Find research datasets worth reusing

opencc-by-4.0Dec 2016View details →

Competitive growth experiments with a high-lipid Chlamydomonas reinhardtii mutant strain and its wild-type to predict industrial and ecological risks

Key microalgal species are currently being exploited as biomanufacturing platforms using mass cultivation systems. The opportunities to enhance productivity levels or produce non-native compounds are increasing as genetic manipulation and metabolic engineering tools are rapidly advancing. Regardless of the end product, there are both environmental and industrial risks associated to open pond cultivation of mutant microalgal strains. A mutant escape could be detrimental to local biodiversity and increase the risk of algal blooms. Similarly, if the cultivation pond is invaded by a wild-type microalgae or the mutant reverts to wild-type phenotypes, productivity could be impacted. To investigate these potential risks, a response surface methodology was applied to determine the competitive outcome of two Chlamydomonas reinhardtii strains, a wild-type (CC-124) and a high-lipid accumulating mutant (CC-4333), grown in mixotrophic conditions, with differing levels of nitrogen and initial wild-type to mutant ratios. Results of the growth experiments show that mutant cells have double the exponential growth rate of the wild-type in monoculture. However, due to a slower transition from lag phase to exponential phase, mutant cells are outcompeted by the wild-type in every co-culture treatment. This suggests that, under the conditions tested, outdoor cultivation of the C. reinhardtii cell wall-deficient mutant strains does not carry a significant environmental risk to its wild-type in an escape scenario. Furthermore, lipid results show the mutant strain accumulates over 200% more TAGs per cell, at 50 mg/L NH4Cl, compared to the wild-type, therefore, the fragility of the mutant strain could impact on overall industrial productivity.

opencc-by-4.0Jul 2010View details →

Defect Prediction: Xerces

Background: This paper describes an analysis that was conducted on newly collected repository with 92 versions of 38 proprietary, open-source and academic projects. A preliminary study performed before showed the need for a further in-depth analysis in order to identify project clusters. Aims: The goal of this research is to perform clustering on software projects in order to identify groups of software projects with similar characteristic from the defect prediction point of view. One defect prediction model should work well for all projects that belong to such group. The existence of those groups was investigated with statistical tests and by comparing the mean value of prediction efficiency. Method: Hierarchical and k-means clustering, as well as Kohonen’s neural network was used to find groups of similar projects. The obtained clusters were investigated with the discriminant analysis. For each of the identified group a statistical analysis has been conducted in order to distinguish whether this group really exists. Two defect prediction models were created for each of the identified groups. The first one was based on the projects that belong to a given group, and the second one - on all the projects. Then, both models were applied to all versions of projects from the investigated group. If the predictions from the model based on projects that belong to the identified group are significantly better than the all-projects model (the mean values were compared and statistical tests were used), we conclude that the group really exists. Results: Six different clusters were identified and the existence of two of them was statistically proven: 1) cluster proprietary B – T=19, p=0.035, r=0.40; 2) cluster proprietary/open - t(17)=3.18, p=0.05, r=0.59. The obtained effect sizes (r) represent large effects according to Cohen’s benchmark, which is a substantial finding. Conclusions: The two identified clusters were described and compared with results obtained by other researchers. The results of this work makes next step towards defining formal methods of reuse defect prediction models by identifying groups of projects within which the same defect prediction model may be used. Furthermore, a method of clustering was suggested and applied.

opencc-by-4.0Feb 2017View details →

Raw data for CPC2015: A Choice Prediction Competition for decisions under risk, under Ambiguity, and from experience

This is the raw data collected as part of a choice prediction competition organized by the three authors for decisions under risk, under ambiguity, and from experience. It includes over 330,000 consequential choices of human participants between two risky and/or uncertain prospects. More details about the competition and the data can be found in http://departments.agri.huji.ac.il/cpc2015. If you use this data, please cite the paper describing the competition: Erev, I., Ert, E., Plonsky, O., Cohen, D., & Cohen, O. (2017). From anomalies to forecasts: Toward a descriptive model of decision under risk, under ambiguity, and from experience. Psychological Review, 124(4), 369-409. DOI: 10.1037/rev0000062

opencc-by-4.0Mar 2017View details →

Data supplementing the article "Einhäuser, W., & Nuthmann, A. (2016). Salient in space, salient in time: Fixation probability predicts fixation duration during natural scene viewing. Journal of Vision, 16(11):13, 1-17, doi:10.1167/16.11.13."

These data supplement the article Einhäuser, W., & Nuthmann, A. (2016). Salient in space, salient in time: Fixation probability predicts fixation duration during natural scene viewing. Journal of Vision, 16(11):13, 1-17, doi:10.1167/16.11.13. The data can be used freely for academic purposes, provided the aforementioned reference is appropriately cited. The following files are available for experiment 2 of the article: allData.mat Includes the datamatrix allData with the following columns: 1) Line used for analysis in the article (0 - no, 1-yes). Possible reasons for exclusion: a. fixation duration smaller than 50ms or larger 1000ms b. fixation adjacent to a blink (preceding or following) c. fixation outside the image 2) ID of observer (1-24) 3) ID of condition (1:grayscale, 2: reduced luminance, 3: reduced contrast, 4: equalized luminance, 5: equalized contrast, 6: phasenoise) 4) ID of image (48 unique numbers between 1 and 135) 5) horizontal eye position 6) vertical eye position 7) fixation duration in ms 8) value of empirical map generated from search condition of experiment 1 at fixated location 9) value of empirical map generated from preference condition of experiment 1 at fixated location 10) value of empirical map generated from memorization condition of experiment 1 at fixated location 11) value of empirical map generated from joining memorization and preference condition of experiment 1 at fixated location 12) value of empirical map generated from condition 1 at fixated location 13) value of empirical map generated from condition 2 at fixated location 14) value of empirical map generated from condition 3 at fixated location 15) value of empirical map generated from condition 4 at fixated location 16) value of empirical map generated from condition 5 at fixated location 17) value of empirical map generated from condition 6 at fixated location 18) value of empirical map generated from condition 1 at fixated location leaving out the current observer 19) value of empirical map generated from condition 2 at fixated location leaving out the current observer 20) value of empirical map generated from condition 3 at fixated location leaving out the current observer 21) value of empirical map generated from condition 4 at fixated location leaving out the current observer 22) value of empirical map generated from condition 5 at fixated location leaving out the current observer 23) value of empirical map generated from condition 6 at fixated location leaving out the current observer 24) luminance at fixation 25) luminance contrast at fixation 26) edge density at fixation 27) eccentricity of fixation usedData.Rdata - for all lines that are used for analysis (allData(:,1)==1) a field in an R dataframe is created, which contains the following fields (for details, see description of matlab file above): obsNum: the ID of the observer (1-24) condNum: the ID of the condition (1-6) imgNum: the ID of the image (48 unique numbers between 1 and 135) fixDur: fixation duration LUM, LCG, ED, ECC: luminance, contrast, edge density and eccentricity at fixation empMapFromSearch, empMapFromPref, empMapFromMem, empMapFromJoint: values of empirical maps generated from data of experiment 1 (search, preference, memorization task as well as combination of the latter two) at fixation empMapFromC1 through empMapFromC6: value of empirical map generated from condition 1 through 6 at fixated location empMapFromC1loo through empMapFromC6loo - value of empirical map generated from condition 1 through 6 at fixated location leaving out the current observer x,y - coordinates of fixation modelsFigure7.R - computes all models for figure 7 of the aforementioned article (Note: depending on your system, this can take substantial time; depending on the version of the lme-package results may deviate slightly from those given in the paper) modelsFigure8.R - computes all models for figure 8 of the aforementioned article (Note: depending on your system, this can take substantial time; depending on the version of the lme-package results may deviate slightly from those given in the paper)

opencc-by-4.0Nov 2024View details →

Data accompanying publication: "General Chemically Intuitive Atom-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction"

Embeddings and raw files to complement the paper "General Chemically Intuitive Atom-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction". The embeddings should be all the data needed for full reproducibility of the results published. The GitHub repo GeneralDFT (https://github.com/moleculebits/GeneralDFT) contains the python scripts required to make use of the data, along with some basic plotting functionalities.

opencc-by-4.0Mar 2024View details →

Supplementary material for the publication: "Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties"

This dataset contains supplementary code, images and models for the publication &bdquo;Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties“.   The content will be updated and additionally linked to the corresponding git repositories.

opencc-by-4.0Feb 2023View details →

Generative artificial intelligence predicts human performance

Research data for a study that used generative artificial intelligence (i.e., ChatGPT with the GPT-4 and the Google Gemini 2.0 Flash models) to predict human performance in a language-based memory task. In particular, we studied the effects of context on the relatedness and memorability of garden-path sentences.

openapache2.0Mar 2024View details →

Interformer: An Interaction-Aware Model for Protein-Ligand Docking and Affinity Prediction

The code, dataset, and model weights are described in the paper "Interformer: An Interaction-Aware Model for Protein-Ligand Docking and Affinity Prediction."   experiment_results.zip: Contains generated results that can reproduce the result from the reported paper. benchmark.zip: Contains docking and affinity input data of the interformer. You can use the source code to make predictions and reproduce the number of the reported paper. checkpoints.zip: Contains one weight for the Energy and four PoseScore and Affinity models. source_code_1.0.zip: Contains the initial version of the source code. interformer_train.tar.gz: Contains prepared training data for interformer. poses/ contains all structure need for training, poses/ligand contains the re-docking poses generated by interformer energy, poses/ligand/rcsb contains the conformation of reference ligand, poses/pocket contains all pocket extract by raw PDB from rcsb, poses/uff contains all ligand conformation minimized using UFF from reference ligand, and train/ contains the training csv. baseline_results.tar.gz:  Contains the predictions from three methods: Interformer, DiffDock, and DeepDock. The results align with the exact numbers reported in the paper. For further details, please refer to the eda/ directory.   You can also find the newest version of the source code at <a href="https://github.com/tencent-ailab/Interformer" target="_blank" rel="noopener">https://github.com/tencent-ailab/Interformer</a>

opencc-by-nc-4.0May 2024View details →

MagicBathyNet: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-based Classification in Shallow Waters

The dataset MagicBathyNet is a benchmark dataset made up of image patches of Sentinel-2, SPOT-6 and aerial imagery, bathymetry in raster format and seabed classes annotations. MagicBathyNet has been designed to be geographically well distributed. It’s coverage includes two very different coastal areas (in terms of water column characteristics and bottom type): i) Agia Napa area in Cyprus, covering a wide range of typical Mediterranean waters and seabed types, and ii) Puck Lagoon area in Poland, representing in a great degree Baltic Sea waters and bottom. MagicBathyNet contains 3355 RGB co-registered triplets of Sentinel-2 (S2), SPOT-6, and aerial image patches, complemented by 1244 RGB co-registered S2 and SPOT-6 doublets, 3354 DSM (Digital Surface Model) raster patches for the aerial patches and 3396 DSM raster patches for S2 and SPOT-6. Additionally, it contains 533 annotated raster patches for seabed habitat and type, facilitating supervised pixel-based classification. Each patch covers 180x180m, represented by 18x18 pixels in S2 imagery, 30x30 pixels in SPOT-6 imagery and 720x720 pixels in airborne imagery.  For the implementation code and pre-trained models visit our project page: <a href="https://www.magicbathy.eu/magicbathynet.html">https://www.magicbathy.eu/magicbathynet.html</a>  MagicBathyNet.zip file contains the original dataset presented in the respective paper. MagicBathyNet_extension_for_Swin-BathyUNet.zip file is added in the new version to support the experiments and the results presented in "Agrafiotis, P., & Demir, B. (2025). Deep learning-based bathymetry retrieval without in-situ depths using remote sensing imagery and SfM-MVS DSMs with data gaps. ISPRS Journal of Photogrammetry and Remote Sensing, 225, 341-361. <a href="https://doi.org/10.1016/j.isprsjprs.2025.04.020">https://doi.org/10.1016/j.isprsjprs.2025.04.020</a> "     Citation If you use the code in this repository or the dataset please cite our paper: P. Agrafiotis, L. Janowski, D. Skarlatos, and B. Demir, <a href="https://arxiv.org/abs/2405.15477" target="_blank" rel="noopener noreferrer">"MagicBathyNet: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-based Classification in Shallow Waters"</a>, arXiv:2405.15477, 2024. or  P. Agrafiotis, Ł. Janowski, D. Skarlatos and B. Demir, "MAGICBATHYNET: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-Based Classification in Shallow Waters," IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 249-253, doi: 10.1109/IGARSS53475.2024.10641355. Folder structure ┗ 📂 magicbathynet/   ┣ 📂 agia_napa/   ┃ ┣ 📂 img/   ┃ ┃ ┣ 📂 aerial/   ┃ ┃ ┃ ┣ 📜 img_339.tif   ┃ ┃ ┃ ┣ 📜 ...   ┃ ┃ ┣ 📂 s2/   ┃ ┃ ┃ ┣ 📜 img_339.tif   ┃ ┃ ┃ ┣ 📜 ...   ┃ ┃ ┣ 📂 spot6/   ┃ ┃ ┃ ┣ 📜 img_339.tif   ┃ ┃ ┃ ┣ 📜 ...   ┃ ┣ 📂 depth/   ┃ ┃ ┣ 📂 aerial/   ┃ ┃ ┃ ┣ 📜 depth_339.tif   ┃ ┃ ┃ ┣ 📜 ...   ┃ ┃ ┣ 📂 s2/   ┃ ┃ ┃ ┣ 📜 depth_339.tif   ┃ ┃ ┃ ┣ 📜 ...   ┃ ┃ ┣ 📂 spot6/   ┃ ┃ ┃ ┣ 📜 depth_339.tif   ┃ ┃ ┃ ┣ 📜 ...   ┃ ┣ 📂 gts/   ┃ ┃ ┣ 📂 aerial/   ┃ ┃ ┃ ┣ 📜 gts_339.tif   ┃ ┃ ┃ ┣ 📜 ...   ┃ ┃ ┣ 📂 s2/   ┃ ┃ ┃ ┣ 📜 gts_339.tif   ┃ ┃ ┃ ┣ 📜 ...   ┃ ┃ ┣ 📂 spot6/   ┃ ┃ ┃ ┣ 📜 gts_339.tif   ┃ ┃ ┃ ┣ 📜 ...   ┃ ┣ 📜 [modality]_split_bathymetry.txt   ┃ ┣ 📜 [modality]_split_pixel_class.txt   ┃ ┣ 📜 norm_param_[modality]_an.txt   ┃   ┣ 📂 puck_lagoon/   ┃ ┣ 📂 img/   ┃ ┃ ┣ 📜 ...   ┃ ┣ 📂 depth/   ┃ ┃ ┣ 📜 ...   ┃ ┣ 📂 gts/   ┃ ┃ ┣ 📜 ...   ┃ ┣ 📜 [modality]_split_bathymetry.txt   ┃ ┣ 📜 [modality]_split_pixel_class.txt   ┃ ┣ 📜 norm_param_[modality]_pl.txt   Package for benchmarking MagicBathyNet dataset Donwload the package for benchmarking MagicBathyNet dataset in learning-based bathymetry and pixel-based classification here: <a href="https://github.com/pagraf/MagicBathyNet">https://github.com/pagraf/MagicBathyNet</a>   Version history v1.0.0 - First release   License Dataset: Creative Commons Attribution Non Commercial 4.0 International Code: Attribution-NonCommercial-ShareAlike 4.0 International License Copyright (c) 2024 The MagicBathyNet Authors   Acknowledgments This work was part of the project MagicBathy which is a research project funded by the European Commission for the period 2023-2025. It is funded under the HORIZON Europe MSCA Postdoctoral Fellowships - European Fellowships (GA 101063294). European Space Agency (ESA) is also acknowledged for providing the SPOT-6 images within its TPM programme in the frame of proposal PP0092443 and Airbus for being the provider of the original SPOT-6 images. The Dep. of Land and Surveys of Cyprus is acknowledged for providing the LiDAR reference data for Cyprus.

opencc-by-4.0Oct 2023View details →

WildfireSpreadTS: A dataset of multi-modal time series for wildfire spread prediction

We present a multi-temporal, multi-modal remote-sensing dataset for predicting how active wildfires will spread at a resolution of 24 hours. The dataset consists of 13.607 images across 607 fire events in the United States from January 2018 to October 2021. For each fire event, the dataset contains a full time series of daily observations, containing detected active fires and variables related to fuel, topography and weather conditions.<h2>Documentation</h2>WildfireSpreadTS_Documentation.pdf includes further details about the dataset, following Gebru et al.'s "Datasheets for Datasets" framework. This documentation is similar to the supplementary material of the associated NeurIPS paper, excluding only information about experimental setup and results. For full details, please refer to the associated paper. <h2>Code: Getting started</h2>Get started working with the dataset at <a href="https://github.com/SebastianGer/WildfireSpreadTS">https://github.com/SebastianGer/WildfireSpreadTS</a>. The code includes a PyTorch Dataset and Lightning DataModule to allow for easy access. We recommend converting the GeoTIFF files provided here to HDF5 files (bigger files, but much faster). The necessary code is also available in the repository. This work is funded by Digital Futures in the project EO-AI4GlobalChange. The computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at C3SE partially funded by the Swedish Research Council through grant agreement no. 2022-06725.

opencc-zeroOct 2023View details →

Growth traits of a tropical timber species at Southeast Asia, Shorea macrophylla, and scripts for genome wide association study and genomic prediction

Shorea macrophylla is a commercially important tropical tree species grown for timber and oil. It is amenable to plantation forestry due to its fast initial growth. Genomic selection (GS) has been used in tree breeding studies to shorten long breeding cycles but has not previously been applied to S. macrophylla. To build genomic prediction models for GS, leaves and growth trait data were collected from a half-sib progeny population of S. macrophylla in Sari Bumi Kusuma forest concession, central Kalimantan, Indonesia. 18037 SNP markers were identified in two ddRAD-seq libraries. Genomic prediction models based on these SNPs were then generated for breast height and total height in the 7th year from planting (D7 and H7). These traits were chosen because of their relatively high narrow-sense genomic heritability and because seven years was considered long enough to assess initial growth. Genomic prediction models were built using 12 methods with the full set of identified SNPs and subsets of 48, 96, and 192 SNPs selected based on the results of a genome-wide association study (GWAS). The GBLUP and RKHS methods gave the highest predictive ability (PA) for D7 and H7 and showed that D7 has an additive genetic architecture while H7 has an epistatic genetic architecture. LightGBM and CNN1D also achieved high PA for D7 with 48 and 96 selected SNPs, and for H7 with 96 and 192 selected SNPs, showing that gradient boosting decision trees and deep learning can be useful in genomic prediction. For almost all methods and both traits, PA was higher when SNPs were selected based on their GWAS P-values than when using the full set of SNPs. These results suggest that GS with GWAS-based SNP selection could be used in S. macrophylla breeding to improve initial growth and reduce genotyping costs for next generation seedlings.

Behavior-relevant top-down cross-modal predictions in mouse neocortex

Simultaneously recorded S1 and PPC neuronal population activity from awake mice during a texture discrimination task. Data acquired with two-photon calcium imaging.

A Two Level Neural Approach Combining Off-Chip Prediction with Adaptive Prefetch Filtering

To alleviate the performance and energy overheads of contemporary applications with large data footprints, we propose the Two Level Perceptron (TLP) predictor, a neural mechanism that effectively combines predicting whether an access will be off-chip with adaptive prefetch filtering at the first-level data cache (L1D). TLP is composed of two connected microarchitectural perception predictors, named First Level Predictor (FLP) and Second Level Predictor (SLP). FLP performs accurate off-chip prediction by using several program features based on virtual addresses and a novel selective delay component. The novelty of SLP relies on leveraging off-chip prediction to drive L1D prefetch filtering by using physical addresses and the FLP prediction as features. TLP constitutes the first hardware proposal targeting both off-chip prediction and prefetch filtering using a multi-level perception hardware approach. TLP only requires 7KB of storage. To demonstrate the benefits of TLP we compare its performance with state-of-the-art approaches using off-chip prediction and prefetch filtering on a wide range of single-core and multi-core workloads. Our experiments show that TLP reduces the average DRAM transactions by 30.7% and 17.7%, as compared to a baseline using state-of-the-art cache prefetchers but no off-chip prediction mechanism, across the single-core and multi-core workloads, respectively, while recent work significantly increases DRAM transactions. As a result, TLP achieves geometric mean performance speedups of 6.2% and 11.8% across single-core and multi-core workloads, respectively. In addition, our evaluation demonstrates that TLP is effective independently of the L1D prefetching logic.

A Two Level Neural Approach Combining Off-Chip Prediction with Adaptive Prefetch Filtering

A Two Level Neural Approach Combining Off-Chip Prediction with Adaptive Prefetch Filtering

opencc-by-4.0Dec 2022View details →

Dataset of the HazardNet: A Thermal Hazard Prediction Framework for Datacenters

This dataset entry showcases a comprehensive collection obtained from the Tier-0 supercomputer, Marconi A2, hosted at CINECA (<a href="https://www.hpc.cineca.it/">https://www.hpc.cineca.it/</a>). The dataset records inlet and outlet temperatures along with power consumption data from 3312 computing nodes, spanning from January 14, 2019, to December 31, 2019. The data is generated through ExaMon, a sophisticated monitoring datacenter infrastructure. The primary objective of this dataset is to support the research and development of HazardNet, an innovative thermal hazard prediction framework tailored specifically for datacenters. HazardNet integrates a comprehensive pipeline of machine-learning models. Researchers and enthusiasts interested in exploring our work further can find the complete set of codes and machine-learning models at our GitHub repository: <a href="https://github.com/MSKazemi/HazardNet">https://github.com/MSKazemi/HazardNet</a>

opencc-zeroNov 2023View details →

Male song structure predicts offspring recruitment to the breeding population in a migratory bird

Bird song is a classic example of a sexually selected trait, but much of the work relating individual song components to fitness has not accounted for song typically being composed of multiple, often-correlated components, necessitating a multivariate approach. We explored the role of sexual selection in shaping complex male song of house wrens (Troglodytes aedon) by simultaneously relating its multiple components to fitness using multivariate selection analysis, which is widely used in insect and anuran studies but not in birds. The analysis revealed significant variation in the form and strength of selection acting on song across different selection episodes, from nest-site defense to recruitment of offspring to the breeding population. Males that sang more song typically employed in close communication sired more offspring that were subsequently recruited to the breeding population than those that sang far-communication song. However, this relationship was not consistent across earlier selection episodes, as evidenced by non-linear selection acting on these song components in other contexts. Collectively, our results present a complex picture of multivariate selection on male song structure that would not be evident using univariate approaches and suggest possible trade-offs within and among song components at different points of the breeding season.

opencc-zeroNov 2023View details →

Explaining and predicting animal migration under global change

Many migratory species are declining due to global environmental change. Yet, their complex annual cycles make unravelling the impacts of potential drivers such as climate and land-use change on migrations a major challenge. Identifying where, when, and how threatening processes impact species' migratory journeys and population dynamics is crucial for identifying effective conservation actions. Here, we describe how a new migration modelling framework – Spatially-explicit Adaptive Migration Models (SAMMs) – can simulate the optimal behavioural decisions required to migrate across open land- or seascapes varying in character over space and time, without requiring predefined behavioural rules. Models of adaptive behaviour have been used widely in theoretical ecology but have great untapped potential in real-world contexts. Applying adaptive behaviour models across open environments will allow users to explore flexibility in how migratory strategies respond to environmental change and the consequences of migrants not being able to adapt to change. We outline how SAMMs can be used to model migratory journeys through aerial, terrestrial, and aquatic environments, demonstrating their potential using a case study on the common cuckoo (Cuculus canorus) and comparing modelled to observed behaviours. SAMMs offer a tool to identify the key threats faced by migratory species and to predict how they will adapt future migratory journeys in response to changing environmental conditions.

opencc-zeroNov 2023View details →

Data from: Spatiotemporal-social association predicts immunological similarity in rewilded mice

Environmental influences on immune phenotypes are well-documented, but our understanding of which elements of the environment affect immune systems, and how, remains vague. Behaviors, including socializing with others, are central to an individual's interaction with its environment. We therefore tracked behavior of rewilded laboratory mice of three inbred strains in outdoor enclosures and examined contributions of behavior, including associations measured from spatiotemporal cooccurrences, to immune phenotypes. We found extensive variation in individual and social behavior among and within mouse strains upon rewilding. And we found that the more associated two individuals were, the more similar their immune phenotypes were. Spatiotemporal association was particularly predictive of similar memory T and B cell profiles and was more influential than sibling relationships or shared infection status. These results highlight the importance of shared spatiotemporal activity patterns and/or social networks for immune phenotype and suggest potential immunological correlates of social life.

Plaque assay images from the article "Predicting phage-bacteria interactions at the strain level from genomes"

This dataset contains the plaque assay images for (i) the construction of the 403 bacteria * 96 phages interaction matrix and (ii) the "cocktails" experiment (100 E. coli strains challenged with recommended cocktails vs. a baseline).