This dataset is a list of 100 manually collected URLs of web pages that describe, contain, or link to (research) datasets. The list was annotated and categorised with the following fields:

• URL: a URL to a page that describes, contains, or links (research) datasets
• URL to dataset page: either the same URL or, for URLs that point to repository-like systems, a sub-page specific to a few datasets
• type of page: 0 = list of data sets, 1 = description of a data set, 2 = reference to data sets, 3 = project website, 4 = research data repository, 5 = miscellaneous; this field can contain several values
• number of datasets: how many datasets were found on the dataset page
• file formats: which file formats were found on the dataset page (e.g., jpg, txt, csv)
• types of datasets: which type of data were found on the dataset page (e.g., text, image, video, table)
• available metadata: which metadata for the datasets were found on the dataset page (e.g., title, description, year)
  

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Tabulated data with information on crop types and areas in each river reach in the Hunter river system model. This dataset was derived by the Bioregional Assessment Programme from existing catchment land use data obtained from the Australian Bureau of Agricultural and Resource Economics and Sciences.

Purpose

The AWRA-R river model needs details of irrigated areas and crop types in each river reach in which irrigation is present in order to determine areal extent and crop factors of the most common crop types .

Dataset History

Areas and crop types are obtained from the Catchment scale Land Use Management (CLUM). The dataset was clipped using catchment boundaries defined by the AWRA-R modelling domain and the information summarised by reach in order to determine crop types and associated crop factors . Irrigation areas are determined using the first level classification in the CLUM dataset which describes the main land use type. Crop types were determined from the third level classification, which provides detailed information on crop types.

Dataset Citation

Bioregional Assessment Programme (2016) HUN AWRA-R Irrigation Area Extents and Crop Types v01. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/90013748-6247-47ad-bddb-07a9c2d857cd.

Dataset Ancestors

• Derived From Gippsland Project boundary

• Derived From Bioregional Assessment areas v04

• Derived From National Surface Water sites Hydstra

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From HUN AWRA-R calibration catchments v01

• Derived From Bioregional Assessment areas v05

• Derived From HUN AWRA-R calibration nodes v01

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Bioregional_Assessment_Programme_Catchment Scale Land Use of Australia - 2014

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From GEODATA TOPO 250K Series 3

• Derived From Victoria - Seamless Geology 2014

• Derived From Geological Provinces - Full Extent

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

Get the Data

The dataset supports various deep learning applications, including facial anomaly detection, tissue segmentation, and 3D modeling of facial anatomy. With high-resolution sagittal and axial slices, it is ideal for training AI models aimed at accurate facial analysis.

💵 Access the Dataset: Access to the full dataset is available upon request. Contact us at contact@human-ai-ze.com or visit HumanAIze to discuss pricing and requirements.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F24887857%2Fdf0cb7cc972acc877c29326e2a3a99d8%2Ffcial.png?generation=1739814150106946&alt=media" alt="">

Content

The dataset includes data that showcases the diversity and complexity of facial MRI imaging, suitable for machine learning models and medical analysis. It includes:

• Sagittal and axial MRI slices: Key anatomical regions of the face.
• 3D models: Useful for volumetric and surgical planning.
• Clinical data summaries: Including information about patient demographics and scan contexts.

Medical Reports Include the Following Data:

• Type of study
• MRI machine used (e.g., Philips Ingenia 3.0T)
• Patient demographics (age, sex, medical history)
• Anamnesis (patient complaints and symptoms)
• Findings: Detailed imaging observations
• Preliminary diagnosis and clinical recommendations

All data is anonymized to ensure privacy and complies with publication consent regulations.

Potential Applications

• Anomaly detection: Facial deformities, soft tissue damage, and bone irregularities
• Segmentation models: Soft tissue, muscles, bones, and key facial landmarks
• 3D facial reconstruction: AI-powered visualization for surgery planning and diagnostics

Sample Preview

The dataset provides a sample from one patient, showcasing the diversity of the full dataset. It contains the following files for exploration:
- DICOM slices with 100 frames
- 3D representation of the facial structure
- CSV file listing the scan characteristics

🌐 HumanAIze specializes in high-quality datasets, AI/ML data curation, and annotation services. Contact us today for custom solutions tailored to your projects.

The global graph database market size reached USD 2.0 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 8.6 Billion by 2033, exhibiting a growth rate (CAGR) of 17.57% during 2025-2033. The increasing adoption of graph databases in cybersecurity for threat detection and network analysis, growing demand for real-time analytics and AI-driven insights, and expanding application in industries, such as healthcare and finance, for data integration and personalized services, are some of the key factors catalyzing the market growth.

IMARC Group provides an analysis of the key trends in each segment of the global graph database market report, along with forecasts at the global, regional, and country levels from 2025-2033. Our report has categorized the market based on component, type of database, analysis type, deployment model, application, and industry vertical.

Freebase is amongst the largest public cross-domain knowledge graphs. It possesses three main data modeling idiosyncrasies. It has a strong type system; its properties are purposefully represented in reverse pairs; and it uses mediator objects to represent multiary relationships. These design choices are important in modeling the real-world. But they also pose nontrivial challenges in research of embedding models for knowledge graph completion, especially when models are developed and evaluated agnostically of these idiosyncrasies. We make available several variants of the Freebase dataset by inclusion and exclusion of these data modeling idiosyncrasies. This is the first-ever publicly available full-scale Freebase dataset that has gone through proper preparation.

Dataset Details

The dataset consists of the four variants of Freebase dataset as well as related mapping/support files. For each variant, we made three kinds of files available:

• Subject matter triples file
  • fb+/-CVT+/-REV One folder for each variant. In each folder there are 5 files: train.txt, valid.txt, test.txt, entity2id.txt, relation2id.txt Subject matter triples are the triples belong to subject matters domains—domains describing real-world facts.
    • Example of a row in train.txt, valid.txt, and test.txt:
      • 2, 192, 0
    • Example of a row in entity2id.txt:
      • /g/112yfy2xr, 2
    • Example of a row in relation2id.txt:
      • /music/album/release_type, 192
    • Explaination
      • "/g/112yfy2xr" and "/m/02lx2r" are the MID of the subject entity and object entity, respectively. "/music/album/release_type" is the realtionship between the two entities. 2, 192, and 0 are the IDs assigned by the authors to the objects.
• Type system file
  • freebase_endtypes: Each row maps an edge type to its required subject type and object type.
    • Example
      • 92, 47178872, 90
    • Explanation
      • "92" and "90" are the type id of the subject and object which has the relationship id "47178872".
• Metadata files
  • object_types: Each row maps the MID of a Freebase object to a type it belongs to.
    • Example
      • /g/11b41c22g, /type/object/type, /people/person
    • Explanation
      • The entity with MID "/g/11b41c22g" has a type "/people/person"
  • object_names: Each row maps the MID of a Freebase object to its textual label.
    • Example
      • /g/11b78qtr5m, /type/object/name, "Viroliano Tries Jazz"@en
    • Explanation
      • The entity with MID "/g/11b78qtr5m" has name "Viroliano Tries Jazz" in English.
  • object_ids: Each row maps the MID of a Freebase object to its user-friendly identifier.
    • Example
      • /m/05v3y9r, /type/object/id, "/music/live_album/concert"
    • Explanation
      • The entity with MID "/m/05v3y9r" can be interpreted by human as a music concert live album.
  • domains_id_label: Each row maps the MID of a Freebase domain to its label.
    • Example
      • /m/05v4pmy, geology, 77
    • Explanation
      • The object with MID "/m/05v4pmy" in Freebase is the domain "geology", and has id "77" in our dataset.
  • types_id_label: Each row maps the MID of a Freebase type to its label.
    • Example
      • /m/01xljxh, /government/political_party, 147
    • Explanation
      • The object with MID "/m/01xljxh" in Freebase is the type "/government/political_party", and has id "147" in our dataset.
  • entities_id_label: Each row maps the MID of a Freebase entity to its label.
    • Example
      • /g/11b78qtr5m, Viroliano Tries Jazz, 2234
    • Explanation
      • The entity with MID "/g/11b78qtr5m" in Freebase is "Viroliano Tries Jazz", and has id "2234" in our dataset.
    • properties_id_label: Each row maps the MID of a Freebase property to its label.
      • Example
        • /m/010h8tp2, /comedy/comedy_group/members, 47178867
      • Explanation
        • The object with MID "/m/010h8tp2" in Freebase is a property(relation/edge), it has label "/comedy/comedy_group/members" and has id "47178867" in our dataset.
    • uri_original2simplified and uri_simplified2original: The mapping between original URI and simplified URI and the mapping between simplified URI and original URI repectively.
      • Example
        • uri_original2simplified
          • "http://rdf.freebase.com/ns/type.property.unique": "/type/property/unique"
        • uri_simplified2original
          • "/type/property/unique": "http://rdf.freebase.com/ns/type.property.unique"
      • Explanation
        • The URI "http://rdf.freebase.com/ns/type.property.unique" in the original Freebase RDF dataset is simplified into "/type/property/unique" in our dataset.
        • The identifier "/type/property/unique" in our dataset has URI http://rdf.freebase.com/ns/type.property.unique in the original Freebase RDF dataset.

Yu-Gi-Oh! Card Dataset Description

This dataset contains comprehensive information about Yu-Gi-Oh! collectible card game cards. It consists of ten distinct CSV files, each with a variety of information about different types of cards. Here are the key details about the CSV files and the included columns:

1. all_cards.csv - This file contains general information about all cards.
2. dark_cards.csv - Cards with the "Dark" attribute.
3. divine_cards.csv - Cards with the "Divine" attribute.
4. earth_cards.csv - Cards with the "Earth" attribute.
5. fire_cards.csv - Cards with the "Fire" attribute.
6. light_cards.csv - Cards with the "Light" attribute.
7. monster_cards.csv - Monster type cards.
8. spell_trap_cards.csv - Spell and Trap type cards.
9. water_cards.csv - Cards with the "Water" attribute.
10. wind_cards.csv - Cards with the "Wind" attribute.

Each CSV file includes the following columns:

• id: A unique identifier for each card.
• name: The name of the card.
• type: The card type, which can be "Monster," "Spell," or "Trap."
• frameType: The frame type of the card, indicating whether it's a normal card, equipment, or continuous, among others.
• description: A brief description of the card's abilities or effects.
• level: The card's level (relevant for monsters only).
• atk: The card's Attack score (relevant for monsters only).
• def: The card's Defense score (relevant for monsters only).
• race: The card's race (e.g., "Aqua," "Beast," etc.).
• attribute: The card's attribute (e.g., "EARTH," "FIRE," etc.).
• archetype: The card's archetype, if applicable.

This dataset is valuable for Yu-Gi-Oh! enthusiasts looking to perform analyses, create applications, or develop strategies based on the detailed information of these cards. The individual files allow for the analysis of cards with specific attributes, types, or archetypes, facilitating study and strategy planning in the game.

Please remember that data accuracy and completeness are essential for any Yu-Gi-Oh! related project, and this dataset appears to be a useful resource for that purpose.

New version 2.0.0 with majors change

For free and complete informations concerning CASSMIR datasets, please visit our website (in French).

The CASSMIR database (Contribution to the Spatial and Sociological Analysis of Residential Real Estate Markets) is a spatial and population datasets on housing property market of the Parisian metropolitan area, from 1996 to 2018. The indicators in the CASSMIR database cover four "thematic areas of investigation" : prices, socio-demographic profile of buyers and sellers, purchasing regimes and types of property transfers and types of real estate. These indicators characterize spatial units at three scales (communal level, 1km grid and 200m grid) and population groups of buyers and sellers declined according to social, generational and gender criteria. The delivery of the database follows a series of matching and aggregation of individual data from two original databases : a database on real estate transactions (BIEN database) and a database on first-time buyer investments (PTZ database). CASSMIR delivers aggregated data (with nearly 350 variables) in open access for non-commercial use.

This repository consists of sevenfiles.

"CASSMIR_SpatialDataBase" is a Geopackage file, it lists all the data aggregated to spatial units of reference. It is composed of three layers that correspond to the geographical scale of aggregation: at a communal level, a grid of one kilometer on each side and a grid of two hundred meters on each side.

"CASSMIR_GroupesPopDataBase" is a .csv file, it lists all the data aggregated to population groups of reference. There are three types of population groups : groups referenced by the social position of the buyers/sellers (social group), groups referenced by the age group to which the buyers/sellers belong (generational group), groups referenced by the sex of the buyers/sellers (gender group).

Two metadata files (.csv) lists the metadata of the indicators made available. They are systematically structured as follows :

• Id_var: the identifier of the variable contained in "CASSMIR_SpatialDataBase" or "CASSMIR_GroupesPopDataBase"
• Unite d'observation des variables descriptives : descriptive units of observation (Prices, buyers, sellers...)
• Type d'information : precision on the type of information
• Label : Description of the contents of the variable
• Indicator_Group: The group of indicators to which the variable relates (prices, socio-demographics indicators of buyers and sellers...)
• Unit : The unit of measurement of the variable
• Spatial_Availability : A precision on the availability of the variable in the spatial database (communes, 1 km grid and 200m grid)
• GroupesPop_Availability : A precision on the availability of the variable in the population groupes database (Social, generational , gender)
• Data_Source: The main origin of the data (INSEE, BIEN and/or PTZ)
• Remarques : possible remarks on the construction of the variable

"BIENSampleForTest" and "PTZSampleForTest" are two .txt files which restore a sample of individual data from each of the original databases. All data were anonymized and the values randomized. These two files are specifically dedicated to reproducing the different stages of processing that lead to the production of the CASSMIR files ("CASSMIR_SpatialDataBase" or "CASSMIR_GroupesPopDataBase") and cannot be used in any other way.

"LEXIQUE" is a glossary of terms used to name the variables (.csv).

The creation of the database was funded by the National Reseach Agency (ANR WIsDHoM https://anr.fr/Projet-ANR-18-CE41-0004).

All CASSMIR documentation (in French) and R codes are accessible via the Gitlab repository at the following address : https://gitlab.huma-num.fr/tlecorre/cassmir.git

METADATA :

• Licence

This dataset is registered under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. You are free to copy, distribute, transmit, and adapt the data, provided that you give credit to the CASSMIR data base and specify the original source of the data. If you modify or use the data in other derivative works, you may distribute them only under the same license. You may not make commercial use of this database, nor may you use it for any purpose other than scientific research.

• Citation standard

- Figures: (CC - CASSMIR database, indicator(s) constructed from XXX data)

- Bibliography : Productions that use the CASSMIR database must reference the dataset and the data paper.

Dataset: Le Corre T., 2020, CASSMIR (Version 2.0.0) [Data set], Zenodo. http://doi.org/10.5281/zenodo.4497219

Data paper: Thibault Le Corre, « Une base de données pour étudier vingt années de dynamiques du marché immobilier résidentiel en Île-de-France », Cybergeo: European Journal of Geography [En ligne], Data papers, article No.992, mis en ligne le 09 août 2021. URL : http://journals.openedition.org/cybergeo/37430 ; DOI : https://doi.org/10.4000/cybergeo.37430

• Data paper title

"Une base de données pour étudier vingt années de dynamiques du marché immobilier en Île-de-France"

• Author

Thibault Le Corre

• Keywords

Housing market, data base, Île-de-France, spatio-temporal dynamics

• Related Publication

DOI : https://doi.org/10.4000/cybergeo.37430

• Language

French

• Time Period Covered

The time period covered by the indicators in the database depends on the data sources used, respectively:
For data from BIEN: 1996, 1999, 2003-2012, 2015, 2018
For data from PTZ: 1996-2016

• Kind of data

Nature of data submitted

• vector: Vector data

• grid: Data mesh

• code: programming code (see the website or GitLab of the project)

• Data Sources

Base BIEN

Base PTZ

• Geographical Coverage

Île-de-France region

• Geographical Unit

Municipalities and grid mesh elements (1km side grid and 200 side grid) concerned by real estate transactions

• Geographic Bounding Box

Reference Coordinate System (RCS): EPSG 2154 RGF93/Lambert 93.

- Xmin : 586421.7
- Xmax : 741205.6
- Ymin : 6780020
- Ymax : 6905324

• Type of article

Data Paper

Analysis of ‘Pokemon with stats’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/abcsds/pokemon on 28 January 2022.

--- Dataset description provided by original source is as follows ---

This data set includes 721 Pokemon, including their number, name, first and second type, and basic stats: HP, Attack, Defense, Special Attack, Special Defense, and Speed. It has been of great use when teaching statistics to kids. With certain types you can also give a geeky introduction to machine learning.

This are the raw attributes that are used for calculating how much damage an attack will do in the games. This dataset is about the pokemon games (NOT pokemon cards or Pokemon Go).

The data as described by Myles O'Neill is:

• #: ID for each pokemon
• Name: Name of each pokemon
• Type 1: Each pokemon has a type, this determines weakness/resistance to attacks
• Type 2: Some pokemon are dual type and have 2
• Total: sum of all stats that come after this, a general guide to how strong a pokemon is
• HP: hit points, or health, defines how much damage a pokemon can withstand before fainting
• Attack: the base modifier for normal attacks (eg. Scratch, Punch)
• Defense: the base damage resistance against normal attacks
• SP Atk: special attack, the base modifier for special attacks (e.g. fire blast, bubble beam)
• SP Def: the base damage resistance against special attacks
• Speed: determines which pokemon attacks first each round

The data for this table has been acquired from several different sites, including:

• pokemon.com
• pokemondb
• bulbapedia

One question has been answered with this database: The type of a pokemon cannot be inferred only by it's Attack and Deffence. It would be worthy to find which two variables can define the type of a pokemon, if any. Two variables can be plotted in a 2D space, and used as an example for machine learning. This could mean the creation of a visual example any geeky Machine Learning class would love.

--- Original source retains full ownership of the source dataset ---

AQA's NZ HISTORICAL quarry database.

Developed with support from GNS Science.

Quarry data is updated periodically. AQA accepts no liability for incorrect data.

Please email any corrections to tech@aqa.org.nz

​

Q_INDEX: Unique Identifier – DO NOT CHANGE

NAME: Quarry Name

CLASS: Type of Quarry. Options:

• Commercial – open for general sales
• Farm – on-farm, typically a permitted activity if material extracted doesn’t leave the property
• Forestry – used exclusively for forestry roading/structures and located in a forest
• Satellite site - a different extraction area within the same quarry operation
• Other
• REVIEW - requires input from the technical team

ACTIVITY: Indicator of the level of activity at the quarry. Options:

• Historic – has not been used for 5+ years

PRODUCTION_CLASS: Annualised production estimate. Options:

• Farm (<10,000tpa)
• Small (10,000-50,000tpa)
• Medium (50,000-200,000tpa)
• Large (200,000-1,000,000tpa)
• Super (>1,000,000tpa)

OPERATOR: Company operating the quarry

COMMODITY_TYPE: Rock type – taken from the GNS QMAP

COMMODITY_GROUP: Type of quarry. Options:

• Hard Rock
• Gravel
• Sand
• Mineral

REVIEW_STATUS: Indicator of whether the site’s information has been checked by the technical team. Options:

• OPEN – checked and OK to release for public use
• CLOSED – not for public release
• REVIEW – OK for public release but requires further information or reviewing
• DELETE – used by the technical team to delete a site that isn’t and wasn’t ever a quarry. Note that HISTORIC sites are not deleted.

NZTM_EAST: Easting coordinate in NZGD 2000 New Zealand Transverse Mercator projection

NZTM_NORTH: Northing coordinate in NZGD 2000 New Zealand Transverse Mercator projection

WGS84_LONG: Longitude in WGS84 projection (used by Google Earth)

WGS84_LAT: Latitude in WGS84 projection (used by Google Earth)

TERRAUTH: NZ Territorial Authority in which the quarry land is situated.

REGION: NZ Regional Authority in which the quarry land is situated.

QMAP_MAPNAME: QMAP Rock type indicated for the site. E.g. “Manaia Hill Group sandstone and siltstone (Waipapa Composite Terrane)”

QMAP_LITHO: Rock type general classification (what a quarry would describe their rock as) e.g. “sandstone, siltstone”

Note: “sandstone” is used as the preferred geological term instead of “greywacke”.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.0605 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0114 and 0.0178 (in million kms), corressponding to 18.8736% and 29.4628% respectively of the total road length in the dataset region. 0.0313 million km or 51.6635% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0 million km of information (corressponding to 0.137% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

BackgroundThe development of mucosal adenovirus (Ad) vaccine vectors is considered one of the next frontiers to protect vulnerable patients from respiratory and gastrointestinal pathogens. An efficient delivery to or through the oral cavity necessitates a thorough understanding of Ad interactions with saliva for oral, buccal or sublingual vaccine delivery, which could additionally prove instrumental in the containment of natural Ad infections but remains unexplored. Therefore, we investigated the influence of saliva on Ad infectivity, emphasizing its intrinsic antiviral role against particular Ad types in various epithelial cell cultures.MethodsA saliva pool was created from healthy donors (n=16) and incubated with ChAdOx1 or human Ads from 20 different types prior to infection of human immortalized epithelial cells. All human Ads used were replication-competent and expressed a GLN cassette containing a green-fluorescent protein, nano-luciferase, and neomycin resistance. Loss-of-function experiments were conducted by immunoprecipitation or enzymatic digestion of specific saliva components to decipher related mechanisms.ResultsTemporal and inter-individual variability in saliva samples were observed, validating the use of a saliva pool to represent the population. Saliva strongly influenced Ad infectivity, in general through inhibiting species B types and enhancing species D and E Ads, that include the vaccine vector platforms Ad26 and ChAdOx1. Interestingly, Ad20 presented the highest infectivity enhancement, as well as superior to average salivary mucus crossing rates. Furthermore, saliva immunoglobulins and human neutrophil peptides marginally influenced the Ad infectivity, while sialic acid inhibited all tested Ad types.ConclusionSaliva may have a protective role against infection by certain types of Ads. This discovery highlights a potential limitation in the efficacy of next-generation oral Ad vaccine vectors. Consequently, our study underscores the importance of identifying and utilizing saliva-resistant Ad vectors to optimize Ad-based vaccination strategies.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.4063 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0858 and 0.0428 (in million kms), corressponding to 21.1121% and 10.5392% respectively of the total road length in the dataset region. 0.2777 million km or 68.3487% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0032 million km of information (corressponding to 1.1438% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

Abstract

The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from the dataset GLO Receptors 20150518. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

This dataset contains contains an excel spreadsheet that tabulates the percentage length of each river type in the Gloucester subregion.

Dataset History

The length of each river type in GLO (see http://badms.csiro.au/Home/Search?datasetMetadataId=e5931331-5b46-4bbe-a252-0c1fa8947ab9) was summed and divided by the total river length and the result multiplied by 100 to calculate the percentage river length.

Dataset Citation

Bioregional Assessment Programme (XXXX) GLO Surface Water Receptors Landscape Types 20150611. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/64aff060-536d-432a-89ee-48a0bc7c3f1f.

Dataset Ancestors

• Derived From Standard Instrument Local Environmental Plan (LEP) - Heritage (HER) (NSW)

• Derived From NSW Office of Water GW licence extract linked to spatial locations - GLO v5 UID elements 27032014

• Derived From Gloucester digitised coal mine boundaries

• Derived From Groundwater Dependent Ecosystems supplied by the NSW Office of Water on 13/05/2014

• Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv4 UID 14032014

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From Asset database for the Gloucester subregion on 12 September 2014

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From National Groundwater Information System (NGIS) v1.1

• Derived From GLO Receptors 20150518

• Derived From Groundwater Entitlement Data GLO NSW Office of Water 20150320 PersRemoved

• Derived From Geofabric Surface Cartography - V2.1

• Derived From Groundwater Entitlement Data Gloucester - NSW Office of Water 20150320

• Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From EIS Gloucester Coal 2010

• Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only)

• Derived From Asset database for the Gloucester subregion on 28 May 2015

• Derived From NSW Office of Water GW licence extract linked to spatial locations GLOv3 12032014

• Derived From EIS for Rocky Hill Coal Project 2013

• Derived From National Heritage List Spatial Database (NHL) (v2.1)

• Derived From Asset database for the Gloucester subregion on 8 April 2015

• Derived From Gloucester - Additional assets from local councils

• Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions

• Derived From Asset database for the Gloucester subregion on 29 August 2014

• Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public)

• Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 - External Restricted

• Derived From Groundwater Modelling Report for Stratford Coal Mine

• Derived From Groundwater Economic Assets GLO 20150326

• Derived From NSW Office of Water Groundwater Licence Extract Gloucester - Oct 2013

• Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases

• Derived From Freshwater Fish Biodiversity Hotspots

• Derived From NSW Office of Water Groundwater licence extract linked to spatial locations GLOv2 19022014

• Derived From Australia - Species of National Environmental Significance Database

• Derived From Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) Internal

• Derived From NSW Office of Water Groundwater Entitlements Spatial Locations

• Derived From Report for Director Generals Requirement Rocky Hill Project 2012

• Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release)

The Roads database includes an inventory of road assets (roadways, blocks, intersections, sidewalks, curbs) with a spatial representation and various attached information. Aggregate pavement-type road assets represent carriageways located in the public domain and which are part of the local or arterial road network. Aggregate pavements are represented by polygons that are aggregated by type of use. Among the information associated with a roadway-type object is the date of construction, the date of resurfacing, the date of survey, the date of survey, the materials of the pavement, the type of foundation, the presence of bicycle lane, use, etc. island-type road assets represent malls located in the public domain and which are juxtaposed to the local or arterial road network. The islands are represented by polygons that are differentiated by their configuration. Among the information associated with an island-type object is the date of construction, the date of survey, the materials of the block and the border, the presence of trees, the type of block, etc. intersection-type road assets represent the intersections of motorways located in the public domain and which are part of the local or arterial road network. Intersections are represented by polygons that are cut according to the number of traffic axes. Information associated with an intersecting object includes the construction date, resurfacing date, survey date, survey date, intersection materials, foundation type, bike lane presence, etc. sidewalk-type road assets represent sidewalks and curbs juxtaposed with roadways in the public domain that are part of the local or arterial road network. Sidewalks and curbs are represented by polygons differentiated by category and type. Among the information associated with a sidewalk-type object is the construction date, the survey date, the type of sidewalk and curb, the materials of the sidewalk, the border and the developed strip, the presence of trees, the presence of a projection, the presence of a bicycle path, the use, etc. zone-type road assets represent the regions located between other road assets and which do not not part of the local or arterial road network. The areas are represented by polygons. Among the information associated with a zone-type object is the type of zone, etc. The data is also available in separate sets on the portal to support several uses: - Roadway and intersection - Sidewalk and islet - Off-street zone - Sidewalk and block Warnings - The data released on road assets are those in the possession of the City's geomatics team and are not necessarily up to date throughout the country. - The data disseminated on road assets are provided for information purposes only and should not be used for the purposes of designing or carrying out works or for the location of assets.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Strong town. The dataset can be utilized to gain insights into gender-based income distribution within the Strong town population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Strong town, among individuals aged 15 years and older with income, there were 411 men and 381 women in the workforce. Among them, 187 men were engaged in full-time, year-round employment, while 119 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 18.72% fell within the income range of under $24,999, while 22.69% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 9.09% of men in full-time roles earned incomes exceeding $100,000, while 16.81% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Strong town median household income by race. You can refer the same here

NOTICE TO PROVISIONAL 2023 LAND USE DATA USERS: Please note that on December 6, 2024 the Department of Water Resources (DWR) published the Provisional 2023 Statewide Crop Mapping dataset. The link for the shapefile format of the data mistakenly linked to the wrong dataset. The link was updated with the appropriate data on January 27, 2025. If you downloaded the Provisional 2023 Statewide Crop Mapping dataset in shapefile format between December 6, 2024 and January 27, we encourage you to redownload the data. The Map Service and Geodatabase formats were correct as posted on December 06, 2024.

Thank you for your interest in DWR land use datasets.

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 1.8582 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.1766 and 0.1278 (in million kms), corressponding to 9.5052% and 6.877% respectively of the total road length in the dataset region. 1.5538 million km or 83.6178% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0237 million km of information (corressponding to 1.5266% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Beverly. The dataset can be utilized to gain insights into gender-based income distribution within the Beverly population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Beverly, among individuals aged 15 years and older with income, there were 15,516 men and 17,512 women in the workforce. Among them, 8,842 men were engaged in full-time, year-round employment, while 6,848 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 3.06% fell within the income range of under $24,999, while 5.70% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 45.13% of men in full-time roles earned incomes exceeding $100,000, while 32.81% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Beverly median household income by race. You can refer the same here

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.0497 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0117 and 0.0111 (in million kms), corressponding to 23.4856% and 22.3095% respectively of the total road length in the dataset region. 0.0269 million km or 54.2049% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0008 million km of information (corressponding to 3.0011% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

This database collects the chloride ion diffusion coefficient measured by RCM method (including the method adopted by the Nordic standard NT Build 492, the IBAC method of Germany's Anchen University of technology, as well as the methods adopted by China's GB/T 50082-2009 and JTG/T B07-01-2006); The curing method is standard curing., In order to investigate the influences of cement types on chloride diffusion coefï¬ cient of concrete, a total of 790 sets of experimental data of chloride diffusion coefï¬ cient (DRCM;28) tested by the RCM method at the reference period of 28 days were collected from reference based on the following criteria: cement type are the ordinary Portland cement (OPC) or the Portland cement (PC); This database collects the chloride ion diffusion coefficient measured by RCM method (including the method adopted by the Nordic standard NT Build 492, the IBAC method of Germany's Anchen University of technology, as well as the methods adopted by China's GB/T 50082-2009 and JTG/T B07-01-2006); The curing method is standard curing., , # Experimental data for chloride diffusion coefficient of concrete by rapid chloride migration test

The aim of this study is to investigate the inuences of cement types on chloride diffusion coefficient of concrete and develop a prediction model for chloride diffusion coefficient of concrete in terms of material parameters including "Type of cement", "Amount of fly ash", "Amount of slag", “Grade of fly ash†and "Water-binder ratio".

Description of the data and file structure

The dataset contains a total of 4 tables and 581 sets of data. Tables 1 and 2 provide experimental data for ordinary concrete (OPC). Table 3 and Table 4 are the experimental data of concrete mixed with fly ash (FA) and slag (SG). Table 5 is the experimental data of concrete mixed with fly ash (FA).

Variables and Definitions in tables 1~5

Reference: represents the reference of the data source Type of cement: represents the type of cement in concrete which include POP, and P PO: represents ord...