Script and data used to create figures and realize statistical analysis presented in the online paper

BEAUGUITTE, Laurent et PECOUT, Hugues. Diffusion globale et fonctionnements locaux: géographies des scènes metal. Mappemonde, 2019, 127. [https://journals.openedition.org/mappemonde/2017]

The zip folder contains: - R script - shapefiles (FDC folder) - metal-archives database (DATA folder) - relational data regarding two NSBM circles (graph folder).

Students will explore U.S. census data to see the spatial differences in the United States’ population. The activity uses a web-based map and is tied to the AP Human Geography benchmarks. Learning outcomes:· Unit 2, A1: Geographical analysis of population (density, distribute and scale)· Unit 2, A3: Geographical analysis of population (composition: age, sex, income, education and ethnicity)· Unit 2, A4: Geographical analysis of population (patterns of fertility, mortality and health)Find more advanced human geography geoinquiries and explore all geoinquiries at http://www.esri.com/geoinquiries

This data set contains Quarterly Results and yearly Targets by Operating Unit for Fiscal Years 2016 – 2020 and commonly used indicators across PEPFAR’s Program Areas. Data can be downloaded as a compressed (zip) file, which contains text files in csv (comma separated values) format. For indicator definitions, please consult the latest MER Indicator Reference GuideFor additional PEPFAR data, please visit data.pepfar.gov. Unless otherwise noted, the content, data, documentation, code, and related materials on data.pepfar.gov is public _domain and made available with a Creative Commons CC0 1.0 Universal dedication and license-free (per US Code 17 USC § 105). Citation of data.pepfar.gov as a source of the data is appreciated.

Background: The unprecedented impact of the COVID-19 pandemic on modern society has ignited a “gold rush” for effective treatment and diagnostic strategies, with a significant diversion of economic, scientific, and human resources toward dedicated clinical research. We aimed to describe trends in this rapidly changing landscape to inform adequate resource allocation.Methods: We developed an online repository (COVID Trial Monitor) to analyze in real time the growth rate, geographical distribution, and characteristics of COVID-19 related trials. We defined structured semantic ontologies with controlled vocabularies to categorize trial interventions, study endpoints, and study designs. Analyses are publicly available at https://bioinfo.ieo.it/shiny/app/CovidCT.Results: We observe a clear prevalence of monocentric trials with highly heterogeneous endpoints and a significant disconnect between geographic distribution and disease prevalence, implying that most countries would need to recruit unrealistic percentages of their total prevalent cases to fulfill enrolment.Conclusions: This geographically and methodologically incoherent growth casts doubts on the actual feasibility of locally reaching target sample sizes and the probability of most of these trials providing reliable and transferable results. We call for the harmonization of clinical trial design criteria for COVID-19 and the increased use of larger master protocols incorporating elements of adaptive designs. COVID Trial Monitor identifies critical issues in current COVID-19-related clinical research and represents a useful resource with which researchers and policymakers can improve the quality and efficiency of related trials.

National Bank of Ukraine (NBU): Geographical Analysis (GA): Liabilities data was reported at 525,106.000 UAH mn in 2017. This records an increase from the previous number of 502,323.000 UAH mn for 2016. National Bank of Ukraine (NBU): Geographical Analysis (GA): Liabilities data is updated yearly, averaging 202,757.000 UAH mn from Dec 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 525,106.000 UAH mn in 2017 and a record low of 27,508.000 UAH mn in 2001. National Bank of Ukraine (NBU): Geographical Analysis (GA): Liabilities data remains active status in CEIC and is reported by National Bank of Ukraine. The data is categorized under Global Database’s Ukraine – Table UA.KB028: Balance Sheet: National Bank of Ukraine: Geographical Analysis .

National Bank of Ukraine (NBU): Geographical Analysis (GA): Assets data was reported at 409,118.000 UAH mn in 2017. This records a decrease from the previous number of 433,957.000 UAH mn for 2016. National Bank of Ukraine (NBU): Geographical Analysis (GA): Assets data is updated yearly, averaging 118,660.000 UAH mn from Dec 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 473,755.000 UAH mn in 2015 and a record low of 10,377.000 UAH mn in 2007. National Bank of Ukraine (NBU): Geographical Analysis (GA): Assets data remains active status in CEIC and is reported by National Bank of Ukraine. The data is categorized under Global Database’s Ukraine – Table UA.KB028: Balance Sheet: National Bank of Ukraine: Geographical Analysis .

Annual average Child and Working tax credits statistics of finalised awards for each year. Geographical analysis down to local authority level. Supplement on over and under payments. Supplement on over and under payments. Geographical analysis down to local authority level.

Results of the paper "Prospective European District Heating scenarios based on geographical analysis".

Three scenarios are generated : Ambitious, Circular, and Conservative. For each scenario, there is a gpkg file and an excel file. The gpkg file is the whole dataset of inputs and results, each row being a European city. The excel summarizes the results for each EU27+UK country.

Ukraine NBU: GA: Net Position data was reported at -115,988.000 UAH mn in 2017. This records a decrease from the previous number of -68,366.000 UAH mn for 2016. Ukraine NBU: GA: Net Position data is updated yearly, averaging -89,135.000 UAH mn from Dec 2002 (Median) to 2017, with 16 observations. The data reached an all-time high of 44,765.000 UAH mn in 2014 and a record low of -150,388.000 UAH mn in 2007. Ukraine NBU: GA: Net Position data remains active status in CEIC and is reported by National Bank of Ukraine. The data is categorized under Global Database’s Ukraine – Table UA.KB028: Balance Sheet: National Bank of Ukraine: Geographical Analysis .

Ukraine NBU: GA: Assets: Others data was reported at 137.000 UAH mn in 2017. This records a decrease from the previous number of 184.000 UAH mn for 2016. Ukraine NBU: GA: Assets: Others data is updated yearly, averaging 295.000 UAH mn from Dec 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 1,070.000 UAH mn in 2001 and a record low of 114.000 UAH mn in 2007. Ukraine NBU: GA: Assets: Others data remains active status in CEIC and is reported by National Bank of Ukraine. The data is categorized under Global Database’s Ukraine – Table UA.KB028: Balance Sheet: National Bank of Ukraine: Geographical Analysis .

Data publication of approximately 2236 collectionspecimens for type of plants: Oxytropis albovillosa B.Fedtsch. —43; Oxytropis anaulgensis Pavlov—30; Oxytropis arassanica Gontsch.—55; Oxytropis aspera Gontsch.—34; Oxytropis aulieatensis Vved.—23; Oxytropis babatagi Abdusal.—2; Oxytropis baissunensis Vassilcz.—53; Oxytropis caespitosula Gontsch.—2; Oxytropis canopatula Vassilcz.—8; Oxytropis capusii Franch.—84; Oxytropis chesneyoides Gontsch.—21; Oxytropis didymophysa Bunge—3; Oxytropis fedtschenkoana Vassilcz.—52; Oxytropis fedtschenkoi Vassilcz.—22; Oxytropis glabra DC.—2; Oxytropis gymnogyne Bunge—65; Oxytropis humifusa Kar. & Kir.—5; Oxytropis immersa (Baker ex Aitch.) Bunge ex B.Fedtsch.—47; Oxytropis integripetala Bunge—3; Oxytropis iskanderica B.Fedtsch.—10; Oxytropis jucunda Vved.—22; Oxytropis kamelinii Vassilcz.—20; Oxytropis lapponica (Wahlenb.) J.Gay—8; Oxytropis lasiocarpa Gontsch.—32; Oxytropis lehmannii Bunge—77; Oxytropis leptophysa Bunge—18; Oxytropis leucocyanea Bunge—90; Oxytropis lipskyi Gontsch.—7; Oxytropis lithophila Vassilcz.—56; Oxytropis litwinowii B.Fedtsch.—61; Oxytropis macrocarpa Kar. & Kir.—67; Oxytropis macrodonta Gontsch.—106; Oxytropis maidantalensis B.Fedtsch.—1; Oxytropis megalorrhyncha Nevski—9; Oxytropis michelsonii B.Fedtsch.—29; Oxytropis microsphaera Bunge—89; Oxytropis ornata Vassilcz.—49; Oxytropis pamiroalajca Abdusal.—6; Oxytropis pilosissima Vved.—191; Oxytropis poncinsii Franch.—1; Oxytropis pseudoleptophysa Boriss.—72; Oxytropis pseudorosea Filimonova—62; Oxytropis riparia Litv.—5; Oxytropis rosea Bunge—29; Oxytropis savellanica Bunge ex Boiss.—85; Oxytropis schachimardanica Filimonova—3; Oxytropis seravschanica Gontsch.—17; Oxytropis sewerzowii Bunge—7; Oxytropis submutica Bunge—153; Oxytropis tachtensis Franch.—116; Oxytropis terekensis B.Fedtsch.—20; Oxytropis trajectorum B.Fedtsch.—18; Oxytropis trichocalycina Bunge ex Boiss.—34; Oxytropis tschimganica Gontsch.—55; Oxytropis tyttantha Gontsch.—8; Oxytropis ugamica Gontsch.—44; Oxytropis vvedenskyi Filimonova—6

This data provides bi-annual analysis of the number of children and families receiving Child Tax Credit (CTC) or working Tax Credit (WTC) and whether they are in out of work households. This data is updated more regularly than the LSOA data Source: HM Customs and Revenue Publisher: HM Customs and Revenue Geographies: Local Authority District (LAD), Government Office Region (GOR), Parliamentary Constituency Geographic coverage: Great Britain Time coverage: 2003 to 2008 Type of data: Administrative data

Ukraine NBU: GA: Liabilities: Others data was reported at 421.000 UAH mn in 2017. This records an increase from the previous number of 351.000 UAH mn for 2016. Ukraine NBU: GA: Liabilities: Others data is updated yearly, averaging 614.000 UAH mn from Dec 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 2,602.000 UAH mn in 2001 and a record low of 52.000 UAH mn in 2004. Ukraine NBU: GA: Liabilities: Others data remains active status in CEIC and is reported by National Bank of Ukraine. The data is categorized under Global Database’s Ukraine – Table UA.KB028: Balance Sheet: National Bank of Ukraine: Geographical Analysis .

According to Verified Market Research, North America and Europe Ad Tech Software Market were valued at USD 16011.45 Million in 2024 and are projected to reach USD 28386.51 Million by 2031, growing at a CAGR of 7.42% from 2024 to 2031.

When the first advertising technology was introduced at the dawn of digital advertising evolution, the most significant change occurred. Every year, new ad tech solutions emerge, and marketers polish their strategies by adding more complex tools to their strategies. Advertisers may utilize sophisticated tools, extensive data, and cutting-edge methods to create flawless campaigns that get their messages to the correct users, rather than posting ads at random and hoping for luck. In addition, the programmatic advertising market relies heavily on ad tech tools. Modern advertisements are sent to the most appropriate viewers at the right time and in the right context thanks to Ad tech. Moreover, increasing internet penetration and the growing adoption of smart devices have resulted in the growing demand for the Ad-tech Software Market. Ad tech methods assisted by influential data allow agencies to make smarter placements of ads at the perfect time.

Additional climate information for research paper «Ecological and Geographical Analysis of Distribution of Heracleum persicum, H. mantegazzianum and H. sosnowskyi on The Northern Limit of Its Invaded Range in Europe» submitted to Russian Journal of Biological Invasions

This repository serves as supplementary material for the article "Stations of the Publicum Portorium Illyrici are a Strong Predictor of the Mithraic Presence in the Danubian Provinces: Geographical Analysis of the Distribution of the Roman Cult of Mithras". This work was supported by the European Regional Development Fund project “Beyond Security: Role of Conflict in Resilience-Building” (reg. no.: CZ.02.01.01/00/22_008/0004595). The repository contains the following data:

1. Dataset for the evidence of the Roman cult of Mithras as collected by Ales Chalupa for the Roman provinces of Raetia, Noricum, Dalmatia, Regio X, Dalmatia, Pannonia, Moesia, Thracia. File: Mithras_AC.geojson
2. Geocoded Latin inscriptions collected from the LIST dataset (Vojtěch Kaše, Petra Heřmánková, & Adéla Sobotková. (2024). LIST (v1.2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10473706) mentioning the deities Mithras, Minerva, Jupiter, Jupiter Dolichenus, Asclepius, Apollo, Mercury, Silvanus, Isis, Hercules in a csv format. Filename format: ListdeitynameWKTv1_2.csv.
3. Dictionary of noun forms for filtering deities Mithras, Minerva, Jupiter, Jupiter Dolichenus, Asclepius, Apollo, Mercury, Silvanus, Isis, Hercules in Latin inscriptions. File: deities_decline.csv
4. Spatial proximity analysis measurements. WEST_GEO_DIST(km) lists distances from Portorium West stations (N=11) to their nearest cult proxy in kilometers in geographical space; WEST_NET_DIST(m) lists distances from Portorium West stations (N=11) to their nearest cult proxy in meters across the transportation network of Roman roads; EAST_GEO_DIST(km) and EAST_NET_DIST(m) follow the same rationale for stations in the Portorium East group (N=16); NORICUM_DIST_NET_30(m) lists distances from Roman settlements in the province Noricum (and in the distance of 30 kilometers beyond the borders of Noricum) to their nearest cult proxy and portorium stations across the transportation network of Roman roads in meters (N=53). File: Distances.xlsx
5. Filtered Mithras_AC dataset with additional evidence from provinces neighboring Noricum for the purposes of correlation analysis. File: Mithras_AC_Noricum_neighbors.geojson
6. Results of Spearman correlation analysis for NORICUM_DIST_NET_30(m). File: NORICUM_RES_NET_30.csv
7. Geocoded Stations of the Publicum Portorium Illyrici. File: Portorium stations.csv

Asia Pacific Yeast Market Report - By Top Key Players Updates, Business Growing Strategies, Competitive Dynamics, Geographical Analysis and Market Segmentation

Colon Hydrotherapy Market is projected to grow at a CAGR of 4.6% during the forecast during the forecast period 2025-2033 | DataM Intelligence

Ukraine NBU: GA: Liabilities: Accounts of Government and Other Clients data was reported at 56,123.000 UAH mn in 2017. This records an increase from the previous number of 48,541.000 UAH mn for 2016. Ukraine NBU: GA: Liabilities: Accounts of Government and Other Clients data is updated yearly, averaging 13,016.000 UAH mn from Dec 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 56,123.000 UAH mn in 2017 and a record low of 1,501.000 UAH mn in 2001. Ukraine NBU: GA: Liabilities: Accounts of Government and Other Clients data remains active status in CEIC and is reported by National Bank of Ukraine. The data is categorized under Global Database’s Ukraine – Table UA.KB028: Balance Sheet: National Bank of Ukraine: Geographical Analysis .