all image files that are labeled with the location name, species name, year, month, day, hour, minute, second, and number-of-individuals. This can be imported and stored by a spreadsheet such as Excel. Input_Example.txt file is example input for the analysis program. Harris_Thompson_Childs_Sanderson_CamTrapProgram_DataAnalyze.f90 is a Fortran program used to analyze the camera trap images (Reads "Input.txt" and "AllPictures_Example.txt"). Harris_Thompson_Childs_Sanderson_CamTrap_UserManual.pdf is a manual containing the detailed steps necessary for generating a common format for labeling, storing, sharing, and subsequently analyzing information gained from camera traps. Output_Example.txt file is example output from the analysis program.
DOI: 10.6084/m9.figshare.3510396
title: Appendix A. Map showing location of Palmyra Atoll, USA.

description: Map showing location of Palmyra Atoll, USA.
DOI: 10.6084/m9.figshare.3510396.v1
title: Appendix A. Map showing location of Palmyra Atoll, USA.

description: Map showing location of Palmyra Atoll, USA.
DOI: 10.6084/m9.figshare.3510395
title: Appendix B. Methodological details of mark and recapture model.

description: Methodological details of mark and recapture model.
DOI: 10.6084/m9.figshare.3510395.v1
title: Appendix B. Methodological details of mark and recapture model.

description: Methodological details of mark and recapture model.
DOI: 10.6084/m9.figshare.3510394
title: Appendix C. Comparisons of shark density outputs standardized by area and by volume.

description: Comparisons of shark density outputs standardized by area and by volume.
DOI: 10.6084/m9.figshare.3510393
title: Appendix D. Methodological details of computer simulation and description of simulation parameters.

description: Methodological details of computer simulation and description of simulation parameters.
DOI: 10.6084/m9.figshare.3510394.v1
title: Appendix C. Comparisons of shark density outputs standardized by area and by volume.

description: Comparisons of shark density outputs standardized by area and by volume.
DOI: 10.6084/m9.figshare.3510393.v1
title: Appendix D. Methodological details of computer simulation and description of simulation parameters.

description: Methodological details of computer simulation and description of simulation parameters.
DOI: 10.6084/m9.figshare.3510392
title: Appendix E. Detection rates of sharks in field surveys.

description: Detection rates of sharks in field surveys.
DOI: 10.6084/m9.figshare.3510391
title: Appendix F. Statistical comparisons of shark presence/absence data from field surveys.

description: Statistical comparisons of shark presence/absence data from field surveys.
DOI: 10.6084/m9.figshare.3510392.v1
title: Appendix E. Detection rates of sharks in field surveys.

description: Detection rates of sharks in field surveys.
DOI: 10.6084/m9.figshare.3510391.v1
title: Appendix F. Statistical comparisons of shark presence/absence data from field surveys.

description: Statistical comparisons of shark presence/absence data from field surveys.
DOI: 10.6084/m9.figshare.3510390
title: Appendix G. Evaluation of variability of density estimates as a function of survey replication.

description: Evaluation of variability of density estimates as a function of survey replication.
DOI: 10.6084/m9.figshare.3510390.v1
title: Appendix G. Evaluation of variability of density estimates as a function of survey replication.

description: Evaluation of variability of density estimates as a function of survey replication.
DOI: 10.6084/m9.figshare.3510389
title: Appendix H. Algorithms that model the operation of simulation-implemented survey methods.

description: Algorithms that model the operation of simulation-implemented survey methods.
DOI: 10.6084/m9.figshare.3510389.v1
title: Appendix H. Algorithms that model the operation of simulation-implemented survey methods.

description: Algorithms that model the operation of simulation-implemented survey methods.
DOI: 10.6084/m9.figshare.3510388
title: Appendix I. Role of behavioral biases in shark surveys.

description: Role of behavioral biases in shark surveys.
DOI: 10.6084/m9.figshare.3510388.v1
title: Appendix I. Role of behavioral biases in shark surveys.

description: Role of behavioral biases in shark surveys.
DOI: 10.6084/m9.figshare.3510387
title: Appendix J. Video clip of mark and recapture surveys of shark Carcharhinus amblyrhynchos.

description: Video clip of mark and recapture surveys of shark Carcharhinus amblyrhynchos.
DOI: 10.6084/m9.figshare.3510386
title: Supplement 1. Source code for simulation model, simulation readme, and associated parameter files.

description: File List fish.jar (md5: d366caa56781738816a87f7b31bf5226)<br> fish.src.jar (md5: 387da049a0c835710df147f6d03cfe99)<br> fish_stats.txt (md5: e437e19552b37e20871097ff259cbe8a)<br> sim_parameters.txt (md5: 093ccbfb4f6c1642172d6cdd067f35ab)<br> readme.pdf (md5: 57659754dff53bdd4fa07ebbc88e3973)<br> Description fish.jar contains the compiled program to run simulation model used in paper.<br> fish.src.jar contains the Java source for simulation model used in paper.<br> fish_stats.txt contains parameter values for simulation fish/fishes.<br> sim_parameters.txt contains parameter values for the full simulation.<br> readme.pdf contains instruction for operation of the simulation.
DOI: 10.6084/m9.figshare.3510385
title: Raw-Data.xlsx

description: Raw data for the figures in paper titled " A functional polymorphism (rs10817938) in the XPA promoter region is associated with poor prognosis of oral squamous cell Carcinoma in a Chinese Han population " in Plos One
DOI: 10.6084/m9.figshare.3510383
title: Foraging behaviour in East Asian desert rodents and its implications on coexistence

description: We studied the foraging behaviour of two sympatric rodents (<i>Meriones meridianus</i> and <i>Dipus sagitta</i>) in the Gobi Desert, Northwestern China. The role of the foraging behaviour in promoting species coexistence was also examined. We used giving-up densities (GUDs) in artificial food patches to measure the patch use of rodents and video trapping to directly record the foraging behaviour, vigilance, and interspecific interactions. Three potential mechanisms of coexistence were evaluated (1) microhabitat partitioning; (2) spatial heterogeneity of resource abundance with a tradeoff in foraging efficiency vs. locomotion; and (3) temporal partitioning on a daily scale. Compared to <i>M. meridianus</i>, <i>D. sagitta</i> generally possessed lower GUDs, spent more time on patches, and conducted more visits per tray per capita, regardless of microhabitat. However, <i>M. meridianus</i> possessed advantages in average harvesting rates and direct interference against <i>D. sagitta</i>. Our results only partly support the third mechanism listed above. We propose another potential mechanism of coexistence: a tradeoff between interference competition and safety, with <i>M. meridianus</i> better at interference competition and <i>D. sagitta</i> better at avoiding predation risk. This mechanism is uncommon in previously studied desert rodent systems.
DOI: 10.6084/m9.figshare.3510385.v1
title: Raw-Data.xlsx

description: Raw data for the figures in paper titled " A functional polymorphism (rs10817938) in the XPA promoter region is associated with poor prognosis of oral squamous cell Carcinoma in a Chinese Han population " in Plos One
DOI: 10.6084/m9.figshare.3510383.v1
title: Foraging behaviour in East Asian desert rodents and its implications on coexistence

description: We studied the foraging behaviour of two sympatric rodents (<i>Meriones meridianus</i> and <i>Dipus sagitta</i>) in the Gobi Desert, Northwestern China. The role of the foraging behaviour in promoting species coexistence was also examined. We used giving-up densities (GUDs) in artificial food patches to measure the patch use of rodents and video trapping to directly record the foraging behaviour, vigilance, and interspecific interactions. Three potential mechanisms of coexistence were evaluated (1) microhabitat partitioning; (2) spatial heterogeneity of resource abundance with a tradeoff in foraging efficiency vs. locomotion; and (3) temporal partitioning on a daily scale. Compared to <i>M. meridianus</i>, <i>D. sagitta</i> generally possessed lower GUDs, spent more time on patches, and conducted more visits per tray per capita, regardless of microhabitat. However, <i>M. meridianus</i> possessed advantages in average harvesting rates and direct interference against <i>D. sagitta</i>. Our results only partly support the third mechanism listed above. We propose another potential mechanism of coexistence: a tradeoff between interference competition and safety, with <i>M. meridianus</i> better at interference competition and <i>D. sagitta</i> better at avoiding predation risk. This mechanism is uncommon in previously studied desert rodent systems.
DOI: 10.6084/m9.figshare.3510370
title: A planet population from isotopic enrichment?

description: Poster at "Exoplanets I" conference in Davos, CH, July 2016.
DOI: 10.6084/m9.figshare.3510370.v1
title: A planet population from isotopic enrichment?

description: Poster at "Exoplanets I" conference in Davos, CH, July 2016.
DOI: 10.6084/m9.figshare.3510363
title: Code and Data of Dynamic Energy Budget Integral Projection Model

description: Uploaded as online supporting information to the paper "<strong>Mechanistic description of population dynamics using dynamic energy budget theory incorporated into integral projection models</strong>" by I.M. Smallegange, H. Caswell, M.E.M. Toorians and André M. de Roos are: 1. MatLab scripts2. R-scripts and an data files to estimate bulb mite DEB parameters.
DOI: 10.6084/m9.figshare.3510363.v1
title: Code and Data of Dynamic Energy Budget Integral Projection Model

description: Uploaded as online supporting information to the paper "<strong>Mechanistic description of population dynamics using dynamic energy budget theory incorporated into integral projection models</strong>" by I.M. Smallegange, H. Caswell, M.E.M. Toorians and André M. de Roos are: 1. MatLab scripts2. R-scripts and an data files to estimate bulb mite DEB parameters.
DOI: 10.5281/zenodo.3678960
title: The Dataset of ICSE'20 paper titled "On the Efficiency of Test Suite based Program Repair"

description: The artifact contains the results and some logs after executing 16 open-source automated program repair systems (i.e., jGenProg, jKali, jMutRepair, Cardumen, AJRA, GenProg-A, Kali-A, RSRepair-A, Nopol, DynaMoth, SimFix, kPAR, AVATAR, FixMiner, TBar, and ACS). <strong>Results</strong> (i.e., Patches) consist of the patches generated by executing 16 open-source automated program repair systems on Defects4J bugs with two different fault localization settings.<strong> Logs </strong>(i.e., Execution logs) record the execution logs of 10 open-source automated program repair systems as the remaining 6 automated program repair systems do not provide execution logs.
DOI: 10.5281/zenodo.3678959
title: The Dataset of ICSE'20 paper titled "On the Efficiency of Test Suite based Program Repair"

description: The artifact contains the results and some logs after executing 16 open-source automated program repair systems (i.e., jGenProg, jKali, jMutRepair, Cardumen, AJRA, GenProg-A, Kali-A, RSRepair-A, Nopol, DynaMoth, SimFix, kPAR, AVATAR, FixMiner, TBar, and ACS). <strong>Results</strong> (i.e., Patches) consist of the patches generated by executing 16 open-source automated program repair systems on Defects4J bugs with two different fault localization settings.<strong> Logs </strong>(i.e., Execution logs) record the execution logs of 10 open-source automated program repair systems as the remaining 6 automated program repair systems do not provide execution logs.