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

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

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.

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...

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

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.

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.

This is the carbon monoxide data. Each sheet (tab) is formatted to be exported as a .csv for use with the R-code (AQ-June20.R). In order for this code to work properly, it is important that this file remain intact. Do not change the column names or codes for data, for example. And to be safe, don’t even sort. Just in case. One simple change in the excel file could make the code full of bugs.

Additional file 5: Table S3. Species and transcripts observed in the metatranscriptome of a mock fungal community (biological data set 1).

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

Includes both the merged Canadian Election Study dataset and the R script used for all analyses in the paper entitled "Growing Apart?: Partisan Sorting in Canada, 1992-2015,” authored by Anthony Kevins and Stuart Soroka, and published in the Canadian Journal of Political Science 51(1): 103-133.

Diatoms are diverse and widespread freshwater Eukaryotes that make excellent microbial subjects for addressing questions in metacommunity ecology. In the McMurdo Dry Valleys of Antarctica, the simple trophic structure of glacier-fed streams provides an ideal outdoor laboratory where well-described diatom assemblages are found within two cyanobacterial mat types, which occupy different habitats and vary in coverage within and among streams. Specifically, black mats of Nostoc spp. occur in marginal wetted habitats, and orange mats (Oscillatoria spp. and Phormidium spp.) occur in areas of consistent stream flow. Despite their importance as bioindicators for changing environmental conditions, the role of dispersal in structuring dry valley diatom metacommunities remains unclear. Here, we use MCSim, a spatially explicit metacommunity simulation package for R, to test alternative hypotheses about the roles of dispersal and species sorting in maintaining the biodiversity of diatom assemblages residing in black and orange mats. The spatial distribution and patchiness of cyanobacterial mat habitats was characterized by remote imagery of the Lake Fryxell sub-catchment in Taylor Valley. The available species pool for diatom metacommunity simulation scenarios was informed by the Antarctic Freshwater Diatoms Database, maintained by the McMurdo Dry Valleys Long Term Ecological Research program. We used simulation outcomes to test the plausibility of alternative community assembly hypotheses to explain empirically observed patterns of freshwater diatom biodiversity in the long-term record. The most plausible simulation scenarios suggest species sorting by environmental filters, alone, was not sufficient to maintain biodiversity in the Fryxell Basin diatom metacommunity. The most plausible scenarios included either (1) neutral models with different immigration rates for diatoms in orange and black mats or (2) species sorting by a relatively weak environmental filter, such that dispersal dynamics also influenced diatom community assembly, but there was not such a strong disparity in immigration rates between mat types. The results point to the importance of dispersal for understanding current and future biodiversity patterns for diatoms in this ecosystem, and more generally, provide further evidence that metacommunity theory is a useful framework for testing hypotheses about microbial community assembly.

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

This is the gravimetric data used to calibrate the real time readings. Each sheet (tab) is formatted to be exported as a .csv for use with the R-code (AQ-June20.R). In order for this code to work properly, it is important that this file remain intact. Do not change the column names or codes for data, for example. And to be safe, don’t even sort. One simple change in the excel file could make the code full of bugs.

Release Date: 2014-12-09...Table Name. Geographic Area Series: Shipment Characteristics by Commodity by Mode by Shipment Weight for the United States: 2012 ....ReleaseSchedule. The data in this file are scheduled for release in December 2014.....Key TableInformation.None.....Universe. The 2012 Commodity Flow Survey (CFS) covers business establishments with paid employees that are located in the United States and are classified using the 2007 North American Industry Classification System (NAICS) in mining, manufacturing, wholesale trade, and selected retail trade and services industries, namely, electronic shopping and mail-order houses, fuel dealers, and publishers. Establishments classified in transportation, construction, and all other retail and services industries are excluded from the survey. Farms, fisheries, foreign establishments, and most government-owned establishments are also excluded.The survey also covers auxiliary establishments (i.e., warehouses and managing offices) of multi-establishments companies..For the 2012 CFS, an advance survey (pre-canvass) of approximately 100,000 establishments was conducted to identify establishments with shipping activity and to. try and obtain an accurate measure of their shipping activity. Surveyed establishments that indicated undertaking shipping activities and the non-respondents to the .pre-canvass were included in the CFS sample universe....GeographyCoverage. The data are shown at the U.S level.....IndustryCoverage.None.....Data ItemsandOtherIdentifyingRecords. This file contains data on:..Value ($ Millions).Tons (Thousands).Ton-miles (Millions).Average miles per shipment (number).Coefficient of variation or standard error for all above data items. .The data are shown by commodity code (COMM), mode of transportation (DMODE), and shipment weight (SHIPWT)......Sort Order.Data are presented in ascending geography (GEO_ID) by COMM by DMODE by SHIPWT sequence.....FTP Download. Download the entire table at Table 12 FTP. ....ContactInformation.U.S. Census Bureau.Commodity Flow Survey.Tel: (301)763-2108.Email: erd.cfs@census.gov...The estimates presented are based on data from the 2012 Commodity Flow Surveys (CFS) and supersede data previously released in the 2012 CFS Preliminary Report. These estimates only cover businesses with paid employees. All dollar values are expressed in current dollars, i.e., they are based on price levels in effect at the time of the sample. Estimates may not be additive due to rounding. ...For information on Commodity Flow Survey geographies, including changes for 2012, see Census Geographies. .Symbols:.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page..Z - Rounds to Zero..X - Not Applicable..For a complete list of all economic programs symbols, see the Symbols Glossary..Source: U.S. Department of Transportation, Bureau of Transportation Statistics and U.S. Census Bureau, 2012 Commodity Flow Survey. .Note: The noise infusion data protection method has been applied to prevent data disclosure, and to protect respondent's confidentiality. Estimates are based on a sample of establishments and are subject to both sampling and nonsampling error. Estimated measures of sampling variability are provided in the tables. For information on confidentiality protection, sampling error, and nonsampling error see Survey Methodology..Commodity Code changes for 2012 CFS.. (CFS10) 07-R - Prior to the 2012 CFS, oils and fats treated for use as biodiesel were included in Commodity Code 07. In the 2012 CFS, oils and fats treated for use as biodiesel moved to Commodity Code 18. . (CFS20) 08-R - Prior to the 2012 CFS, alcohols intended for use as fuel such as ethanol, although not specifically identified, were included in Commodity Code 08. In the 2012 CFS, ethanol moved to Commodity Code 17. . (CFS30) 17-R - Prior to the 2012 CFS, fuel alcohols such as ethanol were included in Commodity Code 08, although not specifically identified. Also, kerosene was included in Commodity Code 19. In the 2012 CFS, ethanol, fuel alcohols and kerosene moved to Commodity Code 17. . (CFS40) 18-R - Prior to the 2012 CFS, biodiesel, although not specifically identified, was included in Commodity Code 07. In the 2012 CFS, biodiesel moved to Commodity Code 18.

Methane concentration of biogas. Each sheet (tab) is formatted to be exported as a .csv for use with the R-code (AQ-June20.R). In order for this code to work properly, it is important that this file remain intact. Do not change the column names or codes for data, for example. And to be safe, don’t even sort. Just in case. One simple change in the excel file could make the code full of bugs.

Spatial and temporal heterogeneity within landscapes influences the distribution and phenotypic diversity of individuals both within and across populations. Phenotype-habitat correlations arise either through phenotypes within an environment altering through the process of natural selection or plasticity, or phenotypes remaining constant but individuals altering their distribution across environments. The mechanisms of non-random movement and phenotype-dependent habitat choice may account for associations within highly heterogeneous systems, such as streams, where local adaptation may be negated, plasticity too costly and movement is particularly important. Despite growing attention, however, few empirical tests have yet to be conducted. Here we provide a test of phenotype-dependent habitat choice and ask: 1) if individuals collected from a single habitat type continue to select original habitat; 2) if decisions are phenotype-dependent and functionally related to habitat requirements; and 3) if phenotypic-sorting continues despite increasing population density. To do so we both conducted experimental trials manipulating the density of four stream-fish species collected from either a single riffle or pool and developed a game-theoretical model exploring the influence of individuals' growth rate, sampling and competitive abilities as well as interference on distribution across two habitats as a function of density. Our experimental trials show individuals selecting original versus alternative habitats differed in their morphologies, that morphologies were functionally related to habitat-type swimming demands, and that phenotypic-sorting remained significant (although decreased) as density increased. According to our model this only occurs when phenotypes have contrasting habitat preferences and only one phenotype disperses (i.e. selects alternatives) in response to density pressures. This supports our explanation that empirical habitat selection was due to a combination of collecting a fraction of mobile individuals with different habitat preferences and the exclusion of individuals via scramble competition at increased densities. Phenotype-dependent habitat choice can thereby account for observed patterns of natural stream-fish distribution.