Find research datasets worth reusing

Datasets from the RecSys 2023 article "Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect".

We have publicly released the anonymized "new_release_stream.csv" dataset from the music streaming platform Deezer. This dataset is described in detail in the article titled "Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect", which was published in the proceedings of the 17th ACM Conference on Recommender Systems (RecSys 2023). Each row in the dataset contains an anonymized user and item identifier, a reference timestamp in seconds (measured from the first consumption in the dataset), and a binary value "y". This "y" value indicates whether a song was listened to for more than 80% of its duration (y = 1) or not (y = 0). You can find this dataset in the GitHub repository <a href="https://github.com/deezer/ex2vec">deezer/ex2vec</a>, where it is used to reproduce experiments discussed in the article. If you plan to use our code or data in your work, please make sure to cite our paper accordingly.   <pre><code>@inproceedings{sguerra2023ex2vec, title={Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect}, author={Sguerra, Bruno and Tran, Viet-Anh and Hennequin, Romain}, booktitle = {Proceedings of the 17th ACM Conference on Recommender Systems}, year = {2023} }</code></pre>

opencc-by-4.0Aug 2023View details →

Dataset and Experiment Scripts for "When Function Inlining Meets WebAssembly: A Counterintuitive Effect on Runtime Performance"

This repository contains the Experiment Results and Collection scripts for our ESEC/FSE 2023 submission, "When Function Inlining Meets WebAssembly: A Counterintuitive Effect on Runtime Performance" Our runtime experiment data is located in the Experiment Results directory. This directory contains two subdirectories, All Experiment Results and Counterintuitive Results Only. In All Experiment Results, we present the runtime results from our Experiments 1-5 and the Libsodium.js case study as CSV files. For the Chromium and Firefox results, each CSV file list the sample names and multiple columns for each of the four optimization levels, O0-O3. Under each optimization level, we list the runtime (in milliseconds) with inlining enabled (from the Baseline experiment), the runtime with inlining enabled (from one of Experiment #1-5), and percent change in runtime after disabling inlining. The Counterintuitive Results Only directory contains CSV files presenting only the samples from each Experiment #1-5 that meet our threshold of at least a 5% decrease in runtime after disabling inlining. The Excel file, Wasm Function Inlining Experiment Data.xlsx, contains all of these results in a single workbook, as well as formatting applied to highlight the counterintuitive runtime values presented in our paper. The raw data collected from our experiments, including the generated WebAssembly, HTML, and JS files to run the samples, is found under the RawCollectedData directory. This folder contains a zipped file that, when extracted, contains subdirectories for each sample's collected data. The CollectionScripts directory contains the scripts necessary to run our experiments. The PatchFiles directory contains the files from the Binaryen and LLVM infrastructures with the changes that we introduced to enable and disable select optimization passes through environment variables. The Scripts directory contains the scripts we used to run our experiments. The main file within this directory that serves as the entry point is opt_level_inlining.py. This file uses the other scripts to run Experiments 1-5 in our study. The script libsodiumjs_script.py is used to run our Libsodium.js case study. The scripts are written in Python and Node.js, and the dependencies to run the scripts are Node.js, Python, and MySQL. To download the necessary dependencies for the scripts, run the command `pip install -r requirements.txt` in Scripts folder, and run `npm install` in the Scripts/performance-measurement-tool folder. Then, import  the included MySQL schemas under the Scripts folder into a MySQL database. Update the MySQL connection details in the Scripts\__db_utils.py and Scripts\performance-measurement-tool\src\MySQLConnector.ts files, and then run `npm run build` in the Scripts\performance-measurement-tool folder.

Data and code for Haberle, Hackenberger et al.: Effects of climate change on gilthead seabream aquaculture in the Mediterranean

The submission was prepared to accompany the publication Haberle, Hackenberger et al. "Effects of climate change on gilthead seabream aquaculture in the Mediterranean" in Aquaculture (https://doi.org/10.1016/j.aquaculture.2023.740052). The simulations source code is available through GitHub repository at: https://github.com/QuantEcoLab/SparusSim_Haberle_et_al_2023 The Zanodo archive contains GeoTIFF images underlying the figures in the publication, with the corresponding description in the Readme file.

Dataset for "Droplet collection efficiencies inferred from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions"

This dataset in includes MODIS-CloudSat CFODD reference data, the updated Warm Rain Diagnostics implemented in COSPv2.0, RANSAC regression analysis, and figure production scripts associated with the manuscript “Droplet collection efficiencies estimated from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions” Authors:  Beall, Charlotte, M.; Ma, Po-Lun; Christensen, Matthew W.; Mülmenstädt, Johannes; Varble, Adam; Suzuki, Kentaroh; Michibata, Takuro Journal: Atmospheric Chemistry & Physics (submitted, 2023)

opencc-by-4.0Jul 2023View details →

The effect of normal stress oscillations on fault slip behavior near the stability transition from stable to unstable motion

Tectonic fault zones are subject to normal stress variations with a wide range of spatio-temporal scales. Stress perturbations cover a wide range of frequencies and amplitudes from high frequency seismic waves generated by earthquakes to low frequency transients associated with solid Earth tides. These perturbations can reactivate critically stressed faults and trigger earthquakes. Here, we describe lab experiments to illuminate the physics of such changes in friction and the mechanics of earthquake triggering and fault reactivation. Friction tests were done in a double direct shear configuration for conditions near the stability transition from stable to unstable motion. We studied simulated fault gouge composed of quartz powder and conducted experiments at reference normal stress from 10 to 13.5 MPa. After shearing to steady state sliding, we applied sinusoidal normal stress oscillations of amplitude 0.5 to 2 MPa, and period of 0.5 to 50 s. We performed numerical simulations using measured values of rate/state friction (RSF) parameters to assess our data. Our results show that low frequency stress oscillations cause a Coulomb-like response of shear strength that transitions from stable slip to slow lab earthquakes as frequency increases. At the critical frequency predicted by RSF we observe periodic stick-slip behavior. Perturbations of high amplitude and short period weaken the fault, while lower amplitudes strengthen the fault. We find that a modified RSF formulation is able to accurately match our laboratory data. Our findings highlight the complex effects of stress perturbations for fault strength and the mode of fault slip. The data are uploaded are structured as follow: 1) For each experiment a .txt file of the datafile that is recorded from the machine (raw data) and a binary file containing the elaborated data (data_rp). The experiments information are listed in experiment_info.txt 2) The folder <a href="https://zenodo.org/api/files/89fe30fb-a2cb-4fcb-b9df-db80583fc652/codes_results.zip">codes_results.zip</a> contain the codes of the data analysis and the related results  The data are analyzed using rawPy that can be found at <a href="https://github.com/marcoscuderi/rawPy">https://github.com/marcoscuderi/rawPy</a> For any additional information please do not hesitate to contact the corresponding author Federico Pignalberi at federico.pignalberi@uniroma1.it

opencc-by-4.0Oct 2023View details →

A battery of in silico models application for pesticides exerting reproductive health effects: assessment of performance and prioritization of mechanistic studies

Dataset of Table 1-7 Data of Table 1, “Pesticides and their classification” The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1_Tab1.PNG). Corresponding raw data is regarding classification in the hazard class reproductive toxicity available on line. All further related information are provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK__Tab1_PPP_27_1_M.txt) in txt format.   Data of Table 2, “PDB structures of nuclear receptors used in VTL and ED”  The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1_Tab2 15 meta data files as pdf-format with information sources of PDB structures used in employed in silico models (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M1.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M2.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M3.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M4.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M5.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M6.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M7.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M8.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M9.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M10.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M11.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M12.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M13.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M14.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M15.pdf). All further related information are provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab2_27_2_M.txt) in txt format.   Data of Table 3, “Results of in vivo studies (Shepelska et al., 2021; Shepelskaya and Kolyanchuk, 2021; Shepelskaya and Kolianchuk, 2018)” The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1_Table3.PNG). Three meta data file as pdf-format with data of in vivo studies (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_3_M1.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_3_M2.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_3_M3.pdf). All further related information are provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab3_27_3_M.txt) in txt format.   Data of Table 4, “Results of in silico modelling of pesticides interaction with nuclear receptors” The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1 _Tab4.PNG). Corresponding raw data with in silico modelling results provided as two files in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1-17.csv) and seventeen pdf files (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_2.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_3.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_4.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_5.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_6.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_7.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_8.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_9.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_10.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_11.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_12.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_13.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_14.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_15.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_16.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_17.pdf). Four meta data file as pdf-format with detailed in silico modelling descriptions (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M1.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M2.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M3.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_M1.pdf). All further related information is provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab4_24_1-2_M.txt) in txt format.   Data of Tabe 5, “Combination of in silico results with in vitro results by considering as positive result only where both in silico models predict a hit (Combined 1)” The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1 _Tab5.PNG). Corresponding raw data with in silico modelling results provided as two files in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1-17.csv) and seventeen pdf files (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_2.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_3.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_4.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_5.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_6.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_7.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_8.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_9.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_10.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_11.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_12.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_13.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_14.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_15.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_16.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_17.pdf). Four meta data file as pdf-format with detailed in silico modelling descriptions (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M1.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M2.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M3.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_M1.pdf). Corresponding raw data with ToxCast results provided as seventeen files in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_1.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_2.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_3.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_4.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_5.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_6.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_7.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_8.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_9.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_10.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_11.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_12.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_13.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_14.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_15.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_16.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_17.csv)All further related information is provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab5_24_25_1_M.txt) in txt format.   Data of Table 6, “Combination of in silico results with in vitro results by considering as a positive any in silico hit independently of the employed model (Combined 2)” The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1 _Tab6.PNG). Corresponding raw data with in silico modelling results provided as two files in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1-17.csv) and seventeen pdf files (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_2.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_3.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_4.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_5.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_6.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_7.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_8.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_9.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_10.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_11.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_12.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_13.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_14.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_15.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_16.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_17.pdf). Four meta data file as pdf-format with detailed in silico modelling descriptions (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M1.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M2.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M3.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_M1.pdf). Corresponding raw data with ToxCast results provided as seventeen files in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_1.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_2.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_3.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_4.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_5.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_6.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_7.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_8.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_9.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_10.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_11.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_12.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_13.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_14.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_15.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_16.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_17.csv)All further related information is provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab6_24_25_1_M.txt) in txt format.   Data of Table 7, “Metrics of performance of in silico models separately and combined.” The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1 _Tab7.PNG). Corresponding raw data with calculation of relevant performance metrics provided as one file in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_26_1.csv). One meta data file as pdf-format with detailed description of the method used for calculation (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_26_1_M1.pdf). All further related information is provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab7_26_1_M.txt) in txt format.

Analysis of AaH-II effect in the axon initial of neocortical pyramidal neurons

This dataset contains data in HEK293 neurons expressing either human Nav1.2 or human Nav1.6 and whole-cell electrophysiological recordings and imaging data from neocortical layer-5 pyramidal neuron in brain slices of the mouse.   This dataset is used in the paper: Abbas F, Blömer LA, Millet H, Montnach J, De Waard M, Canepari M. Analysis of the effect of the scorpion toxin AaH-II on action potential generation in the axon initial segment. bioRxiv, 2023. https://www.biorxiv.org/content/10.1101/2023.10.06.561226v1

opencc-by-4.0Oct 2023View details →

AlphaFold structures reported in "AlphaFold2 Can Predict Single-Mutation Effects"

This contains AlphaFold predictions for X proteins that are found in the Protein Data Bank (PDB), that were used to evalluate AlphaFold's predictions of mutation effects. This includes one set of structures predicted by AlphaFold2.0, using default settings, and one structure for each of 5 models. This also includes structures predicted by the ColabFold version of AlphaFold (6 recycles, 5 models, no template, amber minimization, 4 repeats).There are also additional predicted structures that are found in the PDB that were not analyzed in the paper.There are AlphaFold predictions for three proteins (BFP / RFP, GFP, and PafA), covering either all (BFP/RFP, PafA) or a subset (GFP) of the sequences in three datasets of phenotype measurements from high-throughput experiments.Results are separated into tar files based on whether DeepMind  (AF2.0) or ColabFold implementation was used.Folders under "ColabFold/PDB" are labelled according to a sequence ID, since multiple PDB structures can exist for a single sequence. These sequence IDs can be mapped back to PDB IDs using the information in "seq_id_pdb_id.json".All PDB files have been compressed using Foldcomp (<a href="https://github.com/steineggerlab/foldcomp">https://github.com/steineggerlab/foldcomp</a>). Foldcomp is required to decompress the ".fcz" files in order to recover the ".pdb" files.

opencc-by-4.0May 2019View details →

Data Sets of Cause-Effect Pairs

Data Sets of Cause-Effect Pairs first used in the following paper: Answering Binary Causal Questions Through Large-Scale Text Mining: An Evaluation Using Cause-Effect Pairs from Human Experts Oktie Hassanzadeh, Debarun Bhattacharjya, Mark Feblowitz, Kavitha Srinivas, Michael Perrone, Shirin Sohrabi, Michael Katz IJCAI 2019 <pre><code>@inproceedings{Hassanzadeh19, author = {Oktie Hassanzadeh and Debarun Bhattacharjya and Mark Feblowitz and Kavitha Srinivas and Michael Perrone and Shirin Sohrabi and Michael Katz}, title = {Answering Binary Causal Questions Through Large-Scale Text Mining: An Evaluation Using Cause-Effect Pairs from Human Experts}, booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, {IJCAI} 2019, August 10-16, 2019, Macao, China}, year = {2019} }</code></pre> See README.txt for details. <pre>$ wc -l * 319 ce_me_benchmark_v1.csv 118 nato_sfa_benchmark_v1.csv 804 risk_models_benchmark_v1.csv 1730 semeval_benchmark_v1.csv 2971 total $ ls -lh * | awk '{print $5,$9}' 23K ce_me_benchmark_v1.csv 11K nato_sfa_benchmark_v1.csv 73K risk_models_benchmark_v1.csv 42K semeval_benchmark_v1.csv NATO SFA Benchmark is created from the tables in the Appendix of the following publicly available document: STRATEGIC FORESIGHT ANALYSIS 2017 REPORT Links: <a href="https://www.act.nato.int/images/stories/media/doclibrary/171004_sfa_2017_report_hr.pdf">https://www.act.nato.int/images/stories/media/doclibrary/171004_sfa_2017_report_hr.pdf</a> <a href="https://www.act.nato.int/images/stories/media/doclibrary/171004_sfa_2017_report_txt.pdf">https://www.act.nato.int/images/stories/media/doclibrary/171004_sfa_2017_report_txt.pdf</a> <a href="https://www.act.nato.int/futures-work">https://www.act.nato.int/futures-work</a> SemEval data is published under the Creative Commons Attribution 3.0 Unported license: <a href="https://creativecommons.org/licenses/by/3.0/">https://creativecommons.org/licenses/by/3.0/</a> Details: <a href="https://docs.google.com/document/d/1QO_CnmvNRnYwNWu1-QCAeR5ToQYkXUqFeAJbdEhsq7w/preview">https://docs.google.com/document/d/1QO_CnmvNRnYwNWu1-QCAeR5ToQYkXUqFeAJbdEhsq7w/preview</a> Original source: <a href="https://drive.google.com/file/d/0B_jQiLugGTAkMDQ5ZjZiMTUtMzQ1Yy00YWNmLWJlZDYtOWY1ZDMwY2U4YjFk/view?sort=name&layout=list&num=50">https://drive.google.com/file/d/0B_jQiLugGTAkMDQ5ZjZiMTUtMzQ1Yy00YWNmLWJlZDYtOWY1ZDMwY2U4YjFk/view?sort=name&layout=list&num=50</a> The rest of the data sets are covered by the Creative Commons: Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a> THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</pre>

dryad44/100

Contrasting effects of landscape composition on crop yield mediated by specialist herbivores

Open the record for dataset details and reuse information.

publicAug 2023View details →

openCC0Oct 2021View details →

Effects of freshwater salinization on a salt-naïve planktonic eukaryote community

Salinization of freshwater ecosystems is a widespread issue, but evidence of ecological effects on aquatic eukaryote communities remains scarce. We experimentally exposed naive planktonic communities of a north-temperate, freshwater lake to a gradient of chloride (Cl-) concentration (0.27-1400 mg Cl-.L-1) with in-situ mesocosms. Following six weeks of exposure, we measured changes in the diversity, composition, and abundance of eukaryotic 18S rRNA gene. Total phytoplankton biomass remained unchanged, but we observed a shift in dominant phytoplankton groups with elevated salt concentration, from Cryptophyta and Chlorophyta that dominated in lower chloride concentrations (<185 mg Cl-.L-1) to Ochrophyta that dominated at higher conductivity (>185 mg Cl-.L-1). Most zooplankton and rotifer taxa were sensitive to the salinity and disappeared at low chloride concentrations (<40 mg Cl-.L-1). While ciliates thrived at low chloride concentrations (<185 mg Cl-.L-1), fungal groups dominated at intermediate chloride concentrations (185 mg Cl-.L-1 to 640 mg Cl-.L-1), and only phytoplankton remained at the highest chloride concentrations (> 640 mg Cl-.L-1).

openCC (other)Dec 2021View details →

Data from: Nutrient identity modifies the destabilizing effects of eutrophication in grasslands

Nutrient enrichment can simultaneously increase and destabilize plant biomass production, with co-limitation by multiple nutrients potentially intensifying these effects. Here, we test how factorial additions of nitrogen (N), phosphorus (P), and potassium with essential nutrients (K+) affect the stability (mean/standard deviation) of aboveground biomass in 34 grasslands over seven years. Destabilization with fertilization was prevalent but was driven by single nutrients, not synergistic nutrient interactions. On average, N-based treatments increased mean biomass production by 21-51% but increased its standard deviation by 40-68% and so consistently reduced stability. Adding P increased interannual variability and reduced stability without altering mean biomass, while K+ had no general effects. Declines in stability were largest in the most nutrient-limited grasslands, or where nutrients reduced species richness or intensified species synchrony. We show that nutrients can differentially impact the stability of biomass production, with N and P in particular disproportionately increasing its interannual variability.

openCC0Apr 2022View details →

Dataset from University of Idaho 2004, master's thesis [Littoral ecology of epilithic algae in the Rocky Reach Pool, Mid-Columbia River (Washington State) - The effects of reservoir fluctuations.]

(Abstract from thesis) Epilithic algae, water column physical/chemical properties, and sediments were examined in the impounded Mid-Columbia River including the Rocky Reach Reservoir. Primary objectives included determination of the effects reservoir drawdown has on epilithic algae and potential nutrient enrichment via sediment. Epilithic algae were analyzed by pigment concentration, gravimetrically, and species composition. Reservoir elevation fluctuated at higher rates at tailrace sites (0.41-0.25 m/hr) compared to the forebay site (0.06-0.08 m/hr). Littoral exposure times were also greater at tailrace sites (mean of 8 hrs compared to 0 hr at the forebay site). Mean epilithic algae monochromatic chlorophyll a over all sampling periods at mainstem sites was 76.7 ± 4.8 mg/m2 (95 % C.I.). Epilithic algae monochromatic chlorophyll a in the zone of water fluctuation (0-1 m) was less at Wells tailrace (38.8 mg/m2) compared to Rocky Reach forebay (141.3 mg/m2) during summer, 2000 and 2001. Mean epilithic biofilm ash-free oven-dry weight over all sampling periods at mainstem sites was 25.6 ± 1.5 g/m2 (95 % C.I.). Mean autotrophic index across all mainstem locations was 439 indicating a large heterotrophic component within the epilithic biofilms. Epilithic algae communities were dominated by diatoms (50.2 %) and cyanobacteria (35.9 %), with some green algae (13.8 %). Canonical correlation analysis indicated that temperature, depth, site, and the water elevation change rate were important controllers of epilithic algae chlorophyll pigments. Mean textural characteristics of dredged sediment were 51.1 % sand, 43.2 % silt, and 5.7 % clay. Mean organic matter content in this sediment was 4.1 %. The mean seston sedimentation rate across mainstem locations was 11.3 g m-2 d-1 and organic matter comprised 14.7 % of the material collected from the water column.

openCC (other)Jan 2023View details →

Experimental microcosm incubations assessing the effect of hypoxia on aqueous iron and organic carbon, pH, sediment organic carbon, and sediment iron-bound organic carbon

To assess the effect of changing oxygen concentrations on coupled carbon and iron cycling in freshwater ecosystems, we performed 6-week microcosm incubations. Incubations were inoculated with sediment and water from Falling Creek Reservoir, Vinton, VA, USA. We started the experiment with 102 microcosms split evenly into oxic and hypoxic treatments. After two weeks, we switched the treatment of approximately half of the remaining microcosms, generating a total of four oxygen regimes: hypoxic, oxic, hypoxic to oxic, and oxic to hypoxic. We sampled the microcosms destructively approximately twice per week, collecting aqueous samples for total and dissolved carbon and iron, as well as sediment samples for organic carbon and iron-bound organic carbon analysis. Iron-bound organic carbon was determined using citrate-bicarbonate-dithionite extractions.

openCC (other)Jan 2023View details →

Urbanization and fragmentation have opposing effects on soil nitrogen availability in temperate forest ecosystems.

Nitrogen (N) availability relative to plant demand has been declining in recent years in terrestrial ecosystems throughout the world, a phenomenon known as N oligotrophication. The temperate forests of the northeastern U.S. have experienced a particularly steep decline in bioavailable N, which is expected to be exacerbated by climate change. This region has also experienced rapid urban expansion in recent decades that leads to forest fragmentation, and it is unknown whether and how these changes affect N availability and uptake by forest trees. Many studies have examined the impact of either urbanization or forest fragmentation on nitrogen (N) cycling, but none to our knowledge have focused on the combined effects of these co-occurring environmental changes. We examined the effects of urbanization and fragmentation on oak-dominated (Quercus spp.) forests along an urban to rural gradient from Boston to central Massachusetts (MA). At eight study sites along the urbanization gradient, plant and soil measurements were made along a 90 m transect from a developed edge to an intact forest interior. Rates of net ammonification, net mineralization, and foliar N concentrations were significantly higher in urban than rural sites, while net nitrification and foliar C:N were not different between urban and rural forests. At urban sites, foliar N and net ammonification and mineralization were higher at forest interiors compared to edges, while net nitrification and foliar C:N were higher at rural forest edges than interiors. These results indicate that urban forests in the northeastern U.S. have greater soil N availability and N uptake by trees compared to rural forests, counteracting the trend for widespread N oligotrophication in temperate forests around the globe. Such increases in available N are diminished at forest edges, however, demonstrating that forest fragmentation has the opposite effect of urbanization on coupled N availability and demand by trees.

openCC (other)Jan 2023View details →

Less fuel for the next fire? Short-interval fire delays forest recovery and interacting drivers amplify effects, Greater Yellowstone Ecosystem, Montana and Wyoming, USA

As 21st-century climate and disturbance dynamics depart from historical baselines, ecosystem resilience is uncertain. Multiple drivers are changing simultaneously, and interactions among drivers could amplify ecosystem vulnerability to change. We explored how interacting drivers affected post-fire recovery of subalpine forests, which Subalpine forests in Greater Yellowstone (Northern Rocky Mountains, USA) were historically resilient to infrequent (100-300 year), severe fire., in Greater Yellowstone (Northern Rocky Mountains, USA). We sampled paired short- (< 30 year) and long- (> 125 year) interval post-fire plots most recently last burned between 1988 and 2018 to address two questions: (1) How do short-interval fire, climate, topography, and distance to unburned live forest edge and other factors (topography, distance to live edge) interact to affect post-fire forest recoveryregeneration? (2) How do forest biomass and fuels vary following short- versus long-interval severe fires? Mean post-fire live stem density was an order of magnitude lower following short- versus long-interval fires (3,240 versus 28,741 stems ha-1, respectively). Differences between paired plots increased with greater climate water deficit normal (ρ = 0.67) and were amplified at longer distances to live forest edge. Surprisingly, warmer-drier climate was associated with higher seedling densities even after short-interval fire, likely relating to regional variation in serotiny of lodgepole pine (Pinus contorta var. latifolia). Unlike conifers, density of aspen (Populus tremuloides), a deciduous resprouter, increased with short- versus long-interval fire (mean 384 versus 62 stems ha-1, respectively). Live biomass and canopy fuels remained low nearly 30 years after short-interval fire, in contrast to rapid recovery after long-interval fire, suggesting that future burn severity may be reduced for several decades following reburns. Short-interval plots also had half as much dead woody biomass compar

openCC (other)Sep 2023View details →

Data in Support of Effects of Urbanization and Forest Fragmentation on Atmospheric Nitrogen Inputs and Ambient Nitrogen Oxide and Ozone Concentrations in Mixed Temperate Forests.

Urban ecosystems around the globe experience greater atmospheric nitrogen (N) deposition compared to rural areas and are particularly vulnerable to fragmentation due to land-use change. However, while the influences of urbanization and forest fragmentation on atmospheric inputs to temperate forests have been determined separately, the combined effects of the two changes on temperate forest ecosystems have yet to be assessed. To investigate these combined effects, we deployed throughfall collectors to measure atmospheric N inputs and passive samplers to measure nitrogen oxides (NOx) and ozone (O3) throughout the 2018 and 2019 growing seasons in seven temperate forest sites along an urbanization gradient from Boston to central Massachusetts. We found a positive relationship between the amount of impervious surface area surrounding each site (% ISA) and throughfall nitrate (NO3-) inputs at the forest edge, with urban edge NO3- inputs nearly double the rate at rural edge sites. There were higher rates of NO3- inputs in the rural forest interior than edge sites. Urban sites experienced significantly higher concentrations of NOx and O3 both in the interior and at the edge compared to rural sites. Atmospheric N inputs were significantly elevated in the early (May-July) compared to the late (August-November) growing season and concentrations of NOx and O3 were also elevated in the mid-growing season (June-September). Our results demonstrate that together, urbanization and forest fragmentation lead to greater rates of atmospheric N inputs and ambient pollutant concentrations of NOx and O3 in temperate forests of the northeastern U.S.

openCC (other)Jun 2023View details →

Effects of drying temperature on potential carbon mineralization and water-extractable organic carbon in Iowa cropland and riparian buffer soils

Measuring carbon dioxide (CO2) produced after re-wetting a previously dried soil is an increasingly popular soil health assay, but there is disagreement on the optimal soil drying temperature. We tested whether soil drying temperature impacts water-extractable organic carbon (WEOC) and soil CO2 emissions (potential carbon mineralization) following rewetting of dried soil. Samples were collected at four sites in north-central Iowa, US, and each site had soils planted to corn/soybean or perennial vegetation. The dataset includes measurements of WEOC prior to the incubation experiment, and measurements of CO2 flux and its stable carbon isotope ratio over the course of a 28-day incubation. The manuscript describing these data is under review in Geoderma.

openCC0Nov 2023View details →

Data from publication: Castillioni, K., & Isbell, F. (2023). Early positive spatial selection effects of beta-diversity on ecosystem functioning. Landscape Ecology, 1-15.

Data from publication: Castillioni, K., & Isbell, F. (2023). Early positive spatial selection effects of beta-diversity on ecosystem functioning. Landscape Ecology, 1-15. Spatial beta-diversity may increase landscape productivity if there are positive spatial selection effects. Alternatively, dominant species in mixtures might not be the most productive species in monoculture leading to negative or neutral spatial selection effects. However, these hypotheses remain untested experimentally. Seedling survival can determine species establishment, influencing productivity later. To address this knowledge gap, we experimentally tested whether transplanted seedlings of dominant species optimally sort among habitat types (grassland dominated by Andropogon gerardii, savanna by Quercus macrocarpa, deciduous forest by Acer rubrum, coniferous forest by Pinus strobus, bog by Larix laricina), creating positive effects of landscape diversity on seedling survival and net biodiversity effects at Cedar Creek Ecosystem Science Reserve (CCESR) in Minnesota, USA. The study is named BetaDIV and consists of 100 plots (20 plots per habitat × 5 habitats). Each of the five habitats includes two true replicate monocultures for each of the five species and two true replicates for each of the five possible mixture compositions of four species (leaving each one out in turn to eventually explore the effect of species identity). Each plot is 1.5 by 1.5 m, with 12 seedlings planted 0.5 m apart in a 4 × 4 square grid, except in the plot corners. In the early June 2022, we tagged and planted all seedlings (i.e., bareroot seedlings for trees and plugs for the grass A. gerardii). Two weeks after the initial transplanting, we started tracking seedling survival (presented here) to investigate how seedlings responded to local habitat conditions. We conducted a seedling census for each of the 1200 tagged seedlings (12 seedlings per plot×100 plots), in early September 2022, which was two months at the end