Special Database 19 contains NIST's entire corpus of training materials for handprinted document and character recognition. It publishes Handprinted Sample Forms from 3600 writers, 810,000 character images isolated from their forms, ground truth classifications for those images, reference forms for further data collection, and software utilities for image management and handling. there are two editions of the databases. One is the original database with the images in mis or pct format. It also includes software to open and manipulate the data. The second edition has the images all in PNG format.

The NIST database of fingerprint images contains 2000 8-bit gray scale fingerprint image pairs. Each image is 512-by-512 pixels with 32 rows of white space at the bottom and classified using one of the five following classes:

In April 2017, the Intelligence Advanced Research Projects Activity (IARPA) held a dry run for the data collection portion of its Nail to Nail (N2N) Fingerprint Challenge. This data collection event was designed to ensure that the real data collection event held in September 2017 would be successful. To this end, biometric data from unhabituated individuals needed to be collected. That data is now released by NIST as Special Database 301.In total, 14 fingerprint sensors were deployed during the data collection, amassing a series of rolled and plain images. The devices include rolled fingerprints captured by skilled experts from the Federal Bureau of Investigation (FBI) Biometric Training Team. Captures of slaps, palms, and other plain impression fingerprint impressions were additionally recorded. NIST also partnered with the FBI and Schwarz Forensic Enterprises to design activity scenarios in which subjects would likely leave fingerprints on different objects. The activities and associated objects were chosen in order to use a number of latent print development techniques and simulate the types of objects often found in real law enforcement case work. NIST also collected some mugshot-style face and iris images of the subjects who participated in the dry run. These data are also available for download.

Dataset Description for Kaggle:

This dataset contains fingerprint images categorized into three main pattern classes: Arch, Whorl, and Loop. The data is selected from NIST DB4 https://www.nist.gov/srd/nist-special-database-4, which originally contained five fingerprint pattern classes.

NISTDB4_RAW: This folder includes the dataset split into three subsets for training and evaluation purposes with the following ratio:

train_set: 70% val_set: 20% test_set: 10% NISTDB4: This folder contains the total number of samples for each class as follows:

class1_arc: 801 samples class2_whorl: 799 samples class3_loop: 801 samples The total number of samples in the dataset is 2,401. This dataset can be used for various fingerprint image processing tasks, including fingerprint pattern classification, transfer learning experiments, and exploring preprocessing and data augmentation techniques.

This database was distributed for use in development and testing of automated mugshot identification systems. The database consists of one zipped file, containing a total of 3,248 images of variable size using PNG format for the images and TXT format for corresponding metadata files. There are images of 1,573 individuals (cases) 1,495 male and 78 female. The database contains both front and side (profile) views when available. Separating front views and profiles, there are 131 cases with two or more front views and 1,418 with only one front view. Profiles have 89 cases with two or more profiles and 1,268 with only one profile. Cases with both fronts and profiles have 89 cases with two or more of both fronts and profiles, 27 with two or more fronts and one profile, and 1,217 with only one front and one profile.

NIST Digital Video 1 is a public-domain collection of digital video created to encourage more researchers to address real-world problems and support the scientific comparison of solutions of digital video search, retrieval, and display. This collection consists of eight videos, totaling over two hours in length, selected from NIST's public domain archive of marketing, technical, and educational material. The characteristics of these videos include, but are not limited to, different levels of motion (static to fast moving objects), close-up figures (talking heads, moving arms, and moving hands), outdoor shots (laboratory, auditorium, and conference room environments), and various levels and quality of audio. In addition to the base data (titles below), pre- or post-production transcripts are included as reference data.It is our intent to gather feedback on the use of this collection, the need for additional base data, and further requirements for reference data (or "truth"). Please send email to dvr-info@nist.gov with questions, comments, or suggestions. Or, visit the Digital Video Retrieval web site at http://www.itl.nist.gov/iaui/894.02/projects/dv .Below is the title of each video included as base data on "Digital Video 1".NIST in 5 Minutes and 41 SecondsInformational tour of the agency and its efforts to promote economic growth by working with industry to develop and apply technology, measurements, and standards.Enhanced Aerial Lift ControllerDescribes how the controller may provide solutions to many jobs that cannot be addressed with existing commercial aerial lifts.Portsmouth Flexible Manufacturing WorkstationDescribes the Portsmouth Fastener Workstation, which makes accurate threaded fasteners for Navy ships.You Don't Have To Be There... Telepresence MicroscopyThe program shows how telepresence can provide the potential for remote, instantaneous, around-the-clock access to critical metrology services using the Internet (1998).A Decade of Business Excellence for AmericaHighlights the decade of excellence as seen through the Malcolm Baldrige National Quality Award.A Uniquely Rewarding ExperienceDescribes the advantages of becoming a Baldrige Quality Award examiner.Aircraft Hangar Fires: Fire Protection ImprovementsDescribes how NIST and the U.S. Navy conducted tests on sprinkler and heat detection systems in high bay aircraft hangars in Iceland and Hawaii.Engineer in SpacePublic lecture which describes a NIST engineer's adventure and research on two missions aboard the space shuttle Columbia.System Requirements: DVD-ROM drive for accessing the digitized video collection and a compatible MPEG decoder is needed to view the collection. Note: Not for use in set-top boxes. Reference data is best viewed using a browser which supports HTML 1.0.

Multiple Encounter Dataset (MEDS-I) is a test corpus organized from an extract of submissions of deceased persons with prior multiple encounters. MEDS is provided to assist the FBI and partner organizations refine tools, techniques, and procedures for face recognition as it supports Next Generation Identification (NGI), forensic comparison, training, and analysis, and face image conformance and inter-agency exchange standards. The MITRE Corporation (MITRE) prepared MEDS in the FBI Data Analysis Support Laboratory (DASL) with support from the FBI Biometric Center of Excellence.

In September 2017, the Intelligence Advanced Research Projects Activity (IARPA) held a data collection as part of its Nail to Nail (N2N) Fingerprint Challenge. Participating Challengers deployed devices designed to collect an image of the full nail to nail surface area of a fingerprint?equivalent to a rolled fingerprint?from an unacclimated user, without assistance from a trained operator. Traditional operator-assisted live-scan rolled fingerprints were also captured, along with assorted other friction ridge live-scan and latent captures. In this data collection, study participants needed to have their fingerprints captured using traditional operator-assisted techniques in order to quantify the performance of the Challenger devices. IARPA invited members of the Federal Bureau of Investigation (FBI) Biometric Training Team to the data collection to perform this task. Each study participant had N2N fingerprint images captured twice, each by a different FBI expert, resulting in two N2N baseline datasets. To ensure the veracity of recorded N2N finger positions in the baseline datasets, Challenge test staff also captured plain fingerprint impressions in a 4-4-2 slap configuration. This capture method refers to simultaneously imaging the index, middle, ring, and little fingers on the right hand, then repeating the process on the left hand, and finishing with the simultaneous capture of the left and right thumbs. This technique is a best practice to ensure finger sequence order, since it is physically challenging for a study participant to change the ordering of fingers when imaging them simultaneously. There were four baseline (two rolled and two slap), eight challenger and ten auxiliary fingerprint sensors deployed during the data collection, amassing a series of rolled and plain images. It was required that the baseline devices achieve 100% acquisition rate, in order to verify the recorded friction ridge generalized positions (FRGPs) and study participant identifiers for other devices. There were no such requirements for Challenger devices. Not all devices were able to achieve 100% acquisition rate. Plain, rolled, and touch-free impression fingerprints were captured from a multitude of devices, as well as sets of plain palm impressions. NIST also partnered with the FBI and Schwarz Forensic Enterprises (SFE) to design activity scenarios in which subjects would likely leave fingerprints on different objects. The activities and associated objects were chosen in order to use a number of latent print development techniques and simulate the types of objects often found in real law enforcement case work.

he set of NIST Test PIV Cards contains sixteen smart cards that are loaded with a PIV Card Application, as specified in NIST Special Publication 800-73-4. The PIV Card Applications on the smart cards are loaded with test data and keys that are similar to what might appear on actual PIV Cards, with the exception that the certificates on the test PIV Cards were issued from a test public key infrastructure. The currently available set of test PIV cards, version 2, includes examples of new, optional features that were introduced in SP 800-73-4, such as on-card biometric comparison, secure messaging, and the virtual contact interface. The set of test cards includes not only examples that are similar to cards issued today, but also examples of cards with features that are expected to appear in cards that will be issued in the future. For example, while the certificates and data objects on most, if not all, cards issued today are signed using RSA PKCS #1 v1.5, the set of test cards include examples of certificates and data objects that are signed using each of the algorithms and key sizes listed in Table 3-2 of Special Publication 800-78-4, including RSASSA-PSS and ECDSA. Similarly, the infrastructure supporting the test cards provides examples of CRLs and OCSP responses that are signed using each of these signature algorithms. The set of test cards also includes certificates with elliptic curve subject public keys in addition to RSA subject public keys, as is permitted by Table 3-1 of Special Publication 800-78-4. The set of test cards, collectively, also include all of the mandatory and optional data objects listed in Section 3 of SP 800-73-4 Part 1, except for Cardholder Iris Images. Several of the cards include a Key History object along with retired key management keys.

Detecting the presence and location of hand written artifacts such as signatures, dates, initials can be critical for scanned (offline)document processing systems. This capability can support multiple downstream tasks such as signature verification, document tagging and categorization. In this work, we present SignverOD, a curated dataset of 2576 scanned document images with 7103 bounding box annotations, across 4 categories (signature, initials, redaction, date). SignverOD cover a diverse set of document types including memos, emails, bank cheques, lease agreements and letters, memos, invoices.

Dataset Source

Images of documents in this dataset are sourced from 4 main locations and then annotated.

Tobacco800

Tobacco800 is a publicly accessible document image collection with realistic scope and complexity is important to the document image analysis and search community. https://www.kaggle.com/sprytte/tobacco-800-dataset.

NIST.gov Special Database

The NIST.gov structured Forms Database consists of 5,590 pages of binary, black-and-white images of synthesized documents. The documents in this database are 12 different tax forms from the IRS 1040 Package X for the year 1988. https://www.nist.gov/srd/nist-special-database-2

Bank Cheques

The bank cheques dataset is a collection of xx colored images of bank checks. They consist of scanned realistic checks as well as examplar signatures with signatures. https://www.kaggle.com/saifkhichi96/bank-checks-signatures-segmentation-dataset

GSA.gov Lease Documents

GSA provides electronic copies of GSA lease documents for general public viewing. The lease documents are sorted by region and contain, for the most part, GSA Lease Forms and Lease Amendments (LA) from selected GSA leases across the nation. https://www.gsa.gov/real-estate/real-estate-services/leasing/executed-lease-documents

Citation

If you use this dataset as part of your work or experiments, please consider citing:

This database contains benchmark results for simulation of plasma population kinetics and emission spectra. The data were contributed by the participants of the 4th Non-LTE Code Comparison Workshop who have unrestricted access to the database. The only limitation for other users is in hidden labeling of the output results. Guest users can proceed to the database entry page without entering userid and password.

The NIST DART-MS Forensics Database is an evaluated collection of in-source collisionally-induced dissociation (is-CID) mass spectra of compounds of interest to the forensics community (e.g. seized drugs, cutting agents, etc.). The is-CID mass spectra were collected using Direct Analysis in Real-Time (DART) Mass Spectrometry (MS), either by NIST scientists or by contributing agencies noted per compound. The database is provided as a general-purpose structure data file (.SDF). For users on Windows operating systems, the .SDF format library can be converted to NIST MS Search format using Lib2NIST and then explored using NIST MS Search v2.4 for general mass spectral analysis. These software tools can be downloaded at https://chemdata.nist.gov. The database is now (09-28-2021) also provided in R data format (.RDS) for use with the R programming language. This database, also commonly referred to as a library, is one in a series of high-quality mass spectral libraries/databases produced by NIST (see NIST SRD 1a, https://dx.doi.org/10.18434/T4H594).

This is the taxonomy that is used for classifying information on the NIST website. It comprises a set of top-level designations that include the research areas within NIST, with two levels of subcategories beneath.

description: This database provides reference data on controlled cell image experiments. The database contains cell images of A-10 rat smooth muscle and NIH-3T3 mouse fibroblasts. A novel rule and root based method is used to create experimental metadata as described in About Us page.; abstract: This database provides reference data on controlled cell image experiments. The database contains cell images of A-10 rat smooth muscle and NIH-3T3 mouse fibroblasts. A novel rule and root based method is used to create experimental metadata as described in About Us page.

Data here contain and describe an open-source structured query language (SQLite) portable database containing high resolution mass spectrometry data (MS1 and MS2) for per- and polyfluorinated alykl substances (PFAS) and associated metadata regarding their measurement techniques, quality assurance metrics, and the samples from which they were produced. These data are stored in a format adhering to the Database Infrastructure for Mass Spectrometry (DIMSpec) project. That project produces and uses databases like this one, providing a complete toolkit for non-targeted analysis. See more information about the full DIMSpec code base - as well as these data for demonstration purposes - at GitHub (https://github.com/usnistgov/dimspec) or view the full User Guide for DIMSpec (https://pages.nist.gov/dimspec/docs). Files of most interest contained here include the database file itself (dimspec_nist_pfas.sqlite) as well as an entity relationship diagram (ERD.png) and data dictionary (DIMSpec for PFAS_1.0.1.20230615_data_dictionary.json) to elucidate the database structure and assist in interpretation and use.

The NIST Database of Cross Sections for Inner-Shell Ionization by Electron or Positron Impact provides cross sections for ionization of the K shell and of the L and M subshells of neutral atoms of the elements, from hydrogen to einsteinium, by electrons or positrons, for projectile energies from the ionization threshold to 1 GeV. These cross sections were calculated from a combination of the relativistic distorted-wave and the plane-wave Born approximations. Extensive comparisons have been made of the calculated cross sections for inner-shell ionization by electron impact with available experimental data that satisfied mutual-consistency checks. These comparisons showed that the overall root-mean-square deviation between measured and calculated cross sections was 10.9 % [X. Llovet, C. J. Powell, A. Jablonski, and F. Salvat, J. Phys. Chem. Ref. Data 43, 013102 (2014)].

The NIST Chemical Kinetics Database includes essentially all reported kinetics results for thermal gas-phase chemical reactions. The database is designed to be searched for kinetics data based on the specific reactants involved, for reactions resulting in specified products, for all the reactions of a particular species, or for various combinations of these. In addition, the bibliography can be searched by author name or combination of names. The database contains in excess of 38,000 separate reaction records for over 11,700 distinct reactant pairs. These data have been abstracted from over 12,000 papers with literature coverage through early 2000. Rate constant records for a specified reaction are found by searching the Reaction Database. All rate constant records for that reaction are returned, with a link to 'Details' on that record. Each rate constant record contains the following information (as available): a) Reactants and, if defined, reaction products; b) Rate parameters: A, n, Ea/R, where k = A* (T/298)**n exp[-(Ea/R)/T], where T is the temperature in Kelvins; c) Uncertainty in A, n, and Ea/R, if reported; d) Temperature range of experiment or temperature range of validity of a review or theoretical paper; e) Pressure range and bulk gas of the experiment; f) Data type of the record (i.e., experimental, relative rate measurement, theoretical calculation, modeling result, etc.). If the result is a relative rate measurement, then the reaction to which the rate is relative is also given; g) Experimental procedure, including separate fields for the description of the apparatus, the time resolution of the experiment, and the excitation technique. A majority of contemporary chemical kinetics methods are represented. The Kinetics Database is being expanded to include other resources for the convenience of the users. Presently this includes direct links to the corresponding NIST WebBook page for all substances for which such a link is possible. This is indicated by underling and highlighting the species. The WebBook provides thermodynamic, spectral, and other data on the species. Note that the link to the WebBook is opened as a new frame in your browser.

Description
This synthetic dataset has been generated to facilitate object detection (in YOLO format) for research on dyslexia-related handwriting patterns. It builds upon an original corpus of uppercase and lowercase letters obtained from multiple sources: the NIST Special Database 19 111, the Kaggle dataset “A-Z Handwritten Alphabets in .csv format” 222, as well as handwriting samples from dyslexic primary school children of Seberang Jaya, Penang (Malaysia).

In the original dataset, uppercase letters originated from NIST Special Database 19, while lowercase letters came from the Kaggle dataset curated by S. Patel. Additional images (categorized as Normal, Reversal, and Corrected) were collected and labeled based on handwriting samples of dyslexic and non-dyslexic students, resulting in:

• 78,275 images labeled as Normal
• 52,196 images labeled as Reversal
• 8,029 images labeled as Corrected

Building upon this foundation, the Synthetic Dyslexia Handwriting Dataset presented here was programmatically generated to produce labeled examples suitable for training and validating object detection models. Each synthetic image arranges multiple letters of various classes (Normal, Reversal, Corrected) in a “text line” style on a black background, providing YOLO-compatible .txt annotations that specify bounding boxes for each letter.

Key Points of the Synthetic Generation Process

1. Letter-Level Source Data
   Individual characters were sampled from the original image sets.
2. Randomized Layout
   Letters are randomly assembled into words and lines, ensuring a wide variety of visual arrangements.
3. Bounding Box Labels
   Each character is assigned a bounding box with (x, y, width, height) in YOLO format.
4. Class Annotations
   Classes include 0 = Normal, 1 = Reversal, and 2 = Corrected.
5. Preservation of Visual Characteristics
   Letters retain their key dyslexia-relevant features (e.g., reversals).

Historical References & Credits

If you are using this synthetic dataset or the original Dyslexia Handwriting Dataset, please cite the following papers:

• M. S. A. B. Rosli, I. S. Isa, S. A. Ramlan, S. N. Sulaiman and M. I. F. Maruzuki, "Development of CNN Transfer Learning for Dyslexia Handwriting Recognition," 2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2021, pp. 194–199, doi: 10.1109/ICCSCE52189.2021.9530971.
• N. S. L. Seman, I. S. Isa, S. A. Ramlan, W. Li-Chih and M. I. F. Maruzuki, "Notice of Removal: Classification of Handwriting Impairment Using CNN for Potential Dyslexia Symptom," 2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2021, pp. 188–193, doi: 10.1109/ICCSCE52189.2021.9530989.
• Isa, Iza Sazanita. CNN Comparisons Models On Dyslexia Handwriting Classification / Iza Sazanita Isa … [et Al.]. Universiti Teknologi MARA Cawangan Pulau Pinang, 2021.
• Isa, I. S., Rahimi, W. N. S., Ramlan, S. A., & Sulaiman, S. N. (2019). Automated detection of dyslexia symptom based on handwriting image for primary school children. Procedia Computer Science, 163, 440–449.

References to Original Data Sources

111 P. J. Grother, “NIST Special Database 19,” NIST, 2016. [Online]. Available:
https://www.nist.gov/srd/nist-special-database-19

222 S. Patel, “A-Z Handwritten Alphabets in .csv format,” Kaggle, 2017. [Online]. Available:
https://www.kaggle.com/sachinpatel21/az-handwritten-alphabets-in-csv-format

Usage & Citation

Researchers and practitioners are encouraged to integrate this synthetic dataset into their computer vision pipelines for tasks such as dyslexia pattern analysis, character recognition, and educational technology development. Please cite the original authors and publications if you utilize this synthetic dataset in your work.

Password Note (Original Data)

The original RAR file was password-protected with the password: WanAsy321. This synthetic dataset, however, is provided openly for streamlined usage.

DC voltage

This NASA Innovative Research Grant activity conducts engineering analysis to demonstrate the feasibility and advantages of applying a breakthrough remote sensor calibration concept to a wide range of future NASA remote sensor science missions, e.g., PACE, GEO-CAPE, CLARREO, HySpIRI, GACM and Heliophysics research.