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"en": "Bacteria evolved genes whose single-cell distributions of expression\r\n levels are broad, or even bimodal. Evidence suggests that they might\r\n enhance phenotypic diversity for coping with fluctuating environments. We\r\n identified seven genes in E. coli with bimodal (low and high) single-cell\r\n expression levels under standard growth conditions and studied how their\r\n dynamics are modified by environmental and antibiotic stresses known to\r\n target gene expression. We found that all genes lose bimodality\r\n under some, but not under all, stresses. Also, bimodality can reemerge\r\n upon cells returning to standard conditions, which suggests that the genes\r\n can switch often between high and low expression rates. As such, these\r\n genes could become valuable components of future multi-stable synthetic\r\n circuits. Next, we proposed models of bimodal transcription dynamics with\r\n realistic parameter values, able to mimic the outcome of the perturbations\r\n studied. We explored several models’ tunability and boundaries of\r\n parameter values, beyond which it shifts to unimodal dynamics. From the\r\n model results, we predict that bimodality is robust, and yet tunable, not\r\n only by RNA and protein degradation rates, but also by the fraction of\r\n time that promoters remain unavailable for new transcription events.\r\n Finally, we show evidence that, although the empirical expression levels\r\n are influenced by many factors, the bimodality emerges during\r\n transcription initiation, at the promoter regions and, thus, may be\r\n evolvable and adaptable.\r\n\r\n# Bimodality in E. coli gene expression: sources and robustness to\r\n genome-wide stresses\r\n [https://doi.org/10.5061/dryad.dz08kps5n](https://doi.org/10.5061/dryad.dz08kps5n) This dataset contains flow cytometry, microscopy and spectrophotometry data. It also contains the manual classification of bimodality. ## Description of the data and file structure #### - 01 Flow cytometry: This folder contains all flow cytometry data used in the project. **Note 1**: Unless stated otherwise, in the subfolders, there are 3 CSV files labelled \"Sample 1\", \"Sample 2\", and \"Sample 3\" that represent the measurements of 3 biological replicates in a given condition. **Note 2**: All CSV files in this folder contain fluorescence data in arbitrary units (AU) for the various flow cytometry detection channels. * 01 WT: Contains data measured by flow cytometry of MG1655 strain without fluorescent tags. * 01 Standard growth: Contains data measured in standard growth condition. * 02 Stationary growth: Contains data measured in stationary growth condition. * 03 Novobiocin: Contains data measured when cells are subjected to novobiocin. * 04 Cold shock: Contains data measured when cells are subjected to cold shock. * 05 Rifampicin: Contains data measured when cells are subjected to rifampicin. * 06 Streptomycin: Contains data measured when cells are subjected to streptomycin. * 02 YFP strains: Contains data measured by flow cytometry of YFP-tagged strains. * 01 Unimodal genes: Contains data regarding 49 strains classified as having unimodal gene expression distributions. * 01 Standard growth: Data measured in standard growth condition. Contains 49 folders, each one named after the gene tagged with YFP. * 02 Bimodal genes: Contains data regarding 7 strains classified as having bimodal gene expression distributions. * 01 Standard growth: Data measured in standard growth condition. Contains 7 folders, each one named after the gene tagged with YFP. * 02 Stationary growth: Data measured in stationary growth condition. Contains 7 folders, each one named after the gene tagged with YFP. * 03 Novobiocin: Data measured when cells are subjected to novobiocin. Contains 7 folders, each one named after the gene tagged with YFP. * 04 Cold shock: Data measured when cells are subjected to cold shock. Contains 7 folders, each one named after the gene tagged with YFP. * 05 Rifampicin: Data measured when cells are subjected to rifampicin. Contains 7 folders, each one named after the gene tagged with YFP. * 06 Streptomycin: Data measured when cells are subjected to streptomycin. Contains 7 folders, each one named after the gene tagged with YFP. * 07 Growth phase shifts: Data measured when cells are subjected to growth phase shifts for 3 generations. * 01 Generation 1: Data pertaining to the measurements of the first generation cells. * 01 Early exponential: Measurements made at the early exponential growth phase conditions. Contains 7 folders, each one named after the gene tagged with YFP. * 02 Exponential: Measurements made at the early exponential growth phase conditions. Contains 7 folders, each one named after the gene tagged with YFP. * 03 Early stationary: Measurements made at the early exponential growth phase conditions. Contains 7 folders, each one named after the gene tagged with YFP. * 04 Stationary: Measurements made at the early exponential growth phase conditions. Contains 7 folders, each one named after the gene tagged with YFP. * 02 Generation 2: Data pertaining to the measurements of the second generation cells. * 01 Early exponential: Measurements made at the early exponential growth phase conditions. Contains 7 folders, each one named after the gene tagged with YFP. * 02 Exponential: Measurements made at the early exponential growth phase conditions. Contains 7 folders, each one named after the gene tagged with YFP. * 03 Early stationary: Measurements made at the early exponential growth phase conditions. Contains 7 folders, each one named after the gene tagged with YFP. * 04 Stationary: Measurements made at the early exponential growth phase conditions. Contains 7 folders, each one named after the gene tagged with YFP. * 03 Generation 3: Data pertaining to the measurements of the third generation cells. * 01 Early exponential: Measurements made at the early exponential growth phase conditions. Contains 7 folders, each one named after the gene tagged with YFP. * 02 Exponential: Measurements made at the early exponential growth phase conditions. Contains 7 folders, each one named after the gene tagged with YFP. * 03 Early stationary: Measurements made at the early exponential growth phase conditions. Contains 7 folders, each one named after the gene tagged with YFP. * 04 Stationary: Measurements made at the early exponential growth phase conditions. Contains 7 folders, each one named after the gene tagged with YFP. * 03 GFP promoter fusions: Contains data measured by flow cytometry of cells with a low-copy plasmid with transcriptional fusions of GFP of promoters. * 01 Bimodal genes: Contains data regarding 6 strains classified as having bimodal gene expression distributions. * 01 Standard growth: Data measured in standard growth condition. Contains 6 folders, each one named after the gene whose promoter is tagged with GFP. #### - 02 Spectrophotometry: This folder contains all spectrophotometry data used in the project. * 01 Growth: This folder contains an Excel file (named \"OD600 data\") with optical density (OD600), a unitless absorbance measurement, for MG1655 under standard growth conditions. Growth is tracked over time, with time recorded in minutes (min). #### - 03 Microscopy: This folder contains all microscopy data used in the project. * 01 YFP strains: Contains data measured by flow cytometry of YFP-tagged strains. * 01 Unimodal genes: Contains data regarding 2 strains classified as having unimodal gene expression distributions. Contains 2 folders, each one named after the gene tagged with YFP. * 02 Bimodal genes: Contains data regarding the 7 strains classified as having bimodal gene expression distributions. Contains 7 folders, each one named after the gene tagged with YFP. **Note**: For both folders: In each gene folder, there are various folders, each for a panel. In each panel folder, there are confocal images of YFP tagged strain along with the correspondent phase contrast image. Confocal images have the prefix \"C2\" and the phase contrast images have the prefix \"C1\". #### - 04 Manual Classification: This folder contains an excel file (named \"Manual Classification Analysis\") with the results of bimodality classification of genes in standard growth conditions (made by 15 individuals)."
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A Cohort Study\"\r\n\r\nThis replication package contains all the Python code to conduct the data collection, preprocessing and analysis of this study.\r\n\r\nContents\r\n\r\nThis repository contains the following\r\n\r\n\r\n\r\n\r\n\r\nINSTALL: Detailed installation instructions for each of the used tools as well as the required Python dependencies.\r\n\r\n\r\n\r\n\r\nanalysis_figures: Figures generated during the analysis\r\n\r\n\r\n\r\nancova-assumption-visuals.pdf: Visualization assesing ANCOVA assumptions.\r\n\r\ncrude_analysis_normality_qq.pdf: QQ normality boxplots drom the crude analysis.\r\n\r\nendvelocity-boxplots.pdf: Boxplots displaying end velocity mean values distribution.\r\n\r\nexact-matching-output-boxplots.pdf: Boxplots displaying balance after exact matching.\r\n\r\nexact-matching-output-qq.pdf: QQ plots displaying balance after exact matching.\r\n\r\nvariables-boxplots.pdf: Boxplots displaying study variables.\r\n\r\nvariables-grouped-language.pdf: Variables displayed grouped by programming languages.\r\n\r\nvariables-grouped-year.pdf: Variables displayed grouped by creation year.\r\n\r\n\r\n\r\n\r\n\r\nArticle_figures: Figures added in the PDF version of the manuscript.\r\n\r\n\r\n\r\n[cdf-confounders](Article_figures/cdf-confounders.pdf): Cumulative Density Function plots for model confounders.\r\n\r\n[confounder-boxplots](Article_figures/confounder-boxplots.pdf): Boxplots for model confounders.\r\n\r\n[creation-year-confounder](Article_figures/creation-year-confounder.pdf): Balance histogram from the confounder \"Creation Year\"\r\n\r\n[dag-diagram](Article_figures/dag-diagram.pdf): Directional Acyclic graph diagram of the confounding relationship.\r\n\r\n[data-collection-vertical](Article_figures/data-collection-vertical.pdf): Data filtering stage diagram.\r\n\r\n[example-velocity](Article_figures/example-velocity.pdf): Graphical example of the velocity calculation.\r\n\r\n[kernel-density](Article_figures/kernel-density.pdf): Kernel density plots for model confounders.\r\n\r\n[stat-analysis-short](Article_figures/stat-analysis-short.pdf): Statistical analysis process diagram.\r\n\r\n[study-design-observed-project](Article_figures/study-design-observed-project.pdf): Augmented display at a project level of the follow-up period.\r\n\r\n[study-design](Article_figures/study-design.pdf): Graphical representation of the overall study design\r\n\r\n[velocity-boxplots](Article_figures/velocity-boxplots.pdf): Boxplots of the start and end velocity.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ncodes:\r\n\r\n\r\n\r\nanalyze_repo_multi_thread_per_commit.py: Performs the monitorization of MS usage.\r\n\r\nclone_projects.py: Performs the repostory clonning.\r\n\r\ncommons.py: Stores the global paths and variables.\r\n\r\nfind_ms_usage.py: Checks whether the MS usage doesn't respect the criteria during the follow-up period.\r\n\r\nformat_data_for_analysis.py: Making the initial raw data accessible in R.\r\n\r\nget_confounders.py: Mines the version control history dat from the studied projects.\r\n\r\nget_velocity.py: Calculates velocity from the studied software projects' maintenance activity.\r\n\r\nmain.py: As will be explained, coordinates the execution of the creation of the raw data doe this study.\r\n\r\nmodules.py: Common used functions during the project execution.\r\n\r\ntrendAnalysis.py: Tests the trend of commit and issue maintenance activity of the studied projects.\r\n\r\ntsanalysis.py: COntains functions of Time Series Analysis models to assist the testing in the trendAnalysis.py script.\r\n\r\nbasic_statistics_table.py: Creates the basic statistics table of the studied projects, included in this online appendix.\r\n\r\nconfounder-matching.R: Performs different confounder matching techniques.\r\n\r\ncrude-analysis.R: Functions to perform the crude analysis.\r\n\r\ndata-transformation.R: Functions to perform the data transformation to reach normality.\r\n\r\ndescriptive-analysis.R: Functions to perform the descriptive statistics.\r\n\r\npower-analysis.R: Functions to perform the statistical power analysis.\r\n\r\nstat-analysis-long.R: Statistical Adjustmed analysis with graphical representations.\r\n\r\nstat-analysis.R: Straight numerical calculations of the Statistical Adjusted analysis.\r\n\r\n\r\n\r\n\r\n\r\ndata: Contains all the required data to start, follow and finish the analysis of this study.\r\n\r\n\r\n\r\n\r\n\r\ncases: All the data related to the subjects within the cases group.\r\n\r\n\r\n\r\n\r\ncontrols: All the data related to the subjects within the controls group.\r\n\r\n\r\n\r\n\r\nanalyzedDataset.zip: Initial MS datasets\r\n\r\n\r\n\r\n\r\nbasic_statistics_table.pdf: PDF file with the basic statistics of the projects considered in the statistical analysis of this study.\r\n\r\n\r\n\r\n\r\nbasic_statistics_table_extended.csv: Same version of the PDF table in CSV format.\r\n\r\n\r\n\r\n\r\nfinal_data_file.csv: Final data file with the raw data used in the statistical analysis section.\r\n\r\n\r\n\r\n\r\nms_monitorization_json.json: JSON file with the results from the MS usage monitorization.\r\n\r\n\r\n\r\n\r\nprojects.zip: Overall folder with the MS and Non-MS projects zipped.\r\n\r\n\r\n\r\n\r\nvelocity_results.csv: Results from the velocity calculation stage..\r\n\r\n\r\n\r\nSubsequently during the code execution new data directories and files will be generated.\r\n\r\nThe source of the data is rooted on the global paths defined in commons.py.\r\n\r\n\r\n\r\nrequirements.txt: Python dependencies to be installed.\r\n\r\ngithub-token.txt: Insert the GitHub token as one single line in this TXT file.\r\n\r\n\r\n\r\n\r\nRunning the code\r\n\r\nNOTE 1: Please, find the DATA_PATH global variable in the commons.pyscript and define the path where the program should create all the needed results.\r\n\r\nNOTE 2: The different stages of the study execution are splitted in the main.py script, from the boolean definitions incommons.py practitioners can decide which stages want to be manipulated or re-executed again without affecting the other stages.For a complete execution, set all the boolean global variables to True\r\n\r\nThe main.py file is not divided into the exclusion criteria stages but by stages of the code which required diverse logics. In the following points we briefly explain the diverse stages in the execution, and the reader can observe the involved scripts within the code.\r\n\r\nPython codes\r\n\r\nStage 1: COMPLETE ANALYSIS\r\n\r\nPerforms the initial prefiltering of projects existing in the Microservices dataset replication package. During the process, collects commits and issues,whose trend is analyzed in the same stage as explained in the published article.\r\n\r\nStage 2: MS ANALYSIS\r\n\r\nFor the remaining Microservices projects a separated analysis of their architecture is performed. Shortly,all the commits from the default branch are analyzed based on the criteria used in the Microservices dataset to define if the selected projectsmaintain the required minimum number of microservices during the two years of observational study.\r\n\r\nStage 3: CLONE PROJECTS\r\n\r\nFrom the surviving projects, the cloning of the projects is performed in order to collect further variables from their repositories.\r\n\r\nStage 4: REPO ANALYSIS\r\n\r\nBased on the metrics collected from the cloned repositories further exclusions are performed.\r\n\r\nStage 5: FINAL FILE CREATION\r\n\r\nAfter all the exclusion criteria is performed the final csv file is generated to be used in R. Each of the scripts is called in the name of the stage title shown in these instructions. Furthermore, the used libraries are located at the top of each script so that the reader can activate them directly before executing new lines of code. If the library package is not installed previously a warning might appear, often the same IDE alerts of this before running the line of code for the library activation.\r\n\r\nR codes\r\n\r\nStage 6: CRUDE ANALYSIS\r\n\r\nPerforms the crude analysis in its two formats. Note that for the crude analysis with transformed data the results from the data transformation must be computed beforehand.\r\n\r\nStage 7: MATCHING CONFOUNDERS\r\n\r\nThe matching of confounders is done following the Exact Matching style. At the end of the script (as well as in the other scripts) new datafiles (dataframes are generated)\r\n\r\nStage 8: VARIABLE TRANSFORMATION\r\n\r\nDifferent techniques are used for the variable trandformation. These techniques are used based on the skewed nature of the dependent variable at the beginning. Similarly, only the results from the cube root transformation are taken into account.Therefore, the user is free to choose if running all the script or just the section regarding te cube root transformation.\r\n\r\nStage 9: STATISTICAL ANALYSIS\r\n\r\nR codes:\r\n\r\nStage 10: STATISTICAL ANALYSIS\r\n\r\nNOTE: The ANCOVA and Linear Regression analysis are computed.\r\n\r\nFor further questions, please do not hesitate to contact the authors once the contact details are facilitated in the paper publication."
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In Tampere, part of the data (n=31 interviews) was collected in collaboration with the EKOS research project (this part of the data is described and archived in the Finnish Social Science Data Archive, DoI: https://doi.org/10.60686/t-fsd3816) The second part of the data was collected in Sweden. The data collection sites there were the neighborhoods of Möllevången and Nydala in Malmö, Farsta and Rågsved in Stockholm, and Fröslunda and Årby in Eskilstuna. The French data were collected in the La Plaine area in Marseille; in La-Chapelle-Saint-Luc, Saint-Andre-Les-Vergers and Les Chartreux in Troyes; and in Guillotière in Lyon.\r\n\r\nAcross these sites shared methods were used in data collection, consisting of thematic interviews, walking interviews, and observations. The dataset emphasizes the diversity of experiences and the manifestations of distinctions in diverse urban environments and examines the ways in which people form bonds in relation to each other, their neighborhoods, and the broader society.\r\n\r\nThe first set of participants were located through social media groups (Facebook), from the premises of associations organizing community activities in the areas, libraries, cafes, community events, and youth centers. After this, snowball sampling was used, in addition to which targeted recruitment was applied if a population group represented in the area was completely missing from the dataset. Ethnographic observations were conducted in public spaces, community centres, cafés, stations, and shopping centres that were selected as potentially interesting places based on extant scholarship on living with difference and urban encounters. Here, attention was paid at how people used these sites, who were there and who were absent, as well as how people moved in and across the sites. Notes were made of what kinds of encounters, patterns of behaviour, cooperations, and conflicts occurred. These observations were made at various times of the day, to capture potential temporal changes. This resulted in a rich collection of fieldnotes, sketches, photographs, and movement maps. \r\n\r\nRelevant files: 1) EmergentCommunity ethnographic matrix.pdf, 2) EmergentCommunityEthno interview questions.docx, 3) EmergentCommunity_metadata public.xlsx (contains all metadata from the project), 4) EmergentCommunityEthno_metadata.csv (contains metadata only on desk research, ethnographic interviews and fieldnotes). \r\n\r\nEmergentCommunityVR (dataset 2):\r\n\r\nData collection was conducted in Helsinki, Marseille, and Malmö. The data was collected using 360-degree videos based on the aforementioned ethnographic data as stimuli to which participants were exposed. A separate video was created for each city, using specifically the data collected therein. We put together a mobile laboratory set-up that travelled to each city and collaborated with local NGOs whose premises were used as our laboratory space. The equipment and software used are explained in the document \"EmergentCommunity mobile laboratory.pdf\".\r\n\r\nThe inclusion criteria for participation were: being a major, healthy, not having hearing or vision impairments, being a resident in the city that the video depicted, and knowledge of the local language in which the video was executed. During the viewing of the video stimulus, participants' physiological responses were measured and their eye movements were tracked. VR eye tracking was used as it enables the precise analysis of gaze behaviour – such as fixations and saccades – within immersive, ecologically valid environments. Regarding physiological signals, the focus was on the electrical activity of the heart using electrocardiography (ECG), the electrical activity of the facial muscles using facial electromyography (fEMG), and the electrical conductivity of the skin using galvanic skin response (GSR). To complement the physiological data, a multimodal setup was established to assess the affective content of the stimulus in terms of arousal/valence, avoidance/approach, and unpredictability. After viewing, the participants were asked to evaluate the intensity of their emotional experience and to name the emotional reactions elicited by the video using a questionnaire carried out with Gorilla Experiment Builder. The questionnaire also contained background questions, from basic participant information, such as age and gender, to aspects that relate to diversity and inequality in contemporary societies: language, income, housing, education, political activity, participation, as well as political opinions and social values. After completing the measurements and the questionnaire, participants were interviewed about their experience and the thoughts it provoked, and they were asked to share information regarding their daily lives.\r\n\r\nThe purpose of the dataset was to help understand the formation of emotional experiences and the significance and functioning of emotions in the everyday life of increasingly diverse and unequal cities. The call for participation was distributed in several thematic Facebook groups (related to e.g., urban development, multiculturalism, neighborhood, local NGOs and minority communities) and via Instagram, as well as through flyers/posters in libraries, local associations, shopping centers, cafes, and on the project's Facebook page and Instagram profile. In the case of Marseille and Malmö, local assistants were used to spread the invitation within their networks and distribute participation invitation leaflets on the streets. In each city, it was possible for already registered participants to invite additional participants as well. Overall, the goal was to ensure the representativeness of the data in terms of age, gender, and minority status. \r\n\r\nRelevant files: 1) EmergentCommunity video stimuli.pdf, 2) EmergentCommunityVR interview questions.pdf, 3) EmergentCommunityVR Gorilla questionnaires.pdf, 4) EmergentCommunity mobile laboratory.pdf, 5) EmergentCommunity_metadata public.xlsx (contains all metadata from the project), 6) EmergentCommunityVR interviews.csv (contains metadata on interviews done after watching the 360-degree video), 7) EmergentCommunityVR physio.csv (contains metadata on physiological measuring and questionnaires). \r\n\r\nPurpose of the data\r\n\r\nThe EmergentCommunity project aimed at producing knowledge about what community means and how it is formed in increasingly diverse societies, as well as the conflicts and tensions that everyday life brings out. The project empirically examined the concrete challenges that societal changes produce for cities and coexistence. The aim was to identify how peaceful coexistence could be supported and population relations promoted in urban everyday life. The project emphasized that community relations and everyday coexistence are affective, social, and spatial phenomena, which is why a wide range of research methods from ethnography and observation to psychophysiological measurements and interviews were applied. These approaches were brought into dialogue through virtual reality by utilizing ethnography-based 360-degree videos depicting everyday life in the latter part of the project (EmergentCommunityVR). Thus, the project created new understanding of emotions formed in everyday life and produced unique knowledge in the fields of psychological and sociological emotion research. Bringing these areas together enabled a critical examination of the concept of community and the identification of the practices and ways in which communities are produced in the everyday life of diverse and unequal cities (see CORDIS database for public description, results, and reporting).\r\n\r\nThroughout the data collection, the research focused on everyday life and the forms, practices, and interpretations of everyday coexistence in public urban spaces in the selected research neighbourhoods. Participants were also asked to share their experiences, interpretations, and views on societal change and how the change has been visible in their own neighborhoods and what thoughts and feelings it evokes in them. The data was formed through non-probability sampling (self-formed sample).\r\n\r\nThe research sites were selected by examining statistics, policy reports, and available data on demographic changes and diversity, income inequality, trends of residential and ethnic segregation in different countries and cities (desk research). We chose the countries and cities so that they would complement each other and that changes were observable in each selected context, although their forms, emphases, and manifestations might vary. After this extensive background review, we focused on the city level, complementing the available statistical data with news articles and reports and analyses related to urban areas and development. This allowed us to identify pockets of diversity and inequality within each city. Finally, study neighborhoods were selected based on them having undergone urban development projects, being targeted with anti-segregation measures, their residents' socio-economic backgrounds being diverse, and several dimensions of inequality (regarding education level, employment rate, income level) being present in the areas.\r\n\r\nThe total size of the data\r\n\r\nThe dataset consists of desk research data made up of 960 files (reports, news articles, anonymized social media discussions), amounting to 4Gb. The data were used to identify the research sites and set the collected data within the broader socio-political context.\r\n\r\nEmergentCommunityEthno data consists of 142 recorded interviews with 185 people that amount to 118 hours of talk, and 2214 pages of transcribed material (+ 31 interviews collected with the EKOS project, archived separately in FSD, ). In addition to the interviews, the data includes 338 pages of fieldnotes, 1974 photographs/videos, and 63 sketches/maps. \r\n\r\nEmergentCommunityVR data includes 3 ethnography-based 360-degree video stimuli (length approximately 7:30 minutes/video) and 198 recorded interviews with 200 participants amounting to 116 hours of talk, 2017 pages of transcribed data. The size of the physiological and eye-tracking data is 294 Gb, and ~30Mb for the questionnaire answers; this data was collected from 197 participants."
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