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

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.

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.

Phase 1: ASK

Key Objectives:

1. Business Task * Cyclist is looking to increase their earnings, and wants to know if creating a social media campaign can influence "Casual" users to become "Annual" members.

2. Key Stakeholders: * The main stakeholder from Cyclist is Lily Moreno, whom is the Director of Marketing and responsible for the development of campaigns and initiatives to promote their bike-share program. The other teams involved with this project will be Marketing & Analytics, and the Executive Team.

3. Business Task: * Comparing the two kinds of users and defining how they use the platform, what variables they have in common, what variables are different, and how can they get Casual users to become Annual members

Phase 2: PREPARE:

Key Objectives:

1. Determine Data Credibility * Cyclist provided data from years 2013-2021 (through March 2021), all of which is first-hand data collected by the company.

2. Sort & Filter Data: * The stakeholders want to know how the current users are using their service, so I am focusing on using the data from 2020-2021 since this is the most relevant period of time to answer the business task.

#Installing packages
install.packages("tidyverse", repos = "http://cran.us.r-project.org")
install.packages("readr", repos = "http://cran.us.r-project.org")
install.packages("janitor", repos = "http://cran.us.r-project.org")
install.packages("geosphere", repos = "http://cran.us.r-project.org")
install.packages("gridExtra", repos = "http://cran.us.r-project.org")

library(tidyverse)
library(readr)
library(janitor)
library(geosphere)
library(gridExtra)

#Importing data & verifying the information within the dataset
all_tripdata_clean <- read.csv("/Data Projects/cyclist/cyclist_data_cleaned.csv")

glimpse(all_tripdata_clean)

summary(all_tripdata_clean)

Phase 3: PROCESS

Key Objectives:

1. Cleaning Data & Preparing for Analysis: * Once the data has been placed into one dataset, and checked for errors, we began cleaning the data. * Eliminating data that correlates to the company servicing the bikes, and any ride with a traveled distance of zero. * New columns will be added to assist in the analysis, and to provide accurate assessments of whom is using the bikes.

#Eliminating any data that represents the company performing maintenance, and trips without any measureable distance
all_tripdata_clean <- all_tripdata_clean[!(all_tripdata_clean$start_station_name == "HQ QR" | all_tripdata_clean$ride_length<0),] 

#Creating columns for the individual date components (days_of_week should be run last)
all_tripdata_clean$day_of_week <- format(as.Date(all_tripdata_clean$date), "%A")
all_tripdata_clean$date <- as.Date(all_tripdata_clean$started_at)
all_tripdata_clean$day <- format(as.Date(all_tripdata_clean$date), "%d")
all_tripdata_clean$month <- format(as.Date(all_tripdata_clean$date), "%m")
all_tripdata_clean$year <- format(as.Date(all_tripdata_clean$date), "%Y")

** Now I will begin calculating the length of rides being taken, distance traveled, and the mean amount of time & distance.**

#Calculating the ride length in miles & minutes
all_tripdata_clean$ride_length <- difftime(all_tripdata_clean$ended_at,all_tripdata_clean$started_at,units = "mins")

all_tripdata_clean$ride_distance <- distGeo(matrix(c(all_tripdata_clean$start_lng, all_tripdata_clean$start_lat), ncol = 2), matrix(c(all_tripdata_clean$end_lng, all_tripdata_clean$end_lat), ncol = 2))
all_tripdata_clean$ride_distance = all_tripdata_clean$ride_distance/1609.34 #converting to miles

#Calculating the mean time and distance based on the user groups
userType_means <- all_tripdata_clean %>% group_by(member_casual) %>% summarise(mean_time = mean(ride_length))


userType_means <- all_tripdata_clean %>% 
 group_by(member_casual) %>% 
 summarise(mean_time = mean(ride_length),mean_distance = mean(ride_distance))

Adding in calculations that will differentiate between bike types and which type of user is using each specific bike type.

#Calculations

with_bike_type <- all_tripdata_clean %>% filter(rideable_type=="classic_bike" | rideable_type=="electric_bike")

with_bike_type %>%
 mutate(weekday = wday(started_at, label = TRUE)) %>% 
 group_by(member_casual,rideable_type,weekday) %>%
 summarise(totals=n(), .groups="drop") %>%
 
with_bike_type %>%
 group_by(member_casual,rideable_type) %>%
 summarise(totals=n(), .groups="drop") %>%

 #Calculating the ride differential
 
 all_tripdata_clean %>% 
 mutate(weekday = wkday(started_at, label = TRUE)) %>% 
 group_by(member_casual, weekday) %>% 
 summarise(number_of_rides = n()
      ,average_duration = mean(ride_length),.groups = 'drop') %>% 
 arrange(me...

This dataset is updated weekly. Please ensure that you use the most up-to-date version.###\r

\r The Australian Charities and Not-for-profits Commission (ACNC) is Australia’s national regulator of charities.\r \r Since 3 December 2012, charities wanting to access Commonwealth charity tax concessions (and other benefits), need to register with the ACNC. Although many charities choose to register, registration with the ACNC is voluntary.\r \r Each year, registered charities are required to lodge an Annual Information Statement (AIS) with the ACNC. Charities are required to submit their AIS within six months of the end of their reporting period.\r \r Registered charities can apply to the ACNC to have some or all of the information they provide withheld from the ACNC Register. However, there are only limited circumstances when the ACNC can agree to withhold information. If a charity has applied to have their data withheld, the AIS data relating to that charity has been excluded from this dataset.\r \r This dataset can be used to find the AIS information lodged by multiple charities. It can also be used to filter and sort by different variables across all AIS information.\r \r This dataset can be used to find the AIS information lodged by multiple charities. It can also be used to filter and sort by different variables across all AIS information. AIS Information for individual charities can be viewed via the ACNC Charity Register.\r \r The AIS collects information about charity finances, and financial information provides a basis for understanding the charity and its activities in greater detail. \r We have published explanatory notes to help you understand this dataset.\r \r When comparing charities’ financial information it is important to consider each charity's unique situation. This is particularly true for small charities, which are not compelled to provide financial reports – reports that often contain more details about their financial position and activities – as part of their AIS.\r \r For more information on interpreting financial information, please refer to the ACNC website.\r \r The ACNC also publishes other datasets on data.gov.au as part of our commitment to open data and transparent regulation. Please click here to view them.\r \r NOTE: It is possible that some information in this dataset might be subject to a future request from a charity to have their information withheld. If this occurs, this information will still appear in the dataset until the next update.\r \r Please consider this risk when using this dataset.

Some vertebrates demonstrate complex numerosity concepts—including addition, sequential ordering of numbers, or even the concept of zero—but whether an insect can develop an understanding for such concepts remains unknown. We trained individual honey bees to the numerical concepts of "greater than" or "less than" using stimuli containing one to six elemental features. Bees could subsequently extrapolate the concept of less than to order zero numerosity at the lower end of the numerical continuum. Bees demonstrated an understanding that parallels animals such as the African grey parrot, nonhuman primates, and even preschool children.

CitiesGOER is a database that provides environmental data for 52,602 cities and 48 environmental variables, including 38 bioclimatic variables, 8 soil variables and 2 topographic variables. Data were extracted from the same 30 arc-seconds global grid layers that were prepared when making the TreeGOER (Tree Globally Observed Environmental Ranges) database that is available from https://doi.org/10.5281/zenodo.7922927. Details on the preparations of these layers are provided by Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology 29: 6303–6318. https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914. CitiesGOER was designed to be used together with TreeGOER and possibly also with the GlobalUsefulNativeTrees database (Kindt et al. 2023) to allow users to filter suitable tree species based on environmental conditions of the planting site.

The identities and coordinates of cities were sourced from a data set with information for cities with a population size larger than 1000 that was created by Opendatasoft and made available from https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/table/?disjunctive.cou_name_en&sort=name. The data was downloaded on 22-JULY-2023 and afterwards filtered for cities with a population of 5000 or above. Cities where information on the country was missing were removed. The coordinates of cities were used to extract the environmental data via the terra package (Hijmans et al. 2022, version 1.6-47) in the R 4.2.1 environment.

Version 2023.08 provided median values from 23 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 and from 18 GCMs for SSP 3-7.0, both for the 2050s (2041-2060). Similar methods were used to calculate these median values as in the case studies for the TreeGOER manuscript (calculations were partially done via the BiodiversityR::ensemble.envirem.run function and with downscaled bioclimatic and monthly climate 2.5 arc-minutes future grid layers available from WorldClim 2.1).

Version 2023.09 used similar methods as for previous versions to provide median values from 13 GCMs for the 2090s (2081-2100) for SSP 5-8.5.

The locations of the 52,602 cities are mapped in one of the series available from the TreeGOER Global Zones atlas that can be obtained from https://doi.org/10.5281/zenodo.8252756.

Version 2024.10 includes a new data set that documents the location of the city locations in Holdridge Life Zones. Information is given for historical (1901-1920), contemporary (1979-2013) and future (2061-2080; separately for RCP 4.5 and RCP 8.5) climates inferred from global raster layers that are available for download from DRYAD and were created for the following article: Elsen et al. 2022. Accelerated shifts in terrestrial life zones under rapid climate change. Global Change Biology, 28, 918–935. https://doi.org/10.1111/gcb.15962. Version 2024.10 further includes Holdridge Life Zones for the climates that were available from the previous versions, calculating biotemperatures and life zones with similar methods as used by Holdridge (1947; 1967) and Elsen et al. (2022) (for future climates, median values were determined first for monthly maximum and minimum temperatures across GCMs ). The distributions of the 48,129 species documented in TreeGOER across the Holdridge Life Zones are given in this Zenodo archive: https://zenodo.org/records/14020914.

Version 2024.11 includes a new data set that documents the location of the city locations in Köppen-Geiger climate zones. Information is given for historical (1901-1930, 1931-1960, 1961-1990) and future (2041-2070 and 2071-2099) climates, with for the future climates seven scenarios each (SSP 1-1.9, SSP 1-2.6, SSP 2-4.5, SSP 3-7.0, SSP 4-3.4, SSP 4-6.0 and SSP 5-8.5). This data set was created from 30 arc-second raster layers available via: Beck, H.E., McVicar, T.R., Vergopolan, N. et al. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci Data 10, 724 (2023). https://doi.org/10.1038/s41597-023-02549-6

Version 2025.03 includes extra columns for the baseline, 2050s and 2090s datasets that partially correspond to climate zones used in the GlobalUsefulNativeTrees database. One of these zones are the Whittaker biome types, available as a polygon from the plotbiomes package (see also here). Whittaker biome types were extracted with similar R scripts as described by Kindt 2025 (these were also used to calculate environmental ranges of TreeGOER species, as archived here).

Version 2025.03 further includes information for the baseline climate on the steady state water table depth, obtained from a 30 arc-seconds raster layer calculated by the GLOBGM v1.0 model (Verkaik et al. 2024). Also included was the elevation, obtained from the same WorldClim 2.1 raster layer used to prepare TreeGOER.

As an alternative to CitiesGOER, the ClimateForecasts database (https://zenodo.org/records/10776414) documents the environmental conditions at the locations of 15,504 weather stations. ClimateForecasts was integrated in the GlobalUsefulNativeTrees database (see Kindt et al. 2023).

When using CitiesGOER in your work, cite this depository and the following:

Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086

Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography, 41(2), 291–307. https://doi.org/10.1111/ecog.02880

Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. SOIL, 7(1), 217–240. https://doi.org/10.5194/soil-7-217-2021

Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology 29: 6303–6318. https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914.

Opendatasoft (2023) Geonames - All Cities with a population > 1000. https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name (accessed 22-JULY-2023)

When using information from the Holdridge Life Zones, also cite:

Elsen, P. R., Saxon, E. C., Simmons, B. A., Ward, M., Williams, B. A., Grantham, H. S., Kark, S., Levin, N., Perez-Hammerle, K.-V., Reside, A. E., & Watson, J. E. M. (2022). Accelerated shifts in terrestrial life zones under rapid climate change. Global Change Biology, 28, 918–935. https://doi.org/10.1111/gcb.15962

When using information from Köppen-Geiger climate zones, also cite:

Beck, H.E., McVicar, T.R., Vergopolan, N., Berg, A., Lutsko, N.J., Dufour, A., Zeng, Z., Jiang, X., van Dijk, A.I. and Miralles, D.G. 2023. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci Data 10, 724. https://doi.org/10.1038/s41597-023-02549-6

When using information on the Whittaker biome types, also cite:

Ricklefs, R. E., Relyea, R. (2018). Ecology: The Economy of Nature. United States: W.H. Freeman.

Whittaker, R. H. (1970). Communities and ecosystems.

Valentin Ștefan, & Sam Levin. (2018). plotbiomes: R package for plotting Whittaker biomes with ggplot2 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.7145245

When using information on the steady state water table depth, also cite:

Verkaik, J., Sutanudjaja, E. H., Oude Essink, G. H., Lin, H. X., & Bierkens, M. F. (2024). GLOBGM v1. 0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model. Geoscientific Model Development, 17(1), 275-300. https://gmd.copernicus.org/articles/17/275/2024/

The development of CitiesGOER was supported by the Darwin Initiative to project DAREX001 of Developing a Global Biodiversity Standard certification for tree-planting and restoration, by Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the Provision of Adequate Tree Seed Portfolio project in Ethiopia, and by the Green Climate Fund through the IUCN-led Transforming the Eastern Province of Rwanda through Adaptation project. Development of version 2024.10 was further supported by the Green Climate Fund through the Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso project, by the Bezos Earth Fund to the Quality Tree Seed for Africa in Kenya and Rwanda project and by the German International Climate Initiative (IKI) to the regional tree seed programme on The Right Tree for the Right Place for the Right Purpose in Africa.

This dataset contains 146 jpeg images of multicores collected during R/V Weatherbird II cruise 1305 from September 22nd to 29th 2012. Additionally, this includes a file of raw sort data for macrofauna to the family level.

Dataset from Accounting and Corporate Regulatory Authority. For more information, visit https://data.gov.sg/datasets/d_2b8c54b2a490d2fa36b925289e5d9572/view

The factors that stabilize the folding and oligomerization of membrane proteins are still not well understood. In particular, it remains unclear how the tight and complementary packing between apolar side chains observed in the core of membrane proteins contributes to their stability. Complementary packing is a necessary feature since packing defects are generally destabilizing for membrane proteins. The question is the extent to which packing of apolar side chains – and the resulting van der Waals interactions – is a sufficient driving force for stabilizing the interaction between transmembrane helices in the absence of hydrogen bonding and polar interactions. We addressed this question with an approach based on high-throughput protein design and the homodimerization of single-pass helices as the model system. We designed hundreds of transmembrane helix dimers mediated by apolar packing in the backbone configurations that are most commonly found in membrane proteins. We assessed the as..., , # Structural models and Sort-seq data for: Packing of apolar amino acids is not a strong stabilizing force in transmembrane helix dimerization

This repository contains data relative to Loiseau and Senes, bioRxiv article https://doi.org/10.1101/2025.04.26.649789

• Structural model of designed dimers (PDB files)
• Sort-seq Data

Structural model of dimers (PDB files)

The compressed zip file contains the structural models of the designed transmembrane dimers. The file name corresponds to the constructs listed in Table S1: [G/R/L]_NNN.pdb, where

• G identifies the GAS-right dimers
• L the Left dimers
• R the Right dimers NNN is an integer serial number.

File

• structural_models.zip

Code/software

PDB files are viewable with PyMol or other software that can read Protein Data Bank coordinate files.

NGS Data

Raw data from Next Generation Sequencing utilizing the TOXGREEN sort-seq methods as in Anderson et al., 2025...,

This is the raw particulate matter 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. One simple change in the excel file could make the code full of bugs.

This is the raw H2S data- concentration of H2S in parts per million in the 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. One simple change in the excel file could make the code full of bugs.

With the use of high-density multi-electrode recording devices, electrophysiological signals resulting from action potentials of individual neurons can now be reliably detected on multiple adjacent recording electrodes. Spike sorting assigns these signals to putative neural sources. However, until now, spike sorting can only be performed after completion of the recording, preventing true real time usage of spike sorting algorithms. Utilizing the unique propagation patterns of action potentials along axons detected as high-fidelity sequential activations on adjacent electrodes, together with a convolutional neural network-based spike detection algorithm, we introduce RT-Sort (Real Time Sorting), a spike sorting algorithm that enables the sorted detection of action potentials within 7.5ms±1.5ms (mean±STD) after the waveform trough while the recording remains ongoing. RT-Sort’s true real-time spike sorting capabilities enable closed loop experiments with latencies comparable to synaptic delay times. We show RT-Sort’s performance on both Multi-Electrode Arrays as well as Neuropixels probes to exemplify RT-Sort’s functionality on different types of recording hardware and electrode configurations.