Please use the MESINESP2 corpus (the second edition of the shared-task) since it has a higher level of curation, quality and is organized by document type (scientific articles, patents and clinical trials).

Introduction

The Mesinesp (Spanish BioASQ track, see https://temu.bsc.es/mesinesp) development set has a total of 750 records indexed manually by seven experienced medical literature indexers. Indexing is done using DeCS codes, a sort of Spanish equivalent to MeSH terms. Records were distributed in a way that each article was annotated, at least, by two different human indexers.

The data annotation process consisted in two steps:

Manual indexing step. DeCS codes were manually assigned to each record following the DeCS manual indexing guidelines.

Manual validation and consensus. The joined set of manually indexed DeCS codes generated by both indexers were manually revised and corrections were done.

These annotations were analyzed, resulting in an agreement using the Jaccard index.

Records consisted basically in medical literature abstracts and titles from the IBECS and LILACS databases.

Zip structure The zip file contains two different development sets:

Official development set, which has the union of the annotations, with an agreement of macro = 0.6568 and micro = 0.6819. This set is composed by all the different (unique) DeCS codes that have been added by any annotator for each document; and

Core-descriptors development set, which has the intersection of the annotations, with an agreement of macro = 1.0 and micro = 1.0. This set is composed of the common DeCS codes that have been added by two or more annotators for each document.

Corpus format

Each dataset is a JSON object with one single key named "articles", which contains a list of documents. So, the raw format of the file is one line per document plus two additional lines (the first and the last) to enclose that list of documents and the expected type of data is as follows:

{"articles":[ {"abstractText":str,"db":str,"decsCodes":list,"id":str,"journal":str,"title":str,"year":int}, ... ]}

To clarify, the order of appearance of the fields in each document is as follows (note that this example it is pretty printed for readability purposes):

{ "articles": [ { "abstractText": "Content of the abstract", "db": "Name of the source database", "decsCodes": [ "code1", "code2", "code3" ], "id": "Id of the document", "journal": "Name of the journal", "title": "Title of the document", "year": 2019 } ] }

Note: The fields "db", "journal" and "year" might be null.

Copyright (c) 2020 Secretaría de Estado de Digitalización e Inteligencia Artificial

Dallas Fed Manufacturing Shipments Index in the United States increased to 15.10 points in November from 5.80 points in October of 2025. This dataset includes a chart with historical data for the United States Dallas Fed Manufacturing Shipments Index.

A monthly measure of the volume of services performed by the for-hire transportation sector. The index covers the activities of local mass transit, intercity passenger rail, and passenger air transportation.

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 data contains Index match, index match Advance

United States Import Value Index data was reported at 126.779 2015=100 in 2021. This records an increase from the previous number of 103.958 2015=100 for 2020. United States Import Value Index data is updated yearly, averaging 51.384 2015=100 from Dec 1980 (Median) to 2021, with 42 observations. The data reached an all-time high of 126.779 2015=100 in 2021 and a record low of 11.319 2015=100 in 1982. United States Import Value Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Trade Index. Import value indexes are the current value of imports (c.i.f.) converted to U.S. dollars and expressed as a percentage of the average for the base period (2015). UNCTAD's import value indexes are reported for most economies.;United Nations Conference on Trade and Development;;

ENVIRONMENTAL HEALTH HAZARD INDEXSummary

The environmental health hazard exposure index summarizes potential exposure to harmful toxins at a neighborhood level. Potential health hazards exposure is a linear combination of standardized EPA estimates of air quality carcinogenic (c), respiratory (r) and neurological (n) hazards with i indexing census tracts.

Where means and standard errors are estimated over the national distribution.

InterpretationValues are inverted and then percentile ranked nationally. Values range from 0 to 100. The higher the index value, the less exposure to toxins harmful to human health. Therefore, the higher the value, the better the environmental quality of a neighborhood, where a neighborhood is a census block-group.

Data Source: National Air Toxics Assessment (NATA) data, 2014. Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 13.

References: https://www.epa.gov/ttn/atw/natamain/

To learn more about the Environmental Health Hazard Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020

Graph and download economic data for Index of Industrial Production and Trade for United States (M1204BUSM363SNBR) from Jan 1915 to Dec 1919 about trade, IP, indexes, and USA.

The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.

Leading Economic Index Honduras increased 3.70 percent in September of 2025 over the same month in the previous year. This dataset provides - Honduras Leading Economic Index- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Office of Policy and Management (OPM) prepares the Public Investment Community (PIC) index not later than July 15 annually, pursuant to §7-545 of the Connecticut General Statutes (CGS). The PIC index measures the relative wealth and need of Connecticut’s towns by ranking them in descending order by their cumulative point allocations for: (1) per capita income; (2) adjusted equalized net grand list per capita; (3) equalized mill rate; (4) per capita aid to children receiving Temporary Family Assistance program benefits; and (5) unemployment rate. Pursuant to CGS §7-545 the PIC index includes each town that has a cumulative point ranking in the top quartile of the PIC Index (i.e. the 42 towns with the highest number of points). When a town’s ranking falls below the top quartile in a given fiscal year, the town's designation as a Public Investment Community continues for that year and the following four fiscal years. As a result, the PIC index includes certain towns carried over from previous fiscal years (indicated in the data as "grandfathered"). The PIC index determines eligibility for several financial assistance programs that various agencies administer, including: -Urban Action Bond Assistance -Small Town Economic Assistance Program -Community Economic Development Program -Residential Mortgage Guarantee Program -Education Cost Sharing -Malpractice Insurance Purchase Program -Connecticut Manufacturing Innovation Fund -Enterprise Corridor Zone Designation Most of the towns included on the PIC index are eligible to elect for assistance under the Small Town Economic Assistance Program (STEAP) in lieu of Urban Action Bond assistance, pursuant to CGS §4-66g(b). An eligible town’s legislative body (or its board of selectmen if the town’s legislative body is the town meeting) must vote to choose STEAP assistance and the town must notify OPM following the vote. STEAP election is valid for four years and the statute allows extensions for additional four-year periods.

The Case Mix Index (CMI) is the average relative DRG weight of a hospital’s inpatient discharges, calculated by summing the Medicare Severity-Diagnosis Related Group (MS-DRG) weight for each discharge and dividing the total by the number of discharges. The CMI reflects the diversity, clinical complexity, and resource needs of all the patients in the hospital. A higher CMI indicates a more complex and resource-intensive case load. Although the MS-DRG weights, provided by the Centers for Medicare & Medicaid Services (CMS), were designed for the Medicare population, they are applied here to all discharges regardless of payer. Note: It is not meaningful to add the CMI values together.

Dataset Name: City Happiness Index

Dataset Description:

This dataset and the related codes are entirely prepared, original, and exclusive by Emirhan BULUT. The dataset includes crucial features and measurements from various cities around the world, focusing on factors that may affect the overall happiness score of each city. By analyzing these factors, we aim to gain insights into the living conditions and satisfaction of the population in urban environments.

The dataset consists of the following features:

• City: Name of the city.
• Month: The month in which the data is recorded.
• Year: The year in which the data is recorded.
• Decibel_Level: Average noise levels in decibels, indicating the auditory comfort of the citizens.
• Traffic_Density: Level of traffic density (Low, Medium, High, Very High), which might impact citizens' daily commute and stress levels.
• Green_Space_Area: Percentage of green spaces in the city, positively contributing to the mental well-being and relaxation of the inhabitants.
• Air_Quality_Index: Index measuring the quality of air, a crucial aspect affecting citizens' health and overall satisfaction.
• Happiness_Score: The average happiness score of the city (on a 1-10 scale), representing the subjective well-being of the population.
• Cost_of_Living_Index: Index measuring the cost of living in the city (relative to a reference city), which could impact the financial satisfaction of the citizens.
• Healthcare_Index: Index measuring the quality of healthcare in the city, an essential component of the population's well-being and contentment.

With these features, the dataset aims to analyze and understand the relationship between various urban factors and the happiness of a city's population. The developed Deep Q-Network model, PIYAAI_2, is designed to learn from this data to provide accurate predictions in future scenarios. Using Reinforcement Learning, the model is expected to improve its performance over time as it learns from new data and adapts to changes in the environment.

The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.

The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.

This is not the historical change in the h-index. Instead, it calculates the h-index by articles before a given year, not the citations before that year.

CFNAI Sales Orders and Inventories Index in the United States increased to 0 percent in August from -0.02 percent in July of 2025. This dataset includes a chart with historical data for the United States CFNAI Sales, Orders and Inventories Index.

United States New York Stock Exchange: Index: US 100 Index data was reported at 18,140.503 NA in Nov 2025. This records an increase from the previous number of 17,877.968 NA for Oct 2025. United States New York Stock Exchange: Index: US 100 Index data is updated monthly, averaging 9,534.600 NA from Jan 2012 (Median) to Nov 2025, with 167 observations. The data reached an all-time high of 18,140.503 NA in Nov 2025 and a record low of 5,695.000 NA in May 2012. United States New York Stock Exchange: Index: US 100 Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: Monthly.

The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.

This dataset provides values for INDEX reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This data set contains vector polygons representing the boundaries of all hardcopy cartographic products produced as part of the Environmental Sensitivity Index (ESI) for Alabama. This data set comprises a portion of the ESI data for Alabama. ESI data characterize the marine and coastal environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources.