Dig into granular industry data: what it is, how to use it and why you should pay attention to it.

The FR 2510 will collect granular exposure data on the assets, liabilities, and off-balance sheet holdings of U.S. G-SIBs (Global Systemically Important Banks), providing breakdowns by instrument, currency, maturity, and sector. The FR 2510 will also collect data covering detailed positions for each U.S. G-SIB’s top 35 countries of exposure, on an immediate-counterparty basis, as reported in the consolidated Country Exposure Report (FFIEC 009; OMB No. 7100-0035), broken out by instrument and counterparty sector, with limited further breakouts by remaining maturity, subject to a $2 billion minimum threshold for country exposure. Further, the FR 2510 will collect information on financial derivatives by instrument type and foreign exchange derivatives by currency. The FR 2510 will allow the Federal Reserve to conduct a more complete balance sheet analysis of U.S. G-SIBs. Additionally, the FR 2510 will provide the Federal Reserve with valuable systemic information through the collection of more granular data regarding common or correlated exposures and funding dependencies than is currently collected by existing reports by providing more information about U.S. G-SIBs’ consolidated exposures and funding positions to different countries according to instrument, counterparty sector, currency and remaining maturity.

Explore Granular import export trade data. Find top buyers, suppliers, HS codes, ports, & market trends to make smarter, data-driven trade decisions.

Granular SKU-level transaction data from Measurable AI's proprietary email receipt panel across e-commerce companies in the emerging markets.

Our data is attained with consumer consent from our two consumer apps. We then aggregate and anonymize all the metrics across our panel to produce consumer insights for our end users. Our datasets are available on a granular and aggregate level.

Key clients range from the e-commerce companies themselves, buyside firms, financial institutions, consultancies, market research agencies and academia.

The "Wikipedia Category Granularity (WikiGrain)" data consists of three files that contain information about articles of the English-language version of Wikipedia (https://en.wikipedia.org).

The data has been generated from the database dump dated 20 October 2016 provided by the Wikimedia foundation licensed under the GNU Free Documentation License (GFDL) and the Creative Commons Attribution-Share-Alike 3.0 License.

WikiGrain provides information on all 5,006,601 Wikipedia articles (that is, pages in Namespace 0 that are not redirects) that are assigned to at least one category.

The WikiGrain Data is analyzed in the paper

Jürgen Lerner and Alessandro Lomi: Knowledge categorization affects popularity and quality of Wikipedia articles. PLoS ONE, 13(1):e0190674, 2018.

===============================================================
Individual files (tables in comma-separated-values-format):

---------------------------------------------------------------
* article_info.csv contains the following variables:

- "id"
(integer) Unique identifier for articles; identical with the page_id in the Wikipedia database.

- "granularity"
(decimal) The granularity of an article A is defined to be the average (mean) granularity of the categories of A, where the granularity of a category C is the shortest path distance in the parent-child subcategory network from the root category (Category:Articles) to C. Higher granularity values indicate articles whose topics are less general, narrower, more specific.

- "is.FA"
(boolean) True ('1') if the article is a featured article; false ('0') else.

- "is.FA.or.GA"
(boolean) True ('1') if the article is a featured article or a good article; false ('0') else.

- "is.top.importance"
(boolean) True ('1') if the article is listed as a top importance article by at least one WikiProject; false ('0') else.

- "number.of.revisions"
(integer) Number of times a new version of the article has been uploaded.

---------------------------------------------------------------
* article_to_tlc.csv
is a list of links from articles to the closest top-level categories (TLC) they are contained in. We say that an article A is a member of a TLC C if A is in a category that is a descendant of C and the distance from C to A (measured by the number of parent-child category links) is minimal over all TLC. An article can thus be member of several TLC.
The file contains the following variables:

- "id"
(integer) Unique identifier for articles; identical with the page_id in the Wikipedia database.

- "id.of.tlc"
(integer) Unique identifier for TLC in which the article is contained; identical with the page_id in the Wikipedia database.

- "title.of.tlc"
(string) Title of the TLC in which the article is contained.

---------------------------------------------------------------
* article_info_normalized.csv
contains more variables associated with articles than article_info.csv. All variables, except "id" and "is.FA" are normalized to standard deviation equal to one. Variables whose name has prefix "log1p." have been transformed by the mapping x --> log(1+x) to make distributions that are skewed to the right 'more normal'.
The file contains the following variables:

- "id"
Article id.

- "is.FA"
Boolean indicator for whether the article is featured.

- "log1p.length"
Length measured by the number of bytes.

- "age"
Age measured by the time since the first edit.

- "log1p.number.of.edits"
Number of times a new version of the article has been uploaded.

- "log1p.number.of.reverts"
Number of times a revision has been reverted to a previous one.

- "log1p.number.of.contributors"
Number of unique contributors to the article.

- "number.of.characters.per.word"
Average number of characters per word (one component of 'reading complexity').

- "number.of.words.per.sentence"
Average number of words per sentence (second component of 'reading complexity').

- "number.of.level.1.sections"
Number of first level sections in the article.

- "number.of.level.2.sections"
Number of second level sections in the article.

- "number.of.categories"
Number of categories the article is in.

- "log1p.average.size.of.categories"
Average size of the categories the article is in.

- "log1p.number.of.intra.wiki.links"
Number of links to pages in the English-language version of Wikipedia.

- "log1p.number.of.external.references"
Number of external references given in the article.

- "log1p.number.of.images"
Number of images in the article.

- "log1p.number.of.templates"
Number of templates that the article uses.

- "log1p.number.of.inter.language.links"
Number of links to articles in different language edition of Wikipedia.

- "granularity"
As in article_info.csv (but normalized to standard deviation one).

A highly granular database of nearly 500 capital flow management measures that cover 14 instruments and 49 countries at monthly frequency between 2008 and 2021.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

South Ossetia and Abkhazia were not included for the safety of the interviewers. In addition, very remote mountainous villages or those with less than 100 inhabitants were also excluded. The excluded areas represent approximately 8 percent of the total population.

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Georgia is 1000.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

844 Global import shipment records of Plus granular with prices, volume & current Buyer’s suppliers relationships based on actual Global import trade database.

This dataset consists of example model outputs for PFAS removal by GAC. Explicit descriptions of the data can be found in the associated manuscript. This dataset is associated with the following publication: Burkhardt, J., N. Burns, D. Mobley, J. Pressman, M. Magnuson, and T. Speth. Modeling PFAS Removal Using Granular Activated Carbon for Full-Scale System Design. JOURNAL OF ENVIRONMENTAL ENGINEERING. American Society of Civil Engineers (ASCE), Reston, VA, USA, 148(3): 04021086-1, (2022).

Dataset

This dataset was created by Mesum Raza Hemani

Released under Data files © Original Authors

Contents

Simulated data of the cerebellar granular layer as in Sudhakar et al. (PLOS Comp Biol, 2017) based on the connectivity generated by two different programs.* BREP.zip is based on the original BREP program in Sudhakar et al.,* Pycabnn.zip is based on a new software, pycabnn.

An Open Context "predicates" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Variables" record is part of the "Madaba Plains Project-`Umayri" data publication.

S1: Detailed GSD data (soil weight percentage within each grain size category) from Gale Crater and the corresponding GSD parameters； S2: Detailed GSD data (soil weight percentage within each grain size category) from Jezero Crater and the corresponding GSD parameters； S3: Detailed GSD data (soil weight percentage within each grain size category) from Gusev Crater and the corresponding GSD parameters； S4: Detailed GSD data (soil weight percentage within each grain size category) from Meridiani Planum and the corresponding GSD parameters.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Bolivia is 1000.

Mode of data collection

Mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

Granular, transactional level real purchase data available on an almost real-time basis from our own proprietary consumer panel.

Measurable AI sources its e-receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients focusing primarily in the emerging markets.

Our clients leverage on our datasets to produce actionable consumer insights such as market share analysis, user behavioural traits (e.g. retention rates), average order values, and promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Most of our clients are the fast-growing tech companies, financial institutions, buyside firms, market research agencies, consultancies and acadamia.

This file set contains the Git repository and resulting datasets for the computational analyses used in the associated publication: Reliable Granular References toChanging Linked Data.The data is supplied in compressed .zip and .gz formats that can be uncompressed by standard compression utilities. The compressed files contain incremental datasets of nanopublications from both DisGeNET and WikiPathways, including TriG RDF graphs for each, along with the Git repository containing scripts, diagrams, background literature, output data and results files.Background from associated publication:Nanopublications are tiny packages of Linked Data that come with provenance and metadata attached, they are also a concept to represent Linked Data in a granular and provenance-aware manner, which has been successfully applied to a number of scientific datasets. We demonstrated in previous work how we can establish reliable and verifiable identifiers for nanopublications and sets thereof. Further adoption of these techniques, however, was probably hindered by the fact that nanopublications can lead to an explosion in the number of triples due to auxiliary information about the structure of each nanopublication and repetitive provenance and metadata. We demonstrate here that this significant overhead disappears once we take the version history of nanopublication datasets into account, calculate incremental updates, and allow users to deal with the specific subsets they need. We show that the total size and overhead of evolving scientific datasets is reduced, and typical subsets that researchers use for their analyses can be referenced and retrieved efficiently with optimized precision, persistence, and reliability.

505 Economics is on a mission to make academic economics accessible. We've developed the first monthly sub-national GDP data for EU and UK regions from January 2015 onwards.

Our GDP dataset uses luminosity as a proxy for GDP. The brighter a place, the more economic activity that place tends to have.

We produce the data using high-resolution night time satellite imagery and Artificial Intelligence.

This builds on our academic research at the London School of Economics, and we're producing the dataset in collaboration with the European Space Agency BIC UK.

We have published peer-reviewed academic articles on the usage of luminosity as an accurate proxy for GDP.

Key features:

• Granular: Data is provided at the following geographical units:
  • NUTS3 (e.g. London Boroughs),
  • NUTS2 (e.g. London),
  • NUTS1 (e.g. England), and
  • NUTS0 (e.g. United Kingdom) levels.
• Frequent: Data is provided every month from January 2015. This is more frequent than the annualised official datasets.
• Timely: Data is provided with a one month lag (i.e. the data for January 2021 was published at the end of February 2021). This is substantially quicker than the 18 month lag of official datasets.
• Accurate: Our dataset uses Deep Learning to maximise accuracy (RMSE 1.2%).

The dataset can be used by:

• Governments and policy makers - to monitor the performance of local economies, to measure the localised impact of policies, and to get a real-time indication of economic activity.
• Financial services - to get an indication of national-level GDP before official GDP statistics are released
• Engineering companies - to monitor and evaluate the localised impact of infrastructure projects
• Consultancies - to forecast the localised impact of specific projects, to retrospectively monitor and evaluate the localised impact of existing projects
• Economics firms - to create macro forecasts at the national and sub-national level, to assess the impact of policy interventions.
• Academia / Think Tanks - to conduct novel research at the local level. E.g. our dataset can be used to measure the impact of localised COVID-19 lockdowns.

We have created this dataset for all UK sub-national regions, 28 EU Countries and Switzerland.

The granular formulation of Bayluscide [Bayluscide 3.2% Granular Sea Lamprey Larvicide, granular Bayluscide (gB)] is applied in lentic and lotic systems to survey (assessment) and kill (treatment) larval sea lampreys (Petromyzon marinus) in the Great Lakes basin. Granules are spread on the water surface, settle to the sediment surface, and dissolve. The potential risk of niclosamide exposure [5 Chloro-N-(2-chloro-4-nitrophenyl)-2-hydroxybenzamide], the active ingredient of gB, to non-target organisms located downstream of survey plots, is a concern of partner agencies (State-level Natural Resource Departments, U.S. Fish and Wildlife Service’s Ecological Service, Fisheries and Oceans Canada Species at Risk Branch). Temporal and spatial distribution of niclosamide in the water column and sediment was evaluated in and downstream of five larval survey plots in two rivers following the application of gB. Water samples were collected at 0.25, 2, 4, 6, 8, and 24 h from 3 depths in the water column (10 cm above the sediment, ½ water column depth, water surface) at three locations inside each survey plot, and 1 meter upstream from three sediment sample grids positioned 10, 30 and 100 m downstream. Sediment samples were collected from inside the grids at 0.25, 2, 4, 6, 8, and 24 h, and from inside the survey plots, 8 and 24 h after gB application. Niclosamide was detected in the sediment and water at all sample locations. From 2 to 24 h after application, average water concentrations 1) varied between study sites, 2) decreased from the survey plots to 100 m downstream, 3) varied by depth in the water column, and 4) decreased over time. Average sediment concentrations varied with distance downstream and time post application, but not by study site or river. Data suggests there would be negligible risk to non-target organisms downstream of a gB survey plot based on low niclosamide concentrations measured in the water and sediment. The depletion rate of niclosamide was also evaluated in St. Clair River sediment dosed at the field application rate. Niclosamide concentration decreased at a rate of 2.28% per hour over the 24 hours measured, equating to a half-life of 1.27 days. This indicates the length of time an organism in the sediment in a survey plot might be exposed.

Database Backup as a Service Market Outlook

According to our latest research, the global Database Backup as a Service (DBaaS) market size reached USD 8.7 billion in 2024. The market is expected to grow at a robust CAGR of 10.9% from 2025 to 2033, propelling the total market value to approximately USD 21.2 billion by 2033. This significant growth is primarily driven by the escalating need for secure, scalable, and cost-effective data protection solutions across industries, as organizations increasingly migrate their critical workloads to cloud environments.

The rapid digitalization of business operations, coupled with the exponential growth in enterprise data volumes, is a primary growth factor for the Database Backup as a Service market. Organizations are generating and storing massive amounts of structured and unstructured data, which must be protected against loss, corruption, and cyber threats. Traditional backup solutions often fall short in scalability and reliability, prompting enterprises to adopt DBaaS offerings that provide automated, policy-driven, and offsite backup capabilities. Furthermore, the rise of stringent regulatory frameworks, such as GDPR and HIPAA, has made data compliance and recovery readiness a top priority, further fueling the demand for advanced backup solutions. The ability of DBaaS platforms to streamline backup management, reduce operational overhead, and ensure rapid data restoration in case of disaster is proving to be a compelling value proposition for businesses of all sizes.

Another critical growth driver for the DBaaS market is the increasing adoption of hybrid and multi-cloud strategies by enterprises seeking to optimize their IT infrastructure. As organizations diversify their cloud deployments to avoid vendor lock-in and enhance resilience, the complexity of managing data across multiple environments rises substantially. DBaaS solutions are uniquely positioned to address these challenges by offering centralized backup orchestration, seamless integration with various cloud providers, and granular data recovery options. The flexibility to support hybrid architectures—encompassing both on-premises and cloud databases—enables businesses to maintain business continuity while leveraging the agility and cost advantages of the cloud. This trend is particularly pronounced among large enterprises with global operations and complex regulatory requirements, but is also gaining traction among small and medium enterprises (SMEs) as they accelerate their digital transformation journeys.

Technological advancements and innovations in backup technologies are also propelling the Database Backup as a Service market forward. The integration of artificial intelligence (AI) and machine learning (ML) into DBaaS platforms is enabling predictive analytics, anomaly detection, and intelligent backup scheduling, which significantly enhance data protection capabilities. Furthermore, the proliferation of ransomware and sophisticated cyber threats has heightened the need for immutable backups and rapid recovery solutions. Vendors are responding by incorporating advanced encryption, air-gapped storage, and zero-trust security models into their offerings. The emergence of containerized applications and serverless architectures is also influencing the evolution of DBaaS, as businesses seek backup solutions that can accommodate modern, cloud-native workloads. Collectively, these technological trends are expanding the addressable market and driving adoption across diverse industry verticals.

Regionally, North America continues to dominate the Database Backup as a Service market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of cloud service providers, high cloud adoption rates, and stringent data protection regulations in North America are major factors contributing to regional growth. Meanwhile, Asia Pacific is witnessing the fastest growth, supported by rapid digital transformation, increasing investments in cloud infrastructure, and a burgeoning SME landscape. Europe remains a key market, driven by robust regulatory compliance requirements and widespread adoption of hybrid cloud strategies. As organizations across all regions prioritize data resilience and business continuity, the global DBaaS market is poised for sustained expansion over the forecast period.

This dataset is made available to third-parties as a part of the effort to make verification and validation procedures transparent and reproducible for granular material research. This dataset includes the the microCT images of Hostun sand and the synthetic particle manufactured by 3D printer, the results of the oedometric test conducted on assembles of synthetic particles, the labelled volume and the discrete digital correlation data that provides the trajectories of individual particles in the assembles.

Please refer to the 'description manual' document for content and information on shared database utilization.