Graph and download economic data for U.S.-Chartered Depository Institutions; Private Commercial CMOs and Other Structured MBS; Asset, Market Value Levels (BOGZ1LM763063693Q) from Q4 1945 to Q4 2024 about mortgage-backed, market value, commercial, assets, private, and USA.

The Platts Structured Heards dataset provides reported transactional activity heard across the market which is published in a structured format.

The documents in this database are 12 different tax forms from the IRS 1040 Package X for the year 1988. These include Forms 1040, 2106, 2441, 4562, and 6251 together with Schedules A, B, C, D, E, F, and SE. Eight of these forms contain two pages or form faces; therefore, there are 20 different form faces represented in the database. The document images in this database appear to be real forms prepared by individuals, but the images have been automatically derived and synthesized using a computer.

The Structured Product Labeling dataset contains the most recent drug labeling information submitted to the Food and Drug Administration (FDA) and currently in use. All labels information are published by DailyMed the official provider of FDA label information.

Get detailed insights into the current valuation of Structured Query Language Server Transformation market size, including growth analysis, current market status and future market projections.

The structured data management software market size was over USD 82.03 billion in 2024 and is poised to exceed USD 231.28 billion by 2037, witnessing over 8.3% CAGR during the forecast period i.e., between 2025-2037. Asia Pacific industry is estimated to hold largest revenue share by 2037, on account of increasing spending by BFSI, manufacturing and other end users for improving business processes by implementing digital technologies in the region.

blockstructured hexahedral grid, 6.7 mio elements, 24 degree minimum grid angle, CGNS format version 2.4, double precision

Binary, Plot3D file

Please contact Thorsten Hansen for information about these files/grids.

This original dataset contains physiological signals collected during structured acute stress induction and aerobic and anaerobic exercise sessions using a wearable device. Blood volume pulse, motion-based activity, skin temperature, and electrodermal activity were recorded with the Empatica E4, a research-grade wearable. The stress induction protocol involved math and emotional tasks designed to provoke stress responses, interleaved with rest periods. Self-reported stress levels were also recorded during this procedure. For the exercise sessions, distinct routines on a stationary bike were created for aerobic and anaerobic activities. The dataset includes records from 36 healthy volunteers for stress sessions, 30 for aerobic exercise, and 31 for anaerobic exercise. By examining the variations in physiological signals, the effects of these activities can be analyzed. This dataset is a valuable resource for research on stress and exercise detection and classification.

Relational SQLite Database Tables of Kickstarter, including projects, creators, funders, comments, geography, pledge and funding, etc.

Dataset Card for test-text-clustering-structured-batched-v0.1

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/plaguss/test-text-clustering-structured-batched-v0.1/raw/main/pipeline.yaml"

or explore the configuration: distilabel… See the full description on the dataset page: https://huggingface.co/datasets/plaguss/test-text-clustering-structured-batched-v0.1.

FILS, Douglas, Ocean Leadership, 1201 New York Ave, NW, 4th Floor, Washington, DC 20005, SHEPHERD, Adam, Woods Hole Oceangraphic Inst, 266 Woods Hole Road, Woods Hole, MA 02543-1050 and LINGERFELT, Eric, Earth Science Support Office, Boulder, CO 80304

The growth in the amount of geoscience data on the internet is paralleled by the need to address issues of data citation, access and reuse. Additionally, new research tools are driving a demand for machine accessible data as part of researcher workflows. In the commercial sector, elements of this have been addressed by the use of the Schema.org vocabulary encoded via JSON-LD and coupled with web publishing patterns. Adaptable publishing approaches are already in use by many data facilities as they work to address publishing and FAIR patterns. While these often lack the structured data elements these workflows could be leveraged to additionally implement schema.org style publishing patterns.

This presentation will report on work that grew out of the EarthCube Council of Data Facilities known as, Project 418. Project 418 was a proof of concept funded by the EarthCube Science Support Office for exploring the approach of publishing JSON-LD with schema.org and extensions by a set of NSF data facilities. The goal was focused on using this approach to describe data set resources and evaluate the use of this structured metadata to address discovery. Additionally, we will discuss growing interest by Google and others in leveraging this approach to data set discovery.

The work scoped 47,650 datasets from 10 NSF-funded data facilities. Across these datasets, the harvester found 54,665 data download URLs, and approximately 560K dataset variables and 35k unique identifiers (DOIs, IGSNs or ORCIDs).

The various publishing workflows used by the involved data facilities will be presented along with the harvesting and interface developments. Details on how resources were indexed into text, spatial and graph systems and used for search interfaces will be presented along with future directions underway building on this foundation.

The vastness of materials space, particularly that which is concerned with metal–organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data-mine published MOF papers to extract the materials informatics knowledge contained within journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials, and text-mined over 52,680 associated properties including the synthesis method, solvent, organic linker, metal precursor, and topology. Additionally, we developed an alternative data extraction technique to obtain and transform the chemical names assigned to each CSD entry in order to determine linker types for each structure in the CSD MOF subset. This data enabled us to match MOFs to a list of known linkers provided by Tokyo Chemical Industry UK Ltd. (TCI) and analyze the cost of these important chemicals. This centralized, structured database reveals the MOF synthetic data embedded within thousands of MOF publications and contains further topology, metal type, accessible surface area, largest cavity diameter, pore limiting diameter, open metal sites, and density calculations for all 3D MOFs in the CSD MOF subset. The DigiMOF database and associated software are publicly available for other researchers to rapidly search for MOFs with specific properties, conduct further analysis of alternative MOF production pathways, and create additional parsers to search for additional desirable properties.

Matlab(R) codes and raw processed images of feature selecting super resolution structured illumination microscopy numerical simulations.

Structured light-based depth sensors provide accurate depth information independently of the scene appearance by extracting pattern positions from the captured pixel intensities.

Spatial neighborhood encoding, in particular, is a popular structured light approach for off-the-shelf hardware. However, it suffers from the distortion and fragmentation of the projected pattern by the scene's geometry in the vicinity of a pixel. This forces algorithms to find a delicate balance between depth prediction accuracy and robustness to pattern fragmentation or appearance change. While stereo matching provides more robustness at the expense of accuracy, we show that learning to regress a pixel's position within the projected pattern is not only more accurate when combined with classification but can be made equally robust. We propose to split the regression problem into smaller classification sub-problems in a coarse-to-fine manner with the use of a weight-adaptive layer that efficiently implements branching per-pixel Multilayer Perceptrons applied to features extracted by a Convolutional Neural Network.

As our approach requires full supervision, we train our algorithm on a rendered dataset sufficiently close to the real-world domain. On a separately captured real-world dataset, we show that our network outperforms state-of-the-art and is significantly more robust than other regression-based approaches.

Three grids: coarse, medium, and fine in Plot3d format.

An Open Context "predicates" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Variables" record is part of the "Kenan Tepe" data publication.

This dataset is about book subjects and is filtered where the books is Structured and object-oriented problem solving using C++, featuring 10 columns including authors, average publication date, book publishers, book subject, and books. The preview is ordered by number of books (descending).

Information about abstracts with distinct, labeled sections (e.g., Introduction, Methods, Results, discussion) that appear in MEDLINE.

sachithgunasekara/phased-self-discover-mistral-structured-0-shot-bbh-eval dataset hosted on Hugging Face and contributed by the HF Datasets community

This is a data publication hosted by the research data repository RADAR.