The Walkability Index dataset characterizes every Census 2019 block group in the U.S. based on its relative walkability. Walkability depends upon characteristics of the built environment that influence the likelihood of walking being used as a mode of travel. The Walkability Index is based on the EPA's previous data product, the Smart Location Database (SLD). Block group data from the SLD was the only input into the Walkability Index, and consisted of four variables from the SLD weighted in a formula to create the new Walkability Index. This dataset shares the SLD's block group boundary definitions from Census 2019. The methodology describing the process of creating the Walkability Index can be found in the documents located at https://edg.epa.gov/EPADataCommons/public/OA/WalkabilityIndex.zip. You can also learn more about the Smart Location Database at https://www.epa.gov/smartgrowth/smart-location-mapping.

This dataset consists of S&P 500 (Standard and Poor's 500) index data including level, dividend, earnings and P/E (Price Earnings) ratio on a monthly basis since 1871. The S&P 500 (Standard and Poor's 500) is a free-float, capitalization-weighted index of the top 500 publicly listed stocks in the US (top 500 by market capitalization).

The dataset collection titled 'index_100km' is a valuable resource comprising one or multiple tables of related data. The data within these tables is meticulously organized in a structured format of columns and rows for easy understanding and analysis. The data for these tables has been sourced from the website of 'Lantmäteriet' (The Land Survey) in Sweden. This ensures the data's authenticity, given the reputation of the source. The dataset collection is crucial for various analyses and can be significantly useful for researchers and professionals in various fields.

There are a total of 5 datasets.sp500_datasp500_newFeatures_datasp500_lagged_datanasdaq_lagged_datahsi_lagged_dataThe first dataset contains 34 years worth of data from 1990 to 2023 for the stock index S&P500. This dataset has been preprocessed and is used for training and testing. The second dataset transforms the initial dataset with the addition of new features derived from the first dataset. The third dataset is a different transformation of the first dataset where the features are mostly contained of lagged features. The fourth dataset contains 10 years of data for the NASDAQ index from 2014-2023 following the same format of lagged features like the third dataset. The fifth dataset has 10 years of data from 2014-2023 for the HSI stock index. This dataset also follows the same format of features as the third datasetAll five of these datasets were used as implementations for a research to predict tomorrow's closing price based on today's financial features

Vermont extract of Uber's H3 Hexagonal Hierarchical Spatial Index, at resolution level 7, approximately 1 km per edge, for indexing locations. Extracted from Uber's dataset at https://eng.uber.com/h3/ via FME's H3HexagonalIndexer transformer, for all filling indexes within and intersecting with the Vermont State Boundary in July 2022.Field Descriptions:_h3index / H3 INDEX: Uber-assigned unique identifier per each individual hexagon at any resolution level._h3res / H3 RESOLUTION LEVEL: Uber-assigned index resolution level between 0 and 15, with 0 being coarsest and 15 being finest.

Vermont extract of Uber's H3 Hexagonal Hierarchical Spatial Index, at resolution level 5, approximately 9 km per edge, for indexing locations. Extracted from Uber's dataset at https://eng.uber.com/h3/ via FME's H3HexagonalIndexer transformer, for all filling indexes within and intersecting with the Vermont State Boundary in July 2022.Field Descriptions:_h3index / H3 INDEX: Uber-assigned unique identifier per each individual hexagon at any resolution level._h3res / H3 RESOLUTION LEVEL: Uber-assigned index resolution level between 0 and 15, with 0 being coarsest and 15 being finest.

alea-institute/kl3m-index-edgar-filings-s dataset hosted on Hugging Face and contributed by the HF Datasets community

The Elemental Data Index provides access to the holdings of NIST Physical Measurement Laboratory (PML) online data organized by element. It is intended to simplify the process of retrieving online scientific data for a specific element from various online databases, including atomic spectroscopy, atomic data, x-ray absorption, and nuclear data. For some of the databases, the data are immediately retrieved; for others, the retrieval form is provided with the element entered in the form, but additional options must be selected in order to retrieve the data. Each of the databases can be individually accessed from the PML's Physical Reference Data page (http://pml.nist.gov/data).

The spatially-explicit harmonized global dataset of critical infrastructure (CI):

• Amount_of_infrastructure.zip contains data on the amount of infrastructure per infrastructure type at a resolution of 0.10x0.10 and 0.25x0.25 degrees
• CISI.zip contains data on the Critical Infrastructure Spatial Index (CISI) at a resolution of 0.10x0.10 and 0.25x0.25 degrees

Please consult the following publication for detailed information: Nirandjan, S., Koks, E.E., Ward, P.J. et al. A spatially-explicit harmonized global dataset of critical infrastructure. Sci Data 9, 150 (2022). https://doi.org/10.1038/s41597-022-01218-4

This dataset package is focused on U.S construction materials and three construction companies: Cemex, Martin Marietta & Vulcan.

In this package, SpaceKnow tracks manufacturing and processing facilities for construction material products all over the US. By tracking these facilities, we are able to give you near-real-time data on spending on these materials, which helps to predict residential and commercial real estate construction and spending in the US.

The dataset includes 40 indices focused on asphalt, cement, concrete, and building materials in general. You can look forward to receiving country-level and regional data (activity in the North, East, West, and South of the country) and the aforementioned company data.

SpaceKnow uses satellite (SAR) data to capture activity and building material manufacturing and processing facilities in the US.

Data is updated daily, has an average lag of 4-6 days, and history back to 2017.

The insights provide you with level and change data for refineries, storage, manufacturing, logistics, and employee parking-based locations.

SpaceKnow offers 3 delivery options: CSV, API, and Insights Dashboard

Available Indices Companies: Cemex (CX): Construction Materials (covers all manufacturing facilities of the company in the US), Concrete, Cement (refinery and storage) indices, and aggregates Martin Marietta (MLM): Construction Materials (covers all manufacturing facilities of the company in the US), Concrete, Cement (refinery and storage) indices, and aggregates Vulcan (VMC): Construction Materials (covers all manufacturing facilities of the company in the US), Concrete, Cement (refinery and storage) indices, and aggregates

USA Indices:

Aggregates USA Asphalt USA Cement USA Cement Refinery USA Cement Storage USA Concrete USA Construction Materials USA Construction Mining USA Construction Parking Lots USA Construction Materials Transfer Hub US Cement - Midwest, Northeast, South, West Cement Refinery - Midwest, Northeast, South, West Cement Storage - Midwest, Northeast, South, West

Why get SpaceKnow's U.S Construction Materials Package?

Monitor Construction Market Trends: Near-real-time insights into the construction industry allow clients to understand and anticipate market trends better.

Track Companies Performance: Monitor the operational activities, such as the volume of sales

Assess Risk: Use satellite activity data to assess the risks associated with investing in the construction industry.

Index Methodology Summary Continuous Feed Index (CFI) is a daily aggregation of the area of metallic objects in square meters. There are two types of CFI indices; CFI-R index gives the data in levels. It shows how many square meters are covered by metallic objects (for example employee cars at a facility). CFI-S index gives the change in data. It shows how many square meters have changed within the locations between two consecutive satellite images.

How to interpret the data SpaceKnow indices can be compared with the related economic indicators or KPIs. If the economic indicator is in monthly terms, perform a 30-day rolling sum and pick the last day of the month to compare with the economic indicator. Each data point will reflect approximately the sum of the month. If the economic indicator is in quarterly terms, perform a 90-day rolling sum and pick the last day of the 90-day to compare with the economic indicator. Each data point will reflect approximately the sum of the quarter.

Where the data comes from SpaceKnow brings you the data edge by applying machine learning and AI algorithms to synthetic aperture radar and optical satellite imagery. The company’s infrastructure searches and downloads new imagery every day, and the computations of the data take place within less than 24 hours.

In contrast to traditional economic data, which are released in monthly and quarterly terms, SpaceKnow data is high-frequency and available daily. It is possible to observe the latest movements in the construction industry with just a 4-6 day lag, on average.

The construction materials data help you to estimate the performance of the construction sector and the business activity of the selected companies.

The foundation of delivering high-quality data is based on the success of defining each location to observe and extract the data. All locations are thoroughly researched and validated by an in-house team of annotators and data analysts.

See below how our Construction Materials index performs against the US Non-residential construction spending benchmark

Each individual location is precisely defined to avoid noise in the data, which may arise from traffic or changing vegetation due to seasonal reasons.

SpaceKnow uses radar imagery and its own unique algorithms, so the indices do not lose their significance in bad weather conditions such as rain or heavy clouds.

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The SDI(g) dataset complements Hickel's (2020) Sustainable Development Index (SDI) by considering the Governance Index (GI) as a proxy of the countries' governance climate computed with the World Bank-Worldwide Governance Indicators (WGIs).

This set of sixteen Landsat Thematic Mapper (TM)and Operational Land Imager (OLI)(Path 014 and Rows 032 and 033) surface reflectance data sets were collected between 2000 and 2015. This data presents a time-series analysis that uses linear spectral unmixing of composite Normalized Difference Vegetation Index, Normalized Difference Water Index, and Normalized Difference Soil Index data, to estimate the percentages of marsh vegetation, water, and exposed marsh substrate on the New Jersey intracoastal marshes. We used the composition of the marshes in terms of the percentage of marsh vegetation, water, and marsh substrate to produce Marsh Surface Condition Index (MSCI) maps consisting of three classes of marshes: severely impacted (characterized by 30% or less marsh vegetation), moderately impacted (characterized by greater than 30% to 60% marsh vegetation), and intact marshes (greater than 60% vegetation). The time-series analysis provides a means of evaluating the effect of Hurricane Sandy in the context of sixteen years of data sets collected at times that represent or approach peak vegetation growth. A seventeenth MSCI is the average percentage of vegetation on the New Jersey intracoastal marshes for the most recent six Landsat TM and OLI data sets: August 25, 2009; August 28, 2010; July 14, 2011; July 19, 2013; August 7, 2014, and August 26, 2015. This averaged MSCI data may be the best tool in identifying those areas of the marsh that are at greatest risk from sea level rise and storm damage.

Global Top Index: Exploring Trends in Stock Markets

  About the Dataset

The Global Top Index dataset offers a detailed view of daily trading activities from several of the world's leading stock market indices. This dataset is ideal for conducting comprehensive analyses to uncover insights and predictive trends in the international stock markets.

  Dataset Contents

The dataset encompasses the following key data points for each trading session across multiple dates… See the full description on the dataset page: https://huggingface.co/datasets/pettah/global-top-Index-exploring-trends-in-stock-Market.

Introduction

The leaf area index (LAI) data sets were generated by reprocessing the MODIS version 6.1 LAI products.

The raw data used include the MODIS LAI Version 6.1 products MCD15A2H (2002.7.4-2021), MOD15A2H (2000.2.18-2002.6.26) (Myneni et al., 2021) and MODIS Land Cover Type product MCD12Q1 (2001-2021) (Friedl and Sulla-Menashe, 2022).

The algorithm is mainly based on the two-step integrated method developed by Yuan et al. (2011), and the method of background value calculation was updated.

• For each year being reprocessed, the nearest 9-year data include itself are used.
• We use the corresponding year's land cover data for calculating the multi-year average, local per class mean, per class mean and multi-year per class mean (ref. Fig. 4, Yuan et al., 2011).

Data description

These monthly LAI data were provided at 0.5-degree resolution covering the period 2000-2021. Data of each year is stored in one NetCDF file, namely lai_monthly_0.5_{YEAR}.nc.

For LAI data with more spatial or temporal resolutions, see Land-Atmosphere Interaction Research Group at Sun Yat-sen University (bnu.edu.cn).

Caution

The reprocessed data for downloading consists two MODIS version 6.1 products, i.e., MCD15A2H (2002.7.4-2021) and MOD15A2H (2000.2.18-2002.6.26). The prefix “MCD” stands for a combined product, whose algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA’s Terra and Aqua satellites, while “MOD” data are retrieved only from the Terra satellite. We have found that their temporal-mean values were different especially in the equatorial region, which may result in an unrealistic trend (see Lin et al., 2022 for detailed discussion). Therefore, attention should be paid when using the reprocessed products for long-term trend analysis and data starting from year 2003 (i.e., only MCD) was recommended for LAI trend study.

Data citation

1. Lin, W.; Yuan, H.; Dong, W.; Zhang, S.; Liu, S.; Wei, N.; Lu, X.; Wei, Z.; Hu, Y.; Dai, Y. Reprocessed MODIS Version 6.1 Leaf Area Index Dataset and Its Evaluation for Land Surface and Climate Modeling. Remote Sens. 2023, 15, 1780. https://doi.org/10.3390/rs15071780
2. Yuan, H., Dai, Y., Xiao, Z., Ji, D., Shangguan, W., 2011. Reprocessing the MODIS Leaf Area Index Products for Land Surface and Climate Modelling. Remote Sensing of Environment, 115(5), 1171-1187. doi:10.1016/j.rse.2011.01.001

This dataset contains 75 entries detailing various health parameters of electrical cables. The data includes measurements of insulation resistance (IR), tan delta stability, delta tan delta, and tan delta at U0. Additionally, it provides partial discharge (PD) characteristics such as peak PD, inception and extinction voltages, and online PD patterns. Each cable's age from the year of installation and a health index rated out of 5 are also included.

This dataset is useful for researchers and engineers focusing on cable health assessment, maintenance scheduling, and reliability analysis in electrical power systems. Note that some columns contain missing values.

Topographic Position Index (TPI) is a topographic position classification identifying upper, middle and lower parts of the landscape. This dataset includes a mask that identifies where topographic position cannot be reliably derived in low relief areas. The TPI product was derived from Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016), which was derived from the 1 arc-second resolution SRTM data acquired by NASA in February 2000. A masked version of the TPI product was derived using the slope relief classification product. The TPI data are available at 1 arc-second and 3 arc-second resolution. The 3 arc-second resolution dataset was generated from the 1 arc-second TPI product and masked by the 3” water and ocean mask datasets.

An Environmental Quality Index (EQI) for all counties in the United States for the time period 2000-2005 was developed which incorporated data from five environmental domains: air, water, land, built, and socio-demographic. The EQI was developed in four parts: domain identification; data source identification and review; variable construction; and data reduction using principal components analysis (PCA). The methods applied provide a reproducible approach that capitalizes almost exclusively on publically-available data sources. The primary goal in creating the EQI is to use it as a composite environmental indicator for research on human health. A series of peer reviewed manuscripts utilized the EQI in examining health outcomes. This dataset is not publicly accessible because: This series of papers are considered Human health research - not to be loaded onto ScienceHub. It can be accessed through the following means: The EQI data can be accessed at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: EQI data, metadata, formats, and data dictionary all available at website. This dataset is associated with the following publications: Gray, C., L. Messer, K. Rappazzo, J. Jagai, S. Grabich, and D. Lobdell. The association between physical inactivity and obesity is modified by five domains of environmental quality in U.S. adults: A cross-sectional study. PLoS ONE. Public Library of Science, San Francisco, CA, USA, 13(8): e0203301, (2018). Patel, A., J. Jagai, L. Messer, C. Gray, K. Rappazzo, S. DeflorioBarker, and D. Lobdell. Associations between environmental quality and infant mortality in the United States, 2000-2005. Archives of Public Health. BioMed Central Ltd, London, UK, 76(60): 1, (2018). Gray, C., D. Lobdell, K. Rappazzo, Y. Jian, J. Jagai, L. Messer, A. Patel, S. Deflorio-Barker, C. Lyttle, J. Solway, and A. Rzhetsky. Associations between environmental quality and adult asthma prevalence in medical claims data. ENVIRONMENTAL RESEARCH. Elsevier B.V., Amsterdam, NETHERLANDS, 166: 529-536, (2018).

This dataset collection is a structured compilation of tables sourced from Lantmäteriet (The Land Survey), a website based in Sweden. These tables are meticulously organized into rows and columns, providing a clear presentation of related data. The dataset's relevance and value stem from its comprehensive nature, as it encapsulates several aspects within one or multiple tables. The dataset collection is designed for ease of navigation, thereby enabling efficient data analysis and extraction.

The SCImago Journal & Country Rank is a publicly available portal that includes the journals and country scientific indicators developed from the information contained in the Scopus® database (Elsevier B.V.). These indicators can be used to assess and analyze scientific domains. Journals can be compared or analysed separately.

💬Also have a look at
💡 COUNTRIES Research & Science Dataset - SCImagoJR
💡 UNIVERSITIES & Research INSTITUTIONS Rank - SCImagoIR

• Journals can be grouped by subject area (27 major thematic areas), subject category (309 specific subject categories) or by country.
• Citation data is drawn from over 34,100 titles from more than 5,000 international publishers
• This platform takes its name from the SCImago Journal Rank (SJR) indicator , developed by SCImago from the widely known algorithm Google PageRank™. This indicator shows the visibility of the journals contained in the Scopus® database from 1996.
• SCImago is a research group from the Consejo Superior de Investigaciones Científicas (CSIC), University of Granada, Extremadura, Carlos III (Madrid) and Alcalá de Henares, dedicated to information analysis, representation and retrieval by means of visualisation techniques.

☢️❓The entire dataset is obtained from public and open-access data of ScimagoJR (SCImago Journal & Country Rank)
ScimagoJR Journal Rank
SCImagoJR About Us

Available indicators:

• SJR (SCImago Journal Rank) indicator: It expresses the average number of weighted citations received in the selected year by the documents published in the selected journal in the three previous years, --i.e. weighted citations received in year X to documents published in the journal in years X-1, X-2 and X-3. See detailed description of SJR (PDF).
• H Index: The h index expresses the journal's number of articles (h) that have received at least h citations. It quantifies both journal scientific productivity and scientific impact and it is also applicable to scientists, countries, etc. (see H-index wikipedia definition)
• Total Documents: Output of the selected period. All types of documents are considered, including citable and non citable documents.
• Total Documents (3years): Published documents in the three previous years (selected year documents are excluded), i.e.when the year X is selected, then X-1, X-2 and X-3 published documents are retrieved. All types of documents are considered, including citable and non citable documents.
• Citable Documents (3 years): Number of citable documents published by a journal in the three previous years (selected year documents are excluded). Exclusively articles, reviews and conference papers are considered. Non-citable Docs. (Available in the graphics) Non-citable documents ratio in the period being considered.
• Total Cites (3years): Number of citations received in the seleted year by a journal to the documents published in the three previous years, --i.e. citations received in year X to documents published in years X-1, X-2 and X-3. All types of documents are considered.
• Cites per Document (2 years): Average citations per document in a 2 year period. It is computed considering the number of citations received by a journal in the current year to the documents published in the two previous years, --i.e. citations received in year X to documents published in years X-1 and X-2.
• Cites per Document (3 years): Average citations per document in a 3 year period. It is computed considering the number of citations received by a journal in the current year to the documents published in the three previous years, --i.e. citations received in year X to documents published in years X-1, X-2 and X-3.
• Self Cites: Number of journal's self-citations in the seleted year to its own documents published in the three previous years, --i.e. self-citations in year X to documents published in years X-1, X-2 and X-3. All types of documents are considered.
• Cited Documents: Number of documents cited at least once in the three previous years, --i.e. years X-1, X-2 and X-3
• Uncited Documents: Number of uncited documents in the three previous years, --i.e. years X-1, X-2 and X-3
• Total References: It includes all the bibliographical references in a journal in the selected period.
• References per Document:Average number of references per document in the selected year.
• % International Collaboration: Document ratio whose affiliation includes more than one country address.

Journals & Ranking

An academic journal or research journal is a periodical publication in which research articles relating to a particular academic discipline is published, according to Wikipedia. Currently, there are more than 25,000 peer-reviewed journals that are indexed in citation index databases such as Scopus and Web of Science. These indexes are ranked on the basis of various metrics such as CiteScore, H-index, etc. The metrics are calculated from yearly citation data of the journal. A lot of efforts are given to make a metric that reflects the journal's quality.

Journal Ranking Dataset

This is a comprehensive dataset on the academic journals coving their metadata information as well as citation, metrics, and ranking information. Detailed data on their subject area is also given in this dataset. The dataset is collected from the following indexing databases: - Scimago Journal Ranking - Scopus - Web of Science Master Journal List

The data is collected by scraping and then it was cleaned, details of which can be found in HERE.

Key Features

• Rank: Overall rank of journal (derived from sorted SJR index).
• Title: Name or title of journal.
• OA: Open Access or not.
• Country: Country of origin.
• SJR-index: A citation index calculated by Scimago.
• CiteScore: A citation index calculated by Scopus.
• H-index: Hirsh index, the largest number h such that at least h articles in that journal were cited at least h times each.
• Best Quartile: Top Q-index or quartile a journal has in any subject area.
• Best Categories: Subject areas with top quartile.
• Best Subject Area: Highest ranking subject area.
• Best Subject Rank: Rank of the highest ranking subject area.
• Total Docs.: Total number of documents of the journal.
• Total Docs. 3y: Total number of documents in the past 3 years.
• Total Refs.: Total number of references of the journal.
• Total Cites 3y: Total number of citations in the past 3 years.
• Citable Docs. 3y: Total number of citable documents in the past 3 years.
• Cites/Doc. 2y: Total number of citations divided by the total number of documents in the past 2 years.
• Refs./Doc.: Total number of references divided by the total number of documents.
• Publisher: Name of the publisher company of the journal.
• Core Collection: Web of Science core collection name.
• Coverage: Starting year of coverage.
• Active: Active or inactive.
• In-Press: Articles in press or not.
• ISO Language Code: Three-letter ISO 639 code for language.
• ASJC Codes: All Science Journal Classification codes for the journal.

Rest of the features provide further details on the journal's subject area or category: - Life Sciences: Top level subject area. - Social Sciences: Top level subject area. - Physical Sciences: Top level subject area. - Health Sciences: Top level subject area. - 1000 General: ASJC main category. - 1100 Agricultural and Biological Sciences: ASJC main category. - 1200 Arts and Humanities: ASJC main category. - 1300 Biochemistry, Genetics and Molecular Biology: ASJC main category. - 1400 Business, Management and Accounting: ASJC main category. - 1500 Chemical Engineering: ASJC main category. - 1600 Chemistry: ASJC main category. - 1700 Computer Science: ASJC main category. - 1800 Decision Sciences: ASJC main category. - 1900 Earth and Planetary Sciences: ASJC main category. - 2000 Economics, Econometrics and Finance: ASJC main category. - 2100 Energy: ASJC main category. - 2200 Engineering: ASJC main category. - 2300 Environmental Science: ASJC main category. - 2400 Immunology and Microbiology: ASJC main category. - 2500 Materials Science: ASJC main category. - 2600 Mathematics: ASJC main category. - 2700 Medicine: ASJC main category. - 2800 Neuroscience: ASJC main category. - 2900 Nursing: ASJC main category. - 3000 Pharmacology, Toxicology and Pharmaceutics: ASJC main category. - 3100 Physics and Astronomy: ASJC main category. - 3200 Psychology: ASJC main category. - 3300 Social Sciences: ASJC main category. - 3400 Veterinary: ASJC main category. - 3500 Dentistry: ASJC main category. - 3600 Health Professions: ASJC main category.