Format

index.csv.gz - CSV comma separated file with 3 columns: , , For example: src-d/go-git,s,README.md

The flag is either "s" (readme found) or "r" (readme does not exist on the root directory level). Readme file name may be any from the list:

"README.md", "readme.md", "Readme.md", "README.MD", "README.txt", "readme.txt", "Readme.txt", "README.TXT", "README", "readme", "Readme", "README.rst", "readme.rst", "Readme.rst", "README.RST"

100 part-r-00xxx files are in "new" Hadoop API format with the following settings:

inputFormatClass is org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat

keyClass is org.apache.hadoop.io.Text - repository name

valueClass is org.apache.hadoop.io.BytesWritable - gzipped readme file

Data and code in support of [Klarevas-Irby, Wikelski, and Farine (2021)], in addition to a readme file with further information. All scripts are written for implementation in R ver. 4.0 and are numbered in order that they are implemented (order does not matter between multiple scripts with same number).

After admixture, recombination breaks down genomic blocks of contiguous ancestry. The breakdown of these blocks forms a new molecular clock', that ticks at a much faster rate than the mutation clock, enabling accurate dating of admixture events in the recent past. However, existing theory on the break down of these blocks, or the accumulation of delineations between blocks, so calledjunctions', has mostly been limited to using regularly spaced markers on phased data. Here, we present an extension to the theory of junctions using the Ancestral Recombination Graph that describes the expected number of junctions for any distribution of markers along the genome. Furthermore, we provide a new framework to infer the time since admixture using unphased data. We demonstrate both the phased and unphased methods on simulated data and show that our new extensions have improved accuracy with respect to previous methods, especially for smaller population sizes and more ancient admixture tim...

Dataset1.zip

Samples were stored unfiltered in the dark at 4° C for approximately five months after sampling.
On the day of measurements, specific volumes of samples were transferred to 2 mL Eppendorf vials so that 11.25 µg carbon was present in each sample vial, while 2 mL of blanks were transferred.
The water in samples and blanks was subsequently removed by vacuum evaporation at 45° C, after which samples were reconstituted in 150 µL 1 % (v/v) formic acid to a final concentration of 75 mg/L carbon.

Reverse-phase chromatography separations were performed on an Agilent 1100 series instrument with an Agilent PLRP‑S series column (150 x 1 mm, 3 µm bed size, 100 Å pore size). Eighty µL sample was loaded at a flow rate of 100 µL min-1 0.1 % formic acid, 0.05 % ammonia, and 5 % acetonitrile. The elution of DOM was achieved through a step-wise increase in concentrat...

The attached files contain the necessary descriptions for understanding and working with data used in the construction of Tapper et al. 2020 (Royal Society Open Science). The RSOS_dryad_readme.docx file contains descriptions of each file name attached to this data repository, and descriptions of the data contained within each column of a data file. Information pertaining to the R code, which has been provided in this data repository as a .Rmd (R Markdown) file, has not been provided within this readme file, because necessary descriptions for understanding our code are provided within the R code file itself.

The attached files contain the necessary descriptions for understanding and working with data used in the construction of Tapper et al. 2020, "Body temperature is a repeatable trait in a free-ranging passerine bird" (submitted to Journal of Experimental Biology). The Repeatability_dryad_readme.docx file contains descriptions of each file name attached to this data repository, and descriptions of the data contained within each column of a data file. Information pertaining to the R code, which has been provided in this data repository as a .Rmd (R Markdown) file, has not been provided within this readme file, because necessary descriptions for understanding our code are provided within the R code file itself.

hklmirs This repository contains the data and code for our two manuscripts (in preparation): Henning Teickner and Klaus-Holger Knorr (in preparation): Improving Models to Predict Holocellulose and Klason Lignin Contents for Peat Soil Organic Matter with Mid Infrared Spectra. Henning Teickner and Klaus-Holger Knorr (in preparation): Predicting Absolute Holocellulose and Klason Lignin Contents for Peat Remains Challenging. How to cite Please cite this compendium as: Henning Teickner and Klaus-Holger Knorr, (2022). Compendium of R code and data for “Improving Models to Predict Holocellulose and Klason Lignin Contents for Peat Soil Organic Matter with Mid Infrared Spectra” and “Predicting Absolute Holocellulose and Klason Lignin Contents for Peat Remains Challenging”. Accessed 03 Mar 2022. Online at https://github.com/henningte/hklmirs/ Contents The analysis directory contains: :file_folder: paper: R Markdown source documents needed to reproduce the manuscript, including figures and tables. The main script is 001-paper-main.Rmd. This script produces both manuscripts and the corresponding supplementary information. Additional scripts are: 002-paper-m-original-models.Rmd: Computes the original models used in Hodgkins et al. (2018) and models with the same model structure, but as Bayesian models. 003-paper-m-gaussian-beta.Rmd: Computes models assuming a Beta distribution for holocellulose and Klason lignin contents and compares them to the original models. 004-paper-m-reduce-underfitting.Rmd: Extents the Beta regression models by including additional variables (additional peaks) or using a different approach (using measured spectral intensities of binned spectra instead of extracted peaks), and validates these models using LOO-CV. 005-paper-m-minerals.Rmd: Uses the models from 003-paper-m-gaussian-beta.Rmd to test how accurate a model for holocellulose content is which is also calibrated on training samples with higher mineral contents. 006-paper-m-prediction-domain.Rmd: Analyzes the prediction domain (Wadoux et al. 2021) of the original models and the modified models and identifie under which conditions models extrapolate for peat and vegetation smaples from Hodgkins et al. (2018). 007-paper-m-prediction-differences.Rmd: Compares predictions for the training data and the peat and vegetation data from Hodgkins et al. (2018) for the original models from Hodgkins et al. (2018) and the modified models from 004-paper-m-reduce-underfitting.Rmd. 008-paper-supplementary.Rmd: Computes supplementary analyses and figures for the first manuscript. 001-reply-main.Rmd: This is the main script for manuscript 2. It is run from within 001-paper-main.Rmd and produces the supplementary information for manuscript 2. 002-reply-main.Rmd: This script produces the document for manuscript 2. It is run from within 001-reply-main.Rmd. :file_folder: data: Data used in the analysis. Note that raw data is not stored in :file_folder: raw_data (empty folder), but in :file_folder: /inst/extdata. :file_folder: derived_data contains derived data computed from the scripts. The raw data are derived from Hodgkins et al. (2018). :file_folder: stan_models: The Stan model used in 001-reply-main.Rmd. The other folders in this directory follow the standard naming scheme and function of folders in R packages. There are the following directories and files: README.md/README.Rmd: Readme for the compendium. DESCRIPTION: The R package DESCRIPTION file for the compendium. NAMESPACE: The R package NAMESPACE file for the compendium. LICENSE.md: Details on the license for the code in the compendium. CONTRIBUTING.md and CONDUCT.md: Files with information on how to contribute to the compendium. Dockerfile: Dockerfile to build a Docker image for the compendium. .Rbuildignore, .gitignore, .dockerignore: Files to ignore during R package building, to ignore by Git, and to ignore while building a Docker image, respectively. renv.lock: renv lock file (Lists all R package dependencies and versions and can be used to restore the R package library using renv). renv.lock was created by running renv::snapshot() in the R package directory and it uses the information included in the DESCRIPTION file. .Rprofile: Code to run upon opening the R-project. R, man, inst, data-raw, data, src: Default folders for making the R package run. Folder inst/extdata: Folder with the raw data used for the analyses. All files in this folder are derived from Hodgkins et al. (2018). How to run in your broswer or download and run locally You can download the compendium as a zip from from this URL: https://github.com/henningte/hklmirs/ Or you can install this compendium as an R package, hklmirs, from GitHub with: remotes::install_github("henningte/hklmirs") How to use Reproduce the analyses To reproduce the analyses for the paper, open the Rstudio project included in this research compendium and run the Rmarkdown script in analysis/paper/001-paper-main.rmd. Running the whole script takes about 12 ho...

The attached files contain the necessary descriptions for understanding and working with data used in the construction of Tapper et al. 2021 (Physiological and Biochemical Zoology). The PBZ_dryad_readme.docx file contains descriptions of each file name attached to this data repository and descriptions of the data contained within each column of a data file. Information pertaining to the R code, which has been provided in this data repository as a .R file, has not been provided within this readme file, because necessary descriptions for understanding our code are provided within the R code file itself.

The attached files contain the necessary descriptions for understanding and working with data used in the construction of Tapper et al. 2020 (Journal of Experimental Biology). The JEB_dryad_readme.docx file contains descriptions of each file name attached to this data repository, and descriptions of the data contained within each column of a data file. Information pertaining to the R code, which has been provided in this data repository as a .Rmd (R Markdown) file, has not been provided within this readme file, because necessary descriptions for understanding our code are provided within the R code file itself.

Summary---------------This is the repository containing the R code and data to produce the analyses and figures in the manuscript ‘Colour biases in learned foraging preferences in Trinidadian guppies’. R version 3.6.2 was used for this project. Here, we explain how to reproduce the results, provides the location of the metadata for the data sheets, and gives descriptions of the root directory contents and folder contents. This material is adapted from the README file of the project, README.md which is located in the root directory.How to reproduce the results-------------------------------------------This project uses the renv package from RStudio to manage package dependencies and ensure reproducibility through time. To ensure results are reproduced based on the versions of the packages used at the time this project was created, you will need to install renv using install.packages("renv") in R.If you want to reproduce the results it is best to download the entire repository onto your system. This can be done by clicking the Download button on the FigShare repository (DOI: 10.6084/m9.figshare.14404868). This will download a zip file of the entire repository. Unzip the zip file to get access to the project files.Once the repository is downloaded onto your system, navigate to the root directory and open guppy-colour-learning-project.Rproj. It is important to open the project using the .Rproj file to ensure the working directory is set correctly. Then install the package dependencies onto your system using renv::restore(). Running renv::restore() will install the correct versions of all the packages needed to reproduce our results. Packages are installed in a stand-alone library for this project and will not affect your installed R packages anywhere else.If you want to reproduce specific results from the analyses you can open either analysis-experiment-1.Rmd for results from experiment 1 or analysis-experiment-2.Rmd for results from experiment 2. Both are located in the root directory. You can select the Run All option under the Code option in the navbar of RStudio to execute all the code chunks. You can also run all chunks independently as well though we advise that you do so sequentially since variables necessary for the analysis are created as the script progresses.Metadata--------------Data are available in the data/ directory. - colour-learning-experiment-1-data.csv are the data for experiment 1- colour-learning-experiment-2-full-data.csv are the data for experiment 2We provide the variable descriptions for the data sets in the file metadata.md located in the data/ directory. The packages required to conduct the analyses and construct the website as well as their versions and citations are provided in the file required-r-packages.md.Directory structure---------------------------- - data/ contains the raw data used to conduct the analyses - docs/ contains the reader-friendly html write-up of the analyses, the GitHub pages site is built from this folder - R/ contains custom R functions used in the analysis - references/ contains reference information and formatting for citations used in the project - renv/ contains an activation script and configuration files for the renv package manager - figs/ contains the individual files for the figures and residual diagnostic plots produced by the analysis scripts. This directory is created and populated by running analysis-experiment-1.Rmd, analysis-experiment-2.Rmd and combined-figures.RmdRoot directory contents------------------------------------The root directory contains Rmd scripts used to conduct the analyses, create figures, and render the website pages. Below we describe the contents of these files as well as the additional files contained in the root directory. - analysis-experiment-1.Rmd is the R code and documentation for the experiment 1 data preparation and analysis. This script generates the Analysis 1 page of the website. - analysis-experiment-2.Rmd is the R code and documentation for the experiment 2 data preparation and analysis. This script generates the Analysis 2 page of the website. - protocols.Rmd contains the protocols used to conduct the experiments and generate the data. This script generates the Protocols page of the website. - index.Rmd creates the Homepage of the project site. - combined-figures.Rmd is the R code used to create figures that combine data from experiments 1 and 2. Not used in the project site. - treatment-object-side-assignment.Rmd is the R code used to assign treatments and object sides during trials for experiment 2. Not used in the project site. - renv.lock is a JSON formatted plain text file which contains package information for the project. renv will install the packages listed in this file upon executing renv::restore() - required-r-packages.md is a plain text file containing the versions and sources of the packages required for the project. - styles.css contains the CSS formatting for the rendered html pages - LICENSE.md contains the license indicating the conditions upon which the code can be reused - guppy-colour-learning-project.Rproj is the R project file which sets the working directory of the R instance to the root directory of this repository. If trying to run the code in this repository to reproduce results it is important to open R by clicking on this .Rproj file.

This is the github repository for analysing data using R and Rstudio.

All information and how to interpret it can be found in the README and in the workflow script

This dataset contains hydrometeor size spectra data for the Seeded and Natural Orographic Wintertime clouds – the Idaho Experiment (SNOWIE). There is one file for each UW King Air (UWKA) research flight. The files contain particle size spectra from all of the particle probes that were operational on the UWKA for that flight. No attempt has been made to combine size spectra. Note: these data are version 1 and there are known issues to be resolved; please refer to the included readme file for detailed information on variables, naming convention, and missing data.

README File: README_MASSARO_2022_DATA_updated04mar2022.txt R-code for data analysis: CodeforCorrelatesofBoundaryPatrols.R Datasets: PPdata.xlsx, PatrolsandPeriph.xlsx, PP5yearPlots.xlsx, WholeStudyPatrolRate.xlsx We do not provide access to the raw data used in some of these analyses, as this raw data represent a substantial fraction of the long-term data from Gombe, which are not publicly available at this time due to multiple ongoing studies, but are available from the corresponding author on reasonable request

Required data to prepare and execute analysis of the data included in this published study. This includes (1) mapping daily mean exposure of total ambient, WF, RX, and fRx PM2.5; (2) calculating annual person-days of exposure; (3) calculating and mapping the fraction of population exposure (in person-days) in the Historical Period (2008-2016) & Future Rx Scenario; (4) calculating the fraction of PM2.5-attributed cardiorespiratory emergency department (ED) visits by HYSPLIT-modeled PM2.5 smoke strata (μg/m3) in California; (5) calculating and plotting average daily PM2.5-attributed burden rates for cardiorespiratory emergency department (ED) visits by HYSPLIT-modeled PM2.5 smoke strata (μg/m3); (6) estimating annual prescribed fire smoke-attributed cardiorespiratory burden rates per 100,000 persons in the Historical Period (2008-2016), Future Prescribed Fire Scenario, and mapping the change in annual prescribed fire-related burden rates between the Historical Period and Future Scenario.Please note, raw health data is not provided due to confidentiality of personal health information used for research. Aggregated estimates of PM2.5-attributed ED visit counts and rates per 100,000 are provided by strata of smoke PM2.5.Data documentation ReadMe files for data and R scripts are provided. See:1.) HYSPLIT_Exposure.README2.) HYSPLIT_Future_Rx_Exp.README3.) PHIRE_Rx_Impacts_README_v2For original HYSPLIT Smoke Modeling datasets, see Kramer et al. (2023) published on Zenodo.Kramer, Samantha J., Huang, ShihMing, McClure, Crystal D., Chaveste, Melissa R., & Lurmann, Fred. (2023). Projected Smoke Impacts from Increased Prescribed Fire Activity (PHIRE) Smoke Modeling Datasets (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7535621Publication Citation:Rosenberg,A., Hoshiko,S., Buckman,J. R., Yeomans,K. R., Hayashi,T., Kramer,S. J., et al. (2024). Health impacts of future prescribed fire smoke: Considerations from an exposure scenario in California. Earth's Future, 12, e2023EF003778. https://doi.org/10.1029/2023EF003778

Ecological meta-analyses usually exhibit high relative heterogeneity of effect size: most among-study variation in effect size represents true variation in mean effect size, rather than sampling error. This heterogeneity arises from both methodological and ecological sources. Methodological heterogeneity is a nuisance that complicates the interpretation of data syntheses. One way to reduce methodological heterogeneity is via coordinated distributed experiments, in which investigators conduct the same experiment at different sites, using the same methods. We tested whether coordinated distributed experiments in ecology exhibit a) low heterogeneity in effect size, and b) lower heterogeneity than meta-analyses, using data on 17 effects from eight coordinated distributed experiments, and 406 meta-analyses. Consistent with our expectations, among-site heterogeneity typically comprised <50% of the variance in effect size in distributed experiments. In contrast, heterogeneity within and amo..., , , # Coordinated distributed experiments in ecology do not consistently reduce heterogeneity in effect size

Included here is a data file for a distributed experiment, and code which analyses the heterogeneity of many coordinated distributed experiments and meta-analyses. The R code file reproduces the results of this study, called meta-analyses vs distd expts - R code for sharing v 2.R.

## Description of the data and file structure

Data File:

rousk et al 2013 table 3 data - INCREASE.csv: data from the INCREASE distributed experiment by Rousk et al. (2013)

All other data used in code is automatically sourced from URLs, but relevant variables are still described below.

Other variables in datasets were not used in our analysis, and so are not explained in this README file. Cells with missing data have "NA" values.

Variables used in code:

Costello & Fox variables:Â

meta.analysis.id: Unique ID number for each meta-analysis

eff.size: Effect size

var. eff.size: Variance in e...

rxivist.org allows readers to sort and filter the tens of thousands of preprints posted to bioRxiv. Rxivist uses a custom web crawler to index all papers on biorxiv.org; this is a snapshot of Rxivist the production database. The version number indicates the date on which the snapshot was taken. See the included "README.md" file for instructions on how to use the "rxivist.backup" file to import data into a PostgreSQL database server.

Please note this is a different repository than the one used for the Rxivist manuscript—that is in a separate Zenodo repository. You're welcome (and encouraged!) to use this data in your research, but please cite our paper, now published in eLife.

Going forward, this information will also be available pre-loaded into Docker images, available at blekhmanlab/rxivist_data.

Version notes:

• 2019-07-01
  • A new "institution" field has been added to the "article_authors" table that stores each author's institutional affiliation as listed on that paper. The "authors" table still has each author's most recently observed institution.
    • We began collecting this data in the middle of May, but it has not been applied to older papers yet.
• 2019-05-11
  • The README was updated to correct a link to the Docker repository used for the pre-built images.
• 2019-03-21
  • The license for this dataset has been changed to CC-BY, which allows use for any purpose and requires only attribution.
  • A new table, "publication_dates," has been added and will be continually updated. This table will include an entry for each preprint that has been published externally for which we can determine a date of publication, based on data from Crossref. (This table was previously included in the "paper" schema but was not updated after early December 2018.)
  • Foreign key constraints have been added to almost every table in the database. This should not impact any read behavior, but anyone writing to these tables will encounter constraints on existing fields that refer to other tables. Most frequently, this means the "article" field in a table will need to refer to an ID that actually exists in the "articles" table.
  • The "author_translations" table has been removed. This was used to redirect incoming requests for outdated author profile pages and was likely not of any functional use to others.
  • The "README.md" file has been renamed "1README.md" because Zenodo only displays a preview for the file that appears first in the list alphabetically.
  • The "article_ranks" and "article_ranks_working" tables have been removed as well; they were unused.
• 2019-02-13.1
  • After consultation with bioRxiv, the "fulltext" table will not be included in further snapshots until (and if) concerns about licensing and copyright can be resolved.
  • The "docker-compose.yml" file was added, with corresponding instructions in the README to streamline deployment of a local copy of this database.
• 2019-02-13
  • The redundant "paper" schema has been removed.
  • BioRxiv has begun making the full text of preprints available online. Beginning with this version, a new table ("fulltext") is available that contains the text of preprints that have been processed already. The format in which this information is stored may change in the future; any digression will be noted here.
  • This is the first version that has a corresponding Docker image.

README

Authors

César E. Corona-González, Claudia Rebeca De Stefano-Ramos, Juan Pablo Rosado-Aíza, Fabiola R Gómez-Velázquez, David I. Ibarra-Zarate, Luz María Alonso-Valerdi

Contact person

César E. Corona-González

https://orcid.org/0000-0002-7680-2953

a00833959@tec.mx

Project name

Psychophysiological data from Mexican children with learning difficulties who strengthen reading and math skills by assistive technology

Year that the project ran

2023

Brief overview of the tasks in the experiment

The current dataset consists of psychometric and electrophysiological data from children with reading or math learning difficulties. These data were collected to evaluate improvements in reading or math skills resulting from using an online learning method called Smartick.

The psychometric evaluations from children with reading difficulties encompassed: spelling tests, where 1) orthographic and 2) phonological errors were considered, 3) reading speed, expressed in words read per minute, and 4) reading comprehension, where multiple-choice questions were given to the children. The last 2 parameters were determined according to the standards from the Ministry of Public Education (Secretaría de Educación Pública in Spanish) in Mexico. On the other hand, group 2 assessments embraced: 1) an assessment of general mathematical knowledge, as well as 2) the hits percentage, and 3) reaction time from an arithmetical task. Additionally, selective attention and intelligence quotient (IQ) were also evaluated.

Then, individuals underwent an EEG experimental paradigm where two conditions were recorded: 1) a 3-minute eyes-open resting state and 2) performing either reading or mathematical activities. EEG recordings from the reading experiment consisted of reading a text aloud and then answering questions about the text. Alternatively, EEG recordings from the math experiment involved the solution of two blocks with 20 arithmetic operations (addition and subtraction). Subsequently, each child was randomly subcategorized as 1) the experimental group, who were asked to engage with Smartick for three months, and 2) the control group, who were not involved with the intervention. Once the 3-month period was over, every child was reassessed as described before.

Description of the contents of the dataset

The dataset contains a total of 76 subjects (sub-), where two study groups were assessed: 1) reading difficulties (R) and 2) math difficulties (M). Then, each individual was subcategorized as experimental subgroup (e), where children were compromised to engage with Smartick, or control subgroup (c), where they did not get involved with any intervention.

Every subject was followed up on for three months. During this period, each subject underwent two EEG sessions, representing the PRE-intervention (ses-1) and the POST-intervention (ses-2).

The EEG recordings from the reading difficulties group consisted of a resting state condition (run-1) and while performing active reading and reading comprehension activities (run-2). On the other hand, EEG data from the math difficulties group was collected from a resting state condition (run-1) and when solving two blocks of 20 arithmetic operations (run-2 and run-3). All EEG files were stored in .set format. The nomenclature and description from filenames are shown below:

Example: the file sub-Rc11_ses-1_task-SmartickDataset_run-2_eeg.set is related to: - The 11th subject from the reading difficulties group, control subgroup (sub-Rc11). - EEG recording from the PRE-intervention (ses-1) while performing the reading activity (run-2)

Independent variables

• Study groups:
  • Reading difficulties
    • Control: children did not follow any intervention
    • Experimental: Children used the reading program of Smartick for 3 months
  • Math difficulties
    • Control: children did not follow any intervention
    • Experimental: Children used the math program of Smartick for 3 months
• Condition:
  • PRE-intervention: first psychological and electroencephalographic evaluation
  • POST-intervention: second psychological and electroencephalographic evaluation

Dependent variables

• Psychometric data from the reading difficulties group:

  • Orthographic_ERR: number of orthographic errors.
  • Phonological_ERR: number of phonological errors.
  • Selective_Attention: score from the selective attention test.
  • Reading_Speed: reading speed in words per minute.
  • Comprehension: score on a reading comprehension task.
  • GROUP: C for the control group, E for the experimental group.
  • GENDER: M for male, F for Female.
  • AGE: age at the beginning of the study.
  • IQ: intelligence quotient.

• Psychometric data from the math difficulties group:

  • WRAT4: score from the WRAT-4 test.
  • hits: hits during the EEG acquisition [%].
  • RT: reaction time during the EEG acquisition [s].
  • Selective_Attention: score from the selective attention test.
  • GROUP: C for the control Group, E for the experimental group.
  • GENDER: M for male, F for female.
  • AGE: age at the beginning of the study.
  • IQ: intelligence quotient.

Psychometric data can be found in the 01_Psychometric_Data.xlsx file

• Engagement percentage within Smartick (only for experimental group)
  • These values represent the engagement percentage through Smartick.
  • Students were asked to get involved with the online method for learning for 3 months, 5 days a week.
  • Greater values than 100% denote participants who regularly logged in more than 5 days weekly.

Engagement percentage be found in the 05_SessionEngagement.xlsx file

Methods

Subjects

Seventy-six Mexican children between 7 and 13 years old were enrolled in this study.

Information about the recruitment procedure

The sample was recruited through non-profit foundations that support learning and foster care programs.

Apparatus

g.USBamp RESEARCH amplifier

Initial setup

1. Explain the task to the participant.
2. Sign informed consent.
3. Set up electrodes.

Task details

The stimuli nested folder contains all stimuli employed in the EEG experiments.

Level 1 - Math: Images used in the math experiment.​​​​​​​ - Reading: Images used in the reading experiment.

Level 2 - Math * POST_Operations: arithmetic operations from the POST-intervention.
* PRE_Operations: arithmetic operations from the PRE-intervention. - Reading * POST_Reading1: text 1 and text-related comprehension questions from the POST-intervention. * POST_Reading2: text 2 and text-related comprehension questions from the POST-intervention. * POST_Reading3: text 3 and text-related comprehension questions from the POST-intervention. * PRE_Reading1: text 1 and text-related comprehension questions from the PRE-intervention. * PRE_Reading2: text 2 and text-related comprehension questions from the PRE-intervention. * PRE_Reading3: text 3 and text-related comprehension questions from the PRE-intervention.

Level 3 - Math * Operation01.jpg to Operation20.jpg: arithmetical operations solved during the first block of the math

Here, we provide the data and R-scripts used in:

Bård-Jørgen Bårdsen and Jan Ove Bustnes (2022). Multiple stressors: negative effects of nest predation on the viability of a threatened gull in different environmental conditions. Journal of Avian Biology. https://doi.org/10.1111/jav.02953. Bård-Jørgen Bårdsen and Jan Ove Bustnes (2023). Correction to Multiple stressors: negative effects of nest predation on the viability of a threatened gull in different environmental conditions. Journal of Avian Biology. https://doi.org/10.1111/jav.12915.

This study assessed the viability of a population of the lesser black-backed gull (Larus fuscus fuscus) using data collected from 2005-2020 from a nature reserve in Northern Norway. The study merged results from statistical analyses of empirical data with a Leslie model. Here, we provide the underlying data and the R-scripts used to analyse the data and run the model. The data set includes information about reproduction at several stages (laying, hatching and fledgling), nest predation, and individual capture histories (used to estimate apparent survival; see Bårdsen and Bustnes 2022). We discovered a misspecification error in the matrix model in Bårdsen and Bustnes (2022). This error did not change the overall conclusions or the results in the original article's empirical analyses. Here, we present an updated version of our scripts, i.e., scripts used by Bårdsen and Bustnes (2023). In the correction, we also highlight which part of the original article was affected by this mistake. Methods Bårdsen and Bustnes (2022), including the online Supplementary Material (Appendix S1-2), provide a detailed description of the study area and the empirical data. In the downloadable software ('ToBePublished.zip'), we provide data, metadata, and R-scripts for the statistical analyses and the models. Please confer with the 'README.txt' in 'ToBePublished.zip' for more information. We also include the data (without the scripts) from our study area as a downloadable dataset ('Data.zip'; see the included 'README.txt' for details).

Original dataset used for analyses presented in Reichard et al. (sex differences in lifespan in Nothobranchius) for Journal of Animal Ecology paper (2021/2022)It includes readme file, R scripts and basic data.