This code is implements the nondominated sorting genetic algorithm (NSGA-II) in the R statistical programming language. The function is theoretically applicable to any number of objectives without modification. The function automatically detects the number of objectives from the population matrix used in the function call. NSGA-II has been applied in ARS research for automatic calibration of hydrolgic models (whittaker link) and economic optimization (whittaker link). Resources in this dataset:Resource Title: Non-dominated Sorting Genetic Algorithm-II. File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=393&modecode=20-72-05-00 download page

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e

**Introduction ** This case study will be based on Cyclistic, a bike sharing company in Chicago. I will perform tasks of a junior data analyst to answer business questions. I will do this by following a process that includes the following phases: ask, prepare, process, analyze, share and act.

Background Cyclistic is a bike sharing company that operates 5828 bikes within 692 docking stations. The company has been around since 2016 and separates itself from the competition due to the fact that they offer a variety of bike services including assistive options. Lily Moreno is the director of the marketing team and will be the person to receive these insights from this analysis.

Case Study and business task Lily Morenos perspective on how to generate more income by marketing Cyclistics services correctly includes converting casual riders (one day passes and/or pay per ride customers) into annual riders with a membership. Annual riders are more profitable than casual riders according to the finance analysts. She would rather see a campaign targeting casual riders into annual riders, instead of launching campaigns targeting new costumers. So her strategy as the manager of the marketing team is simply to maximize the amount of annual riders by converting casual riders.

In order to make a data driven decision, Moreno needs the following insights: - A better understanding of how casual riders and annual riders differ - Why would a casual rider become an annual one - How digital media can affect the marketing tactics

Moreno has directed me to the first question - how do casual riders and annual riders differ?

Stakeholders Lily Moreno, manager of the marketing team Cyclistic Marketing team Executive team

Data sources and organization Data used in this report is made available and is licensed by Motivate International Inc. Personal data is hidden to protect personal information. Data used is from the past 12 months (01/04/2021 – 31/03/2022) of bike share dataset.

By merging all 12 monthly bike share data provided, an extensive amount of data with 5,400,000 rows were returned and included in this analysis.

Data security and limitations: Personal information is secured and hidden to prevent unlawful use. Original files are backed up in folders and subfolders.

Tools and documentation of cleaning process The tools used for data verification and data cleaning are Microsoft Excel and R programming. The original files made accessible by Motivate International Inc. are backed up in their original format and in separate files.

Microsoft Excel is used to generally look through the dataset and get a overview of the content. I performed simple checks of the data by filtering, sorting, formatting and standardizing the data to make it easily mergeable.. In Excel, I also changed data type to have the right format, removed unnecessary data if its incomplete or incorrect, created new columns to subtract and reformat existing columns and deleting empty cells. These tasks are easily done in spreadsheets and provides an initial cleaning process of the data.

R will be used to perform queries of bigger datasets such as this one. R will also be used to create visualizations to answer the question at hand.

Limitations Microsoft Excel has a limitation of 1,048,576 rows while the data of the 12 months combined are over 5,500,000 rows. When combining the 12 months of data into one table/sheet, Excel is no longer efficient and I switched over to R programming.

Replication data and code for The Measurement of Partisan Sorting for 180 Million Voters by Jacob R Brown and Ryan D Enos, Nature Human Behavior 2021. Segregation across social groups is an enduring feature of nearly all human societies and is associated with numerous social maladies. In many countries, reports of growing geographic political polarization raise concerns about the stability of democratic governance. Here, using advances in spatial data computation, we measure individual partisan segregation by calculating the local residential segregation of every registered voter in the United States, creating a spatially weighted measure for more than 180 million individuals. With these data, we present evidence of extensive partisan segregation in the country. A large proportion of voters live with virtually no exposure to voters from the other party in their residential environment. Such high levels of partisan isolation can be found across a range of places and densities and are distinct from racial and ethnic segregation. Moreover, Democrats and Republicans living in the same city, or even the same neighbourhood, are segregated by party.

Contains data and R scripts for the JOP article, "Why Partisans Don't Sort: The Constraints on Political Segregation." When downloading tabular data files, ensure that they appear in your working directory in CSV format.

The Columbia Card Task (CCT) is a psychological test that measures cognitive functions related to executive functioning, such as planning, set shifting, decision-making, and inhibitory control. During the CCT, participants are presented with a deck of cards and are required to sort the cards based on different categories, with the rules for sorting changing over time.

Supplemental file list “A time-sorting pitfall trap and temperature datalogger for the sampling of surface-active arthropods.”

Parts List Epitfall_partsList.xslx – spreadsheet of parts needed for construction, prices, and vendors

CAD Epitfall_sampleWheel2d.dwg – 2D CAD file of sampling wheel Epitfall_sampleWheel2d.pdf - PDF version of 2D CAD file of sampling wheel Epitfall_sampleWheel3d.dwg - 3D CAD file of sampling wheel Epitfall_sampleWheel3d.stl – File for 3D printing of sampling wheel

Example Data TRAP2.TXT – example data created by pitfall trap TRAP5.TXT – example data created by pitfall trap community_matrix_EMPTY.csv – empty matrix with rows of sampled time intervals with unique sample ID codes. This file is generated by “Epitfall_dataPull.R” community_matrix_FULL.csv – same as above, but with ant identities and abundances entered.

Software Epitfall_24hourlySamples.ino – Arduino script to delay start time by 1 day, collect 24 hourly samples, with temperature measurements every 5 minutes during sample collection Epitfall_dataPull.R – R script to read data files from trap, create summaries of each sampling interval, and create an empty spreadsheet with rows of unique sample ID’s in which to enter arthropod abundance data.

Wiring Epitfall_wiringDiagram.fzz – Fritzing file of wiring schematic Epitfall_wiringDiagram.png – image of wiring schematic

Neural spiking activity was recorded from the auditory cortex of ferrets while animals engaged in an auditory detection task. Data was acquired using laminar silicon multi-electrode arrays acutely inserted into the auditory cortex region of interest (A1 or dPEG). Raw data was spike sorted using Kilosort2, followed by manual curation in Phy. For details on the spike sorting procedure or on the experimental set up in general, please refer to the associated eLife manuscript. For the purposes of sharing this data, we have included the post-spike sorted data for all electrophysiology experiments discretized into spike counts at 10 Hz and 50 Hz sampling, as these were the two views of the data we used to generate all analyses in the manuscript. These data were saved using the NEMS recording object format which can be easily loaded and manipulated in Python using the NEMS library (https://github.com/LBHB/NEMS). These recording objects contain additional information about the animal's behavior ...

Contains R scripts and data needed to reproduce the analyses found in Mummolo and Nall, "Why Partisans Don't Sort: The Constraints on Political Segregation." Read READ ME FIRST.rtf or READ ME FIRST.pdf for instructions on executing replication archive contents.

Coverage of Social Insurance Programs in Richest Quintile

Percent of Population Eligible

By data.world's Admin [source]

About this dataset

This dataset offers a unique insight into the coverage of social insurance programs for the wealthiest quintile of populations around the world. It reveals how many individuals in each country are receiving support from old age contributory pensions, disability benefits, and social security and health insurance benefits such as occupational injury benefits, paid sick leave, maternity leave, and more. This data provides an invaluable resource to understand the health and well-being of those most financially privileged in society – often having greater impact on decision making than other groups. With up-to-date figures from 2019-05-11 this dataset is invaluable in uncovering where there is work to be done for improved healthcare provision in each country across the world

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Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

• Understand the context: Before you begin analyzing this dataset, it is important to understand the information that it provides. Take some time to read the description of what is included in the dataset, including a clear understanding of the definitions and scope of coverage provided with each data point.

• Examine the data: Once you have a general understanding of this dataset's contents, take some time to explore its contents in more depth. What specific questions does this dataset help answer? What kind of insights does it provide? Are there any missing pieces?

• Clean & Prepare Data: After you've preliminarily examined its content, start preparing your data for further analysis and visualization. Clean up any formatting issues or irregularities present in your data set by correcting typos and eliminating unnecessary rows or columns before working with your chosen programming language (I prefer R for data manipulation tasks). Additionally, consider performing necessary transformations such as sorting or averaging values if appropriate for the findings you wish to draw from your analysis.

• Visualize Results: Once you've cleaned and prepared your data, use visualizations such as charts, graphs or tables to reveal patterns within it that support specific conclusions about how insurance coverage under social programs vary among different groups within society's quintiles - based on age groups etc.. This type of visualization allows those who aren't familiar with programming to process complex information quickly and accurately than when displayed numerically in tabular form only!

5 Final Analysis & Export Results: Finally export your visuals into presentation-ready formats (e.g., PDFs) which can be shared with colleagues! Additionally use these results as part of a narrative conclusion report providing an accurate assessment and meaningful interpretation about how social insurance programs vary between different members within society's quintiles (i..e., accordingest vs poorest), along with potential policy implications relevant for implementing effective strategies that improve access accordingly!

Research Ideas

• Analyzing the effectiveness of social insurance programs by comparing the coverage levels across different geographic areas or socio-economic groups;
• Estimating the economic impact of social insurance programs on local and national economies by tracking spending levels and revenues generated;
• Identifying potential problems with access to social insurance benefits, such as racial or gender disparities in benefit coverage

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: coverage-of-social-insurance-programs-in-richest-quintile-of-population-1.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit data.world's Admin.

Additional file 6: Table S4. Species observed in the metatranscriptome of wild birds (biological data set 2) and their abundance.

Rarely do we observe competitive exclusion within plant communities, even though plants compete for a limited pool of resources. Thus, our understanding of the mechanisms sustaining plant biodiversity might be limited. In this study, we explore two common ecological strategies, species sorting and character displacement, that promote coexistence by reducing competition. We assess the degree to which woody plants may implement these two strategies to lower belowground competition for nutrients which occurs via nutritional (mostly mycorrhizal) mutualisms. First, we compile data on plant traits and the mycorrhizal association state of woody angiosperms using a global inventory of indigenous flora. Our analysis reveals that species in locations with high mycorrhizal diversity exhibit distinct mean values in leaf area and wood density based on their mycorrhizal type, indicating species sorting. Second, we reanalyze a large dataset on leaf area to demonstrate that in areas with high mycorrhiz..., All analyses were carried out to the subset of Angiosperms that were classified as woody species. We obtained data on leaf Area, tree height, leaf mass per area (LMA), and seed mass trait values from the Global Spectrum of Plant Form and Function Dataset (DÃaz et al., 2022), and wood density information from the International Centre for Research on Agroforestry (World Agroforestry 2023). The selection of plant traits for our analysis was a compromise between feasibility, yielding information for divergent sets of plant species, and covering the three documented sets of autocorrelated traits: the leaf economics spectrum, the wood economics spectrum, and the root economics spectrum. Lists of indigenous species per location were extracted from The Global Naturalized Alien Flora database, a global inventory (van Kleunen et al., 2019). To assess leaf area variability across plant species' distribution ranges, we utilized the SLA dataset from Wright et al. (2017). Mycorrhizal association type..., , # Mechanisms of coexistence: exploring species sorting and character displacement in woody plants to alleviate belowground competition.

https://doi.org/10.5061/dryad.n2z34tn54

Most of the guidelines are available on the attached R script. All data are actually publically available and we derive information, whenever possible, from the original datasets. We included here only two files that required extensive processing which we could not integrate to the code. The two files contain multiple "null" values, which should be replaced by empty cells before running the code.

coords.submit.csv contains coordinates at a crude resolution for all the sites in the Naturalized Alien Flora database for whch we could do the matching.Â

mycorrhizaandwd.csv contains wood density and mycorrhizal type information that we extracted from the following two sources: World Agroforestry and Delavaux et al., 2019.

Description of the data and file structu...

The Digital Picking and Sorting Devices market is witnessing rapid growth as industries increasingly adopt automation to enhance efficiency and accuracy in logistics and supply chain management. These advanced technologies, which include barcode scanners, RFID systems, and automated sorting solutions, play a vital r

Cyrtandra (Gesneriaceae) is a genus of flowering plants with over 800 species distributed throughout Southeast Asia and the Pacific Islands. On the Hawaiian Islands, 60 named species and over 89 putative hybrids exist, most of which are identified on the basis of morphology. Despite many previous studies on the Hawaiian Cyrtandra lineage, questions regarding the reconciliation of morphology and genetics remain, many of which can be attributed to the relatively young age and evidence of hybridization between species. We utilized targeted enrichment, high-throughput sequencing, and modern phylogenomics tools to test 33 Hawaiian Cyrtandra samples for species relationships and hybridization in the presence of incomplete lineage sorting (ILS). Both concatenated and species-tree methods were used to reconstruct species relationships, and network analyses were conducted to test for hybridization. We expected to see high levels of ILS and putative hybrids intermediate to their parent species. Phylogenies reconstructed from the concatenated and species-tree methods were highly incongruent, most likely due to high levels of incomplete lineage sorting. Network analyses inferred gene flow within this lineage, but not always between taxa that we expected. Multiple hybridizations were inferred, but many were on deeper branches of the island lineages suggesting a long history of hybridization. We demonstrated the utility of high-throughput sequencing and a phylogenomic approach to understanding species relationships and gene flow in the presence of ILS.

Additional file 4: Table S2. Precision, recall and F1 scores obtained with the CCMetagen analysis with assembled sequence reads.

Protein-Protein, Genetic, and Chemical Interactions for Malik R (2010):Arrestin-2 interacts with the endosomal sorting complex required for transport machinery to modulate endosomal sorting of CXCR4. curated by BioGRID (https://thebiogrid.org); ABSTRACT: The chemokine receptor CXCR4, a G protein-coupled receptor, is targeted for lysosomal degradation via a ubiquitin-dependent mechanism that involves the endosomal sorting complex required for transport (ESCRT) machinery. We have reported recently that arrestin-2 also targets CXCR4 for lysosomal degradation; however, the molecular mechanisms by which this occurs remain poorly understood. Here, we show that arrestin-2 interacts with ESCRT-0, a protein complex that recognizes and sorts ubiquitinated cargo into the degradative pathway. Signal-transducing adaptor molecule (STAM)-1, but not related STAM-2, interacts directly with arrestin-2 and colocalizes with CXCR4 on early endosomal antigen 1-positive early endosomes. Depletion of STAM-1 by RNA interference and disruption of the arrestin-2/STAM-1 interaction accelerates agonist promoted degradation of CXCR4, suggesting that STAM-1 via its interaction with arrestin-2 negatively regulates CXCR4 endosomal sorting. Interestingly, disruption of this interaction blocks agonist promoted ubiquitination of hepatocyte growth factor-regulated tyrosine kinase substrate (HRS) but not CXCR4 and STAM-1 ubiquitination. Our data suggest a mechanism whereby arrestin-2 via its interaction with STAM-1 modulates CXCR4 sorting by regulating the ubiquitination status of HRS.

Datasets linked to publication "Revealing viral and cellular dynamics of HIV-1 at the single-cell level during early treatment periods", Otte et al 2023 published in Cell Reports Methods pre-ART (antiretroviral therapy) cryo-conserved and and whole blood specimen were sampled for HIV-1 virus reservoir determination in HIV-1 positive individuals from the Swiss HIV Study Cohort. Patients were monitored for proviral (DNA), poly-A transcripts (RNA), late protein translation (Gag and Envelope reactivation co-detection assay, GERDA) and intact viruses (golden standard: viral outgrowth assay, VOA). In this dataset we deposited the pipeline for the multidimensional data analysis of our newly established GERDA method, using DBScan and tSNE. For further comprehension NGS and Sanger sequencing data were attached as processed and raw data (GenBank).

Resubmitted to Cell Reports Methods (Jan-2023), accepted in principal (Mar-2023)

GERDA is a new detection method to decipher the HIV-1 cellular reservoir in blood (tissue or any other specimen). It integrates HIV-1 Gag and Env co-detection along with cellular surface markers to reveal 1) what cells still contain HIV-1 translation competent virus and 2) which marker the respective infected cells express. The phenotypic marker repertoire of the cells allow to make predictions on potential homing and to assess the HIV-1 (tissue) reservoir. All FACS data were acquired on a LSRFortessa BD FACS machine (markers: CCR7, CD45RA, CD28, CD4, CD25, PD1, IntegrinB7, CLA, HIV-1 Env, HIV-1 Gag) Raw FACS data (pre-gated CD4CD3+ T-cells) were arcsin transformed and dimensionally reduced using optsne. Data was further clustered using DBSCAN and either individual clusters were further analyzed for individual marker expression or expression profiles of all relevant clusters were analyzed by heatmaps. Sequences before/after therapy initiation and during viral outgrowth cultures were monitored for individuals P01-46 and P04-56 by Next-generation sequencing (NGS of HIV-1 Envelope V3 loop only) and by Sanger (single genome amplification, SGA)

data normalization code (by Julian Spagnuolo) FACS normalized data as CSV (XXX_arcsin.csv) OMIQ conText file (_OMIQ-context_XXX) arcsin normalized FACS data after optsne dimension reduction with OMIQ.ai as CSV file (XXXarcsin.csv.csv) R pipeline with codes (XXX_commented.R) P01_46-NGS and Sanger sequences P04_56-NGS and Sanger sequences

Additional file 2: Table S1. Precision, recall and F1 scores obtained for fungal communities.

With the increasing focus on patient-centred care, this study sought to understand priorities considered by patients and healthcare providers from their experience with head and neck cancer treatment, and to compare how patients’ priorities compare to healthcare providers’ priorities. Group concept mapping was used to actively identify priorities from participants (patients and healthcare providers) in two phases. In phase one, participants brainstormed statements reflecting considerations related to their experience with head and neck cancer treatment. In phase two, statements were sorted based on their similarity in theme and rated in terms of their priority. Multidimensional scaling and cluster analysis were performed to produce multidimensional maps to visualize the findings. Two-hundred fifty statements were generated by participants in the brainstorming phase, finalized to 94 statements that were included in phase two. From the sorting activity, a two-dimensional map with stress value of 0.2213 was generated, and eight clusters were created to encompass all statements. Timely care, education, and person-centred care were the highest rated priorities for patients and healthcare providers. Overall, there was a strong correlation between patient and healthcare providers’ ratings (r = 0.80). Our findings support the complexity of the treatment planning process in head and neck cancer, evident by the complex maps and highly interconnected statements related to the experience of treatment. Implications for improving the quality of care delivered and care experience of head and cancer are discussed.

Additional file 7: Table S5. Genome sequences and species used to simulate fungal communities.