Customs records of are available for CUDLIE ACCESORIES EXCEL APPAREL GINA GROUP SENSUAL H&R PLANNING GREAT BUY. Learn about its Importer, supply capabilities and the countries to which it supplies goods

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

The GAPs Data Repository provides a comprehensive overview of available qualitative and quantitative data on national return regimes, now accessible through an advanced web interface at https://data.returnmigration.eu/.

This updated guideline outlines the complete process, starting from the initial data collection for the return migration data repository to the development of a comprehensive web-based platform. Through iterative development, participatory approaches, and rigorous quality checks, we have ensured a systematic representation of return migration data at both national and comparative levels.

The Repository organizes data into five main categories, covering diverse aspects and offering a holistic view of return regimes: country profiles, legislation, infrastructure, international cooperation, and descriptive statistics. These categories, further divided into subcategories, are based on insights from a literature review, existing datasets, and empirical data collection from 14 countries. The selection of categories prioritizes relevance for understanding return and readmission policies and practices, data accessibility, reliability, clarity, and comparability. Raw data is meticulously collected by the national experts.

The transition to a web-based interface builds upon the Repository’s original structure, which was initially developed using REDCap (Research Electronic Data Capture). It is a secure web application for building and managing online surveys and databases.The REDCAP ensures systematic data entries and store them on Uppsala University’s servers while significantly improving accessibility and usability as well as data security. It also enables users to export any or all data from the Project when granted full data export privileges. Data can be exported in various ways and formats, including Microsoft Excel, SAS, Stata, R, or SPSS for analysis. At this stage, the Data Repository design team also converted tailored records of available data into public reports accessible to anyone with a unique URL, without the need to log in to REDCap or obtain permission to access the GAPs Project Data Repository. Public reports can be used to share information with stakeholders or external partners without granting them access to the Project or requiring them to set up a personal account. Currently, all public report links inserted in this report are also available on the Repository’s webpage, allowing users to export original data.

This report also includes a detailed codebook to help users understand the structure, variables, and methodologies used in data collection and organization. This addition ensures transparency and provides a comprehensive framework for researchers and practitioners to effectively interpret the data.

The GAPs Data Repository is committed to providing accessible, well-organized, and reliable data by moving to a centralized web platform and incorporating advanced visuals. This Repository aims to contribute inputs for research, policy analysis, and evidence-based decision-making in the return and readmission field.

Explore the GAPs Data Repository at https://data.returnmigration.eu/.

The “Final_matrices” excel file contains research output related to the paper “From exports to value added to income: Accounting for bilateral income transfers”. Details on how the data are assembled can be found in the paper and in its online appendix. Replication files (R-files and Matlab-codes) as well as the raw data needed for replication of all empirical results in the paper are available upon request from the author.

This repository contains two key components:

• Dataset: Export data on fresh grapes from Mexico, Chile, and Peru, focusing on the U.S. and global markets.
• R Scripts: Code for performing Constant Market Share (CMS) and Revealed Comparative Advantage (RCA) analyses based on the dataset.
  The dataset is provided in Excel format, and the R scripts include detailed comments to ensure reproducibility. This work supports the article: "Competitive Dynamics of Mexican Fresh Grapes in the U.S. Market".

Data for corresponding project/publication "Testing sample selection criteria and loss of biomarkers during cleaning of archaeological unglazed pottery to maximize organic residue yield". Raw GC-MS (ChemStation and MassHunter) and GC-MSMS (MassHunter) outputs, as well as data processing (OpenChrom) and quantitation (R, Excel). R data import and export commands will not reference the paths in this data file but the paths on the PC originally used.

A routine was developed in R ('bathy_plots.R') to plot bathymetry data over time during individual CEAMARC events. This is so we can analyse benthic data in relation to habitat, ie. did we trawl over a slope or was the sea floor relatively flat. Note that the depth range in the plots is autoscaled to the data, so a small range in depths appears as a scatetring of points. As long as you look at the depth scale though interpretation will be ok.

 The R files need a file of bathymetry data in '200708V3_one_minute.csv' which is a file containing a data export from the underway PostgreSQL ship database and 'events.csv' which is a stripped down version of the events export from the ship board events database export. If you wish to run the code again you may need to change the pathnames in the R script to relevant locations. If you have opened the csv files in excel at any stage and the R script gets an error you may need to format the date/time columns as yyyy-mm-dd hh;mm:ss, save and close the file as csv without opening it again and then run the R script.

 However, all output files are here for every CEAMARC event. Filenames contain a referenec to CEAMARC event id. Files are in eps format and can be viewed using Ghostview which is available as a free download on the internet.

The following files contains the information used to analyze the trial “Implementing the WHO Labour Care Guide to reduce the use of Caesarean section in four hospitals in India: a pragmatic, stepped wedge, cluster randomized pilot trial” in which it was hypothesized that the intervention would promote correct LCG use by these providers, changing their labour monitoring and management practices to align with WHO’s intrapartum recommendations. In turn, this could reduce overuse of Caesarean section, improve maternal and newborn outcomes, and enhance women’s care experiences.

Two datafiles with extension “csv” are uploaded. The databased named “LCG Trial Women Database (transition period included).csv” is the database which contains the data of the recruited women in the trial. There is one row per women. The databased named “LCG Trial Neonates Database (transition period included).csv” is the database which contains the data of the neonates born from the recruited women. There is one row per neonate.

The excel file “Data Dictionary LCG to Share.xlsx” is the data dictionary of the two databases. In the sheet named “Maternal Variables” a list and description of the variables included in the maternal database is included and, in the sheet, named “Neonatal Variables” a list and description of the variables included in the neonatal database is included.

Three files of “R” extension and one “rmd” are included. The file named “RunningModelsFunctions.R” is the one use to run the models that are included in the analyses, the file named “2. Final Analysis LCG Trial.R” is the one in which the tables are prepared, and the file named “3. LCG Results Output Final.rmd” is used to export the tables with results. The R file named “funciones.tablas.R” is used in the analyses.

Background: Malaria remains one of the leading causes of death in Sub-Saharan Africa (SSA). The scoping review mapped evidence in research on existing studies on malaria genome-wide association studies (GWAS) in SSA.Methods: A scoping review was conducted to map existing studies in genome-wide association on malaria in SSA, with a review period between 1st January 2000 and 31st December 2024. The searches were made with the last search done in January 2025. The extracted data were analyzed using R software and SRplot. Relevant studies were identified through electronic searching of Google Scholar, Pubmed, Scopus, and Web of Science databases. Two independent reviewers followed the inclusion-exclusion criteria to extract relevant studies. Data from the studies were collected and synthesized using Excel and Zotero software.Results: We identified 89 studies for inclusion. Most of these studies (n = 42, ) used a case-control study design, while the rest used cross-sectional, cohort, longitudinal, family-based, and experimental study designs. These studies were conducted between 2000 and 2024, with a noticeable increase in publications from 2012. Most studies were carried out in Kenya (n = 23), Gambia (n = 18), Cameroon (n = 15), and Tanzania (n = 9), primarily exploring genetic variants associated with malaria susceptibility, resistance, and severity.Conclusion: Many case-control studies in Kenya and Gambia reported genetic variants in malaria susceptibility, resistance, and severity. GWAS on malaria is scarce in SSA, and even fewer studies are model-based. Consequently, there is a pressing need for more genome-wide research on malaria in SSA.Keywords: Genome-wide association studies, malaria, Sub-Saharan Africa, scoping review.

A routine was developed in R ('bathy_plots.R') to plot bathymetry data over time during individual CEAMARC events. This is so we can analyse benthic data in relation to habitat, ie. did we trawl …Show full descriptionA routine was developed in R ('bathy_plots.R') to plot bathymetry data over time during individual CEAMARC events. This is so we can analyse benthic data in relation to habitat, ie. did we trawl over a slope or was the sea floor relatively flat. Note that the depth range in the plots is autoscaled to the data, so a small range in depths appears as a scatetring of points. As long as you look at the depth scale though interpretation will be ok. The R files need a file of bathymetry data in '200708V3_one_minute.csv' which is a file containing a data export from the underway PostgreSQL ship database and 'events.csv' which is a stripped down version of the events export from the ship board events database export. If you wish to run the code again you may need to change the pathnames in the R script to relevant locations. If you have opened the csv files in excel at any stage and the R script gets an error you may need to format the date/time columns as yyyy-mm-dd hh;mm:ss, save and close the file as csv without opening it again and then run the R script. However, all output files are here for every CEAMARC event. Filenames contain a referenec to CEAMARC event id. Files are in eps format and can be viewed using Ghostview which is available as a free download on the internet.