There are over 400 service requests types that are reported in the 311 system that affect the quality of life of our citizens, neighborhoods, and communities. The most popular service requests include but are not limited to animal services requests, high weeds, junk motor vehicles, and a number of other code compliance-related issues. Requests that deal with streets and mobility such as street and pot hole repair are also very common. 311 also receives requests to address environmental issues such as water conservation and air quality complaints. This dataset represents all Service Request from October 1, 2018 to present.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

Data mobilsed by WWBIC staff from WTSWW offices in West Wales.

Data from Fig 101-105a. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with...

This is 2020 decennial census data at the county level. Technical documentation for the 2020 census is available here: https://www2.census.gov/programs-surveys/decennial/2020/technical-documentation/complete-tech-docs/summary-file/2020Census_PL94_171Redistricting_NationalTechDoc.pdf

The American Community Survey (ACS) is an ongoing survey that provides data every year -- giving communities the current information they need to plan investments and services. The ACS covers a broad range of topics about social, economic, demographic, and housing characteristics of the U.S. population. Much of the ACS data provided on the Census Bureau's Web site are available separately by age group, race, Hispanic origin, and sex. Summary files, Subject tables, Data profiles, and Comparison profiles are available for the nation, all 50 states, the District of Columbia, Puerto Rico, every congressional district, every metropolitan area, and all counties and places with populations of 65,000 or more. Subject tables provide an overview of the estimates available in a particular topic. The data are presented as population counts and percentages. There are over 16,000 variables in this dataset.

qPCR results for Vitellogenin. This dataset is associated with the following publication: Armstrong, B., J. Lazorchak , K. Jensen , H. Haring , M.E. Smith, R. Flick , D. Bencic , and A. Biales. Reproductive effects in fathead minnows (Pimphales promelas) following a 21 d exposure to 17α-ethinylestradiol. CHEMOSPHERE. Elsevier Science Ltd, New York, NY, USA, 144(1): 366-373, (2015).

Raw 1/10th Degree Wind Force Probability data for all wind speeds.

This dataset contains data provided alongside the paper "An all-Africa dataset of energy model “supply regions” for solar PV and wind power".

It concerns a novel representative subset of attractive sites for solar PV and onshore wind power for the entire African continent. We refer to these sites as “Model Supply Regions” (MSRs). This MSR dataset was created from an in-depth analysis of various existing datasets on resource potential, grid infrastructure, land use, topography and others (see Methods), and achieves hourly temporal resolution and kilometre-scale spatial resolution. This dataset fills an important research need by closing the gap between comprehensive datasets on African VRE potential (such as the Global Solar Atlas and Global Wind Atlas) on the one hand, and the input needed to run cost-optimisation models on the other. It also allows a detailed analysis of the trade-offs involved in exploiting excellent, but far-from-grid resources as compared to mediocre but more accessible resources, which is a crucial component of power systems planning to be elaborated for many African countries.

Five separate datasets are included:

(1) 20220412_country_maps.rar: Country-level visualisations (in the form of maps) of the screened MSRs. We screened the dataset according to the criterion that the total area of screened MSRs should not exceed 5% of an individual country’s surface area. See also (4).

(2) 20220412_excel_files.rar: Excel files containing the screened MSRs suggested for model inclusion alongside various metadata. See also (5).

(3) 20220412_shapefiles.rar: Shapefiles containing the screened MSRs in GIS format.

(4) 20220705_clusters_maps.zip: Same as (1), but showing the clusters (formed from individual MSRs) described in the publication. We use an example of clustering down to 10, 5 or 2 clusters per country, depending on country size. The archive also contains Excel files summarising the clusters, including model-ready hourly profiles.

(5) 20220822_excel_files_prescreen.rar: Same as (2), but containing all identified MSRs prior to screening.

The International Bank for Reconstruction and Development (IBRD) loans are public and publicly guaranteed debt extended by the World Bank Group. IBRD loans are made to, or guaranteed by, countries that are members of IBRD. IBRD may also make loans to IFC. IBRD lends at market rates. Data are in U.S. dollars calculated using historical rates. This dataset contains historical snapshots of the Statement of Loans including the latest available snapshot. The World Bank complies with all sanctions applicable to World Bank transactions.

This dataset contains State data for all Medicare beneficiaries regardless of age. The dataset includes state and county level data that covers demographic, cost utilization and quality data for all ages. The Geographic Variation Public Use File serve as an evaluation of the utilization and quality of healthcare services according to the geographic area of the population covered by Medicare.

background normalization systematic uncertainty band Au+Au 20-60%, 4<p_{\text{T}}^{(t)}<6 GeV/c, 2<p_{\text{T}}^{(a)}<4 GeV/c, slice 3

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

This paper explores a unique dataset of all the SET ratings provided by students of one university in Poland at the end of the winter semester of the 2020/2021 academic year. The SET questionnaire used by this university is presented in Appendix 1. The dataset is unique for several reasons. It covers all SET surveys filled by students in all fields and levels of study offered by the university. In the period analysed, the university was entirely in the online regime amid the Covid-19 pandemic. While the expected learning outcomes formally have not been changed, the online mode of study could have affected the grading policy and could have implications for some of the studied SET biases. This Covid-19 effect is captured by econometric models and discussed in the paper. The average SET scores were matched with the characteristics of the teacher for degree, seniority, gender, and SET scores in the past six semesters; the course characteristics for time of day, day of the week, course type, course breadth, class duration, and class size; the attributes of the SET survey responses as the percentage of students providing SET feedback; and the grades of the course for the mean, standard deviation, and percentage failed. Data on course grades are also available for the previous six semesters. This rich dataset allows many of the biases reported in the literature to be tested for and new hypotheses to be formulated, as presented in the introduction section. The unit of observation or the single row in the data set is identified by three parameters: teacher unique id (j), course unique id (k) and the question number in the SET questionnaire (n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9} ). It means that for each pair (j,k), we have nine rows, one for each SET survey question, or sometimes less when students did not answer one of the SET questions at all. For example, the dependent variable SET_score_avg(j,k,n) for the triplet (j=Calculus, k=John Smith, n=2) is calculated as the average of all Likert-scale answers to question nr 2 in the SET survey distributed to all students that took the Calculus course taught by John Smith. The data set has 8,015 such observations or rows. The full list of variables or columns in the data set included in the analysis is presented in the attached filesection. Their description refers to the triplet (teacher id = j, course id = k, question number = n). When the last value of the triplet (n) is dropped, it means that the variable takes the same values for all n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9}.Two attachments:- word file with variables description- Rdata file with the data set (for R language).Appendix 1. Appendix 1. The SET questionnaire was used for this paper. Evaluation survey of the teaching staff of [university name] Please, complete the following evaluation form, which aims to assess the lecturer’s performance. Only one answer should be indicated for each question. The answers are coded in the following way: 5- I strongly agree; 4- I agree; 3- Neutral; 2- I don’t agree; 1- I strongly don’t agree. Questions 1 2 3 4 5 I learnt a lot during the course. ○ ○ ○ ○ ○ I think that the knowledge acquired during the course is very useful. ○ ○ ○ ○ ○ The professor used activities to make the class more engaging. ○ ○ ○ ○ ○ If it was possible, I would enroll for the course conducted by this lecturer again. ○ ○ ○ ○ ○ The classes started on time. ○ ○ ○ ○ ○ The lecturer always used time efficiently. ○ ○ ○ ○ ○ The lecturer delivered the class content in an understandable and efficient way. ○ ○ ○ ○ ○ The lecturer was available when we had doubts. ○ ○ ○ ○ ○ The lecturer treated all students equally regardless of their race, background and ethnicity. ○ ○

This dataset contains street centerlines for vehicular and foot traffic in Allegheny County.

Street Centerlines are classified as Primary Road, Secondary Road, Unpaved Road, Limited Access Road, Connecting Road, Jeep Trail, Walkway, Stairway, Alleyway and Unknown. A Primary Road is a street paved with either concrete or asphalt that has two (2) or more lanes in each direction. A Secondary Road is a residential type hard surface road, or any hard surface road with only one (1) lane in each direction. An Unpaved Road is any road covered with packed dirt or gravel. A Limited Access Road is one that can only be accessed from a Connecting Road such as an Interstate Highway. A Connecting Road is a ramp connecting a Limited Access Road to a surface street. A Walkway is a paved or unpaved foot track that connects two (2) roads together. Walkways within College Campuses will also be shown. Recreational pedestrian trails and walkways through parks and wooded areas are not considered transportation and will not be digitized during this update. Walkways will not have an Edge of Pavement feature. A Stairway is a paved or wooden structure that connects two (2) roads together. Recreational pedestrian trails and walkways through parks and wooded areas are not considered transportation and will not be digitized during this update. An Alleyway is a road, usually narrower than a Secondary Road that runs between, but parallel to, two (2) Secondary Roads. Generally, Outbuildings will be adjacent to Alleyways. A Jeep Trail is a vehicular trail used for recreation. A Jeep Trail will not have an associated edge of pavement feature. A road coded as Unknown is a road, which in the judgment of the photogrammetrist, does not fall into any of the categories listed.

Centerlines will be visually placed between the edges of pavement. One (1) centerline will be placed between each edge of pavement. Roads with medial strips, such as Limited Access Roads, will have two (2) centerlines for those portions of the road where the medial strip is present. For roads that terminate with a cul-de-sac, the centerline shall continue through the center of the cul-de-sac and stop at the edge of pavement.

All attribute data will remain for all Street Centerlines that are not updated. For Street Centerlines that are new, the only attribute field that will be populated is the FeatureCode and UPDATE_YEAR. If a Street Centerline is graphically modified, the existing attribute data will remain and the UPDATE_YEAR will be set to 2004. The attribute values for 2004 Street Centerlines should be considered suspicious until verified.

The ArcInfo Street Centerline coverage that is being updated has 800 segments of Paper Streets, 66 segments of Vacated Streets and 78 segments of Steps. Street Centerlines that are coded as Paper Streets in the OWNER field will remain unchanged in the updated dataset unless the area has been developed. In the event the area has been developed, the Street Centerlines will be modified to reflect the true condition of the visible roads. Street Centerlines that are coded as Vacated in the OWNER field will also remain unchanged in the updated dataset. In the event the area coinciding with the Vacated Streets has been developed, the Vacated Street Centerlines will be removed in order to reflect the true condition of the area. Street Centerlines that are coded as Steps in the OWNER field will be updated to reflect the current condition of the area. The Street Centerlines dataset consists of an external table that links to the supplied coverages and the Geodatabase created for this project using the "-ID" (UserID) field. In order to maintain the link to the external table and not loose valuable data the decision was made to keep all database information currently in the Street Centerline dataset. When a Street Centerline is modified during the update process, the field "UPDATE_YEAR" is set to 2004. All other database attributes will remain unchanged from the original values. All Street Centerline database data with an "UPDATE_YEAR" of 2004 should be verified before used. In some occasions the Street Centerline was divided into two (2) sections to allow for a new road intersection. Both sections of the resulting Street Centerline will have the same database attributes including Address Range. All new Street Centerlines will have zero (0) for "SystemID" and "UserID".

This dataset was previously harvested from Allegheny County’s GIS data portal. The new authoritative source for this data is now the PASDA page (https://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=1224), which includes links to historical versions of the shapefile representations of this data.

United States Months of Supply: All Residential: Olean, NY data was reported at 2.900 Month in Jul 2020. This records a decrease from the previous number of 5.300 Month for Jun 2020. United States Months of Supply: All Residential: Olean, NY data is updated monthly, averaging 9.050 Month from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 22.400 Month in Feb 2012 and a record low of 2.900 Month in Jul 2020. United States Months of Supply: All Residential: Olean, NY data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB029: Months of Supply: by Metropolitan Areas.

The American Community Survey (ACS) is an ongoing survey that provides data every year -- giving communities the current information they need to plan investments and services. The ACS covers a broad range of topics about social, economic, housing, and demographic characteristics of the U.S. population. The ACS 5-year data profiles include the following geographies: nation, all states (including DC and Puerto Rico), all metropolitan areas, all congressional districts, all counties, all places and all tracts. The Data profiles contain broad social, economic, housing, and demographic information. The data are presented as both counts and percentages. There are over 2,400 variables in this dataset.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

United States Listings w/ Price Drops: sa: All Residential: Pontiac, IL data was reported at 18.502 % in Jul 2020. This records a decrease from the previous number of 22.119 % for Jun 2020. United States Listings w/ Price Drops: sa: All Residential: Pontiac, IL data is updated monthly, averaging 6.237 % from Feb 2012 to Jul 2020, with 102 observations. The data reached an all-time high of 24.480 % in Feb 2020 and a record low of 2.915 % in Jul 2012. United States Listings w/ Price Drops: sa: All Residential: Pontiac, IL data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB042: Listings with Price Drops: by Metropolitan Areas: Seasonally Adjusted.

This repository includes code and data for the following article: TAGLIARI Mario M., Pascal DANTHU, Jean-Michel LEONG POCK TSY, Cyrille CORNU, Jonathan LENOIR, Vítor CARVALHO-ROCHA, Ghislain VIEILLEDENT. 2021. Not all species will migrate poleward as the climate warms: the case of the seven baobab species in Madagascar. Global Change Biology. doi: 10.1111/gcb.15859. The data/baobabs/ folder includes the occurrence dataset for the seven baobab species present in Madagascar (file data_Adansonia‧csv). This file is the result of years of field inventories in Madagascar by botanists and ecologists working at CIRAD. A development version of this code is available on GitHub: https://github.com/ghislainv/baobabs_mada.