Abstract copyright UK Data Service and data collection copyright owner.

UNESCO is a major collector and disseminator of statistical data on education and related subjects. Its statistical activities are aimed at providing relevant, reliable and current information for development and policy-making purposes, both at the national and international levels, and the production of reliable statistical indicators for education. These indicators cover four main areas: educational population; access and participation; the efficiency and effectiveness of education; human and financial resources.
The UNESCO Education Database covers a wide range of these areas, at four main educational levels: pre-primary, primary, secondary and tertiary, in accordance with the International Standard Classification of Education (ISCED) system. This system provides standard definitions for each of the four levels of education examined. UNESCO collects and collates education data according to these definitions from approximately 200 countries, and compiles them into the Education Database time series, which is published annually.

Main Topics:

Data are available in this collection for various topics related to primary education - the first ISCED level (ISCED = International Standard Classification of Education). Primary education usually begins at age five, six or seven years and lasts for about 5 or 6 years. However, in some countries, what is termed basic' education provided at this level may last longer. Primary education programmes are designed to give pupils a sound basic education in reading, writing and arithmetic along with an elementary understanding of other subjects such as natural history, geography, natural science, social science, art and music. From the year 1994, these data also includespecial' education at primary level as part of overall totals.
Topics covered here include : number of institutions and private pupils, pupils in primary education (total, by age and by grade), total numbers of teachers (part- and full-time), and pupils repeating' grades at this level. All data are further defined by gender. <br> Users should note that 15 countries have reported an automatic promotion policy to the next grade, whether pupils have completed their education at the previous grade or not. Thus, there will be no data values forrepeaters' for these countries : Bahamas, Denmark, Japan, Republic of Korea, Malaysia, Montserrat, Norway, Papua New Guinea, Saint Kitts and Nevis, Saint Lucia, Seychelles, Sudan, Sweden, Turks and Caicos Islands, United Kingdom and Zimbabwe.

KEGG LIGAND contains knowledge of chemical substances and reactions that are relevant to life. It is a composite database consisting of COMPOUND, GLYCAN, REACTION, RPAIR, and ENZYME databases, whose entries are identified by C, G, R, RP, and EC numbers, respectively. ENZYME is derived from the IUBMB/IUPAC Enzyme Nomenclature, but the others are internally developed and maintained. The primary database of KEGG LIGAND is a relational database with the KegDraw interface, which is used to generated the secondary (flat file) database for DBGET.

Content of the Bioinformatics for Dentistry, with its respective primary sources.

Since 1987, every year all primary and secondary schools in Slovenia participate in SLOfit, national surveillance system for physical and motor development of children and youth.

With the help of the SLOfit data children, youth and their parents can monitor their physical and motor development, while teachers and physicians acquire important information necessary for the planning and implementation of intervention in the cases when children experience difficulties in their physical and motor development or professional guidance when children show extraordinary abilities.

The data (hmdb_metabolites, released on 17/11/2021) used to create this ID mapping database was downloaded from HMDB (Human Metabolome Database, website URL: https://hmdb.ca/).

This database was used for the BridgeDb demo at BioSB 2022 conference.

The scripts used to create this database based on HGNC: https://github.com/tabbassidaloii/create-bridgedb-secondary2primary

This work was funded by the FAIRplus project (grant agreement no 802750) and NWO Open Science Fund (grant no 203.001.121).

BackgroundThe use of routinely collected health data for secondary research purposes is increasingly recognised as a methodology that advances medical research, improves patient outcomes, and guides policy. This secondary data, as found in electronic medical records (EMRs), can be optimised through conversion into a uniform data structure to enable analysis alongside other comparable health metric datasets. This can be achieved with the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM), which employs a standardised vocabulary to facilitate systematic analysis across various observational databases. The concept behind the OMOP-CDM is the conversion of data into a common format through the harmonisation of terminologies, vocabularies, and coding schemes within a unique repository. The OMOP model enhances research capacity through the development of shared analytic and prediction techniques; pharmacovigilance for the active surveillance of drug safety; and ‘validation’ analyses across multiple institutions across Australia, the United States, Europe, and the Asia Pacific. In this research, we aim to investigate the use of the open-source OMOP-CDM in the PATRON primary care data repository.MethodsWe used standard structured query language (SQL) to construct, extract, transform, and load scripts to convert the data to the OMOP-CDM. The process of mapping distinct free-text terms extracted from various EMRs presented a substantial challenge, as many terms could not be automatically matched to standard vocabularies through direct text comparison. This resulted in a number of terms that required manual assignment. To address this issue, we implemented a strategy where our clinical mappers were instructed to focus only on terms that appeared with sufficient frequency. We established a specific threshold value for each domain, ensuring that more than 95% of all records were linked to an approved vocabulary like SNOMED once appropriate mapping was completed. To assess the data quality of the resultant OMOP dataset we utilised the OHDSI Data Quality Dashboard (DQD) to evaluate the plausibility, conformity, and comprehensiveness of the data in the PATRON repository according to the Kahn framework.ResultsAcross three primary care EMR systems we converted data on 2.03 million active patients to version 5.4 of the OMOP common data model. The DQD assessment involved a total of 3,570 individual evaluations. Each evaluation compared the outcome against a predefined threshold. A ’FAIL’ occurred when the percentage of non-compliant rows exceeded the specified threshold value. In this assessment of the primary care OMOP database described here, we achieved an overall pass rate of 97%.ConclusionThe OMOP CDM’s widespread international use, support, and training provides a well-established pathway for data standardisation in collaborative research. Its compatibility allows the sharing of analysis packages across local and international research groups, which facilitates rapid and reproducible data comparisons. A suite of open-source tools, including the OHDSI Data Quality Dashboard (Version 1.4.1), supports the model. Its simplicity and standards-based approach facilitates adoption and integration into existing data processes.

Broad network of plant metabolic pathway databases with two types of databases: a reference database called PlantCyc and single species/taxon databases, such as AraCyc. Additional external metabolic pathway databases are affiliated with the PMN. These databases remain under the autonomous control of the PMN collaborators who provide them. PMN draws upon the work of many individuals with expertise in annotating genomes, generating metabolic pathway databases, curating biochemical information from the literature, and forming extensive network of collaborations with biological databases and biochemistry researchers. In addition to providing biochemical reaction diagrams, organism-specific metabolic maps, detailed descriptions of enzymes and pathways, and links to other resources, the PMN website houses tutorials to help guide teachers and students as they learn more about the valuable bioinformatic data and analysis tools that are crucial elements of modern biological research. Please help expand the content of the PMN databases, including AraCyc and PlantCyc! Any and all new data submissions related to plant biochemical pathways are welcomed. You can also correct an existing pathway. The PMN has several tools available for analyzing the data presented in PlantCyc and the other species-specific metabolic databases. You may browse the pathway ontology, compound ontology, and enzyme commission ontology. *PlantCyc: a comprehensive plant biochemical pathway database, containing curated information from the literature and computational analyses about the genes, enzymes, compounds, reactions, and pathways involved in primary and secondary metabolism. It provides access to manually curated or reviewed information about shared and unique metabolic pathways present in over 350 plant species. * AraCyc: provides access to manually curated or reviewed information about metabolic pathways for the model plant Arabidopsis thaliana. The pathways may be unique to Arabidopsis or shared with other organisms. Data from gene expression, proteomic, and metabolomic experiments in Arabidopsis can be overlaid on a metabolic pathway map using the OMICS Viewer. * PoplarCyc: provides access to manually curated or reviewed information about metabolic pathways for the model tree Populus trichocarpa and a few other related Populus species and hybrids. The pathways may be unique to poplar or shared with other organisms. Data from gene expression, proteomic, and metabolomic experiments in poplar can be overlaid on a metabolic pathway map using the OMICS Viewer.

The schools, districts, and other educational institutions in the Detroit Educational Institutions datasets were identified from the State of Michigan Center for Educational Performance and Information (CEPI) Educational Entity Master (EEM) database. Schools of all statuses (Open - Active; Open - Pending; Open - Inactive; Open - Under construction/remodeling; Open - Vacant/empty; Closed – Pending; Closed) are included in the dataset to provide access to information about schools that currently and previously operated in Detroit. Educational institutions with a mailing address in Detroit but a physical location outside the City are not included in this dataset. Each record in the dataset represents an educational entity, which may be a school, a district, or other entity directly associated with an educational institution. The word, "entity" is used in field (i.e., column) names and descriptions when a field is applicable to multiple types units associated with an educational entity (e.g., if applicable to schools, districts, and other facilities).Link to metadata: https://cepi.state.mi.us/eem/Documents/ColumnDescriptions.pdf

The Dutch CELEX data is derived from R.H. Baayen, R. Piepenbrock & L. Gulikers, The CELEX Lexical Database (CD-ROM), Release 2, Dutch Version 3.1, Linguistic Data Consortium, University of Pennsylvania, Philadelphia, PA, 1995.Apart from orthographic features, the CELEX database comprises representations of the phonological, morphological, syntactic and frequency properties of lemmata. For the Dutch data, frequencies have been disambiguated on the basis of the 42.4m Dutch Instituut voor Nederlandse Lexicologie text corpora.To make for greater compatibility with other operating systems, the databases have not been tailored to fit any particular database management program. Instead, the information is presented in a series of plain ASCII files, which can be queried with tools such as AWK and ICON. Unique identity numbers allow the linking of information from different files.This database can be divided into different subsets:· orthography: with or without diacritics, with or without word division positions, alternative spellings, number of letters/syllables;· phonology: phonetic transcriptions with syllable boundaries or primary and secondary stress markers, consonant-vowel patterns, number of phonemes/syllables, alternative pronunciations, frequency per phonetic syllable within words;· morphology: division into stems and affixes, flat or hierarchical representations, stems and their inflections;· syntax: word class, subcategorisations per word class;· frequency of the entries: disambiguated for homographic lemmata.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not bedivided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.

PROSITE consists of documentation entries describing protein domains, families and functional sites as well as associated patterns and profiles to identify them [More... / References / Commercial users ]. PROSITE is complemented by ProRule , a collection of rules based on profiles and patterns, which increases the discriminatory power of profiles and patterns by providing additional information about functionally and/or structurally critical amino acids [More...].

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not be divided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.

Tropical forests play a critical role in the global carbon (C) cycle, storing ~45% of terrestrial C and constituting the largest component of the terrestrial C sink. Despite their central importance to the global C cycle, their ecosystem-level C cycles are not as well characterized as those of extra-tropical forests, and knowledge gaps hamper efforts to quantify C budgets across the tropics and to model tropical forest- climate interactions. To advance understanding of C dynamics of pantropical forests, we compiled a new database, the Tropical Forest C database (TropForC-db), which contains data on ground-based measurements of ecosystem-level C stocks and annual fluxes along with disturbance history. This database currently contains 3,568 records from 845 plots in 178 geographically distinct areas, making it the largest and most comprehensive database of its type. Using TropForC-db, we characterized C stocks and fluxes for young, intermediate-aged, and mature forests. Relative to existing C budgets of extra-tropical forests, mature tropical broadleaf evergreen forests had substantially higher gross primary productivity (GPP) and ecosystem respiration (Reco), their autotropic respiration (Ra) consumed a larger proportion (~67%) of GPP, and their woody stem growth (ANPPstem) represented a smaller proportion of net primary productivity (NPP, ~32%) or GPP (~9%). In regrowth stands, aboveground biomass increased rapidly during the first 20 years following stand-clearing disturbance, with slower accumulation following agriculture and in deciduous forests, and continued to accumulate at a slower pace in forests aged 20-100 years. Most other C stocks likewise increased with stand age, while potential to describe age trends in C fluxes was generally data-limited. We expect that TropForC-db will prove useful for model evaluation and for quantifying the contribution of forests to the global C cycle. The database version associated with this publication is archived in Dryad (DOI:10.5061/dryad.t516f) and a dynamic version is maintained at https://github.com/forc-db.

BackgroundPsoriasis is a chronic inflammatory skin disease. Dyslipidemia may be a risk factor of psoriasis. But the causal relationship between psoriasis and blood lipid still remains uncertain.MethodsThe two data of blood lipid were obtained from UK Biobank (UKBB) and Global Lipid Genetics Consortium Results (GLGC). The primary and secondary database were from large publicly available genome-wide association study (GWAS) with more than 400,000 and 170,000 subjects of European ancestry, respectively. The psoriasis from Finnish biobanks of FinnGen research project for psoriasis, consisting of 6,995 cases and 299,128 controls. The single-variable Mendelian randomization (SVMR) and multivariable Mendelian randomization (MVMR) were used to assess the total and direct effects of blood lipid on psoriasis risk.ResultsSVMR estimates in primary data of blood lipid showed low-density lipoprotein cholesterol (LDL-C) (odds ratio (OR): 1.11, 95%, confidence interval (CI): 0.99−1.25, p = 0.082 in stage 1; OR: 1.15, 95% CI: 1.05−1.26, p = 0.002 in stage 2; OR: 1.15, 95% CI: 1.04−1.26, p = 0.006 in stage 3) and triglycerides (TG) (OR: 1.22, 95% CI: 1.10−1.35, p = 1.17E-04 in stage 1; OR: 1.15, 95% CI: 1.06−1.24, p = 0.001 in stage 2; OR: 1.14, 95% CI: 1.05−1.24, p = 0.002 in stage 3) had a highly robust causal relationship on the risk of psoriasis. However, there were no robust causal associations between HDL-C and psoriasis. The SVMR results in secondary data of blood lipid were consistent with the primary data. Reverse MR analysis showed a causal association between psoriasis and LDL-C (beta: -0.009, 95% CI: -0.016− -0.002, p = 0.009) and HDL-C (beta: -0.011, 95% CI: -0.021− -0.002, p = 0.016). The reverse causation analyses results between psoriasis and TG did not reach significance. In MVMR of primary data of blood lipid, the LDL-C (OR: 1.05, 95% CI: 0.99–1.25, p = 0.396 in stage 1; OR: 1.07, 95% CI: 1.01–1.14, p = 0.017 in stage 2; OR: 1.08, 95% CI: 1.02–1.15, p = 0.012 in stage 3) and TG (OR: 1.11, 95% CI: 1.01–1.22, p = 0.036 in stage 1; OR: 1.09, 95% CI: 1.03–1.15, p = 0.002 in stage 2; OR: 1.07, 95% CI: 1.01–1.13 p = 0.015 in stage 3) positively correlated with psoriasis, and there had no correlation between HDL-C and psoriasis. The results of the secondary analysis were consistent with the results of primary analysis.ConclusionsMendelian randomization (MR) findings provide genetic evidence for causal link between psoriasis and blood lipid. It may be meaningful to monitor and control blood lipid level for a management of psoriasis patients in clinic.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not bedivided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.

The Dutch CELEX data is derived from R.H. Baayen, R. Piepenbrock & L. Gulikers, The CELEX Lexical Database (CD-ROM), Release 2, Dutch Version 3.1, Linguistic Data Consortium, University of Pennsylvania, Philadelphia, PA, 1995.Apart from orthographic features, the CELEX database comprises representations of the phonological, morphological, syntactic and frequency properties of lemmata. For the Dutch data, frequencies have been disambiguated on the basis of the 42.4m Dutch Instituut voor Nederlandse Lexicologie text corpora.To make for greater compatibility with other operating systems, the databases have not been tailored to fit any particular database management program. Instead, the information is presented in a series of plain ASCII files, which can be queried with tools such as AWK and ICON. Unique identity numbers allow the linking of information from different files.This database can be divided into different subsets:· orthography: with or without diacritics, with or without word division positions, alternative spellings, number of letters/syllables;· phonology: phonetic transcriptions with syllable boundaries or primary and secondary stress markers, consonant-vowel patterns, number of phonemes/syllables, alternative pronunciations, frequency per phonetic syllable within words;· morphology: division into stems and affixes, flat or hierarchical representations, stems and their inflections;· syntax: word class, subcategorisations per word class;· frequency of the entries: disambiguated for homographic lemmata.

The International Stroke Trial (IST) dataset includes data on 19,435 patients and 112 variables. For each randomized patient, data were extracted on the variables assessed at randomization, at the early outcome point, and at 6-months. This dataset provides a source of primary data and is available for public use for the conduct of secondary analyses and in the planning of future trials particularly in older patients and in resource-poor settings given the age distribution of the dataset.

This site contains the NeSys archive on structure and structure-function data about brain map transformations in the cerebellar system of the rat. This archive presents data not illustrated in the original publications, downloadable original data sets, interactive illustration sequences, including 3-D models. The repository is based on 5 original publications. The publications deal with: - organization of projections to the pontine nuclei from three cortical areas: primary and secondary somatosensory areas (SI and SII), and the primary motor cortex (MI) - organization of pontine neurons projecting to somatosensory representations in the posterior cerebellum The data are also included in the FACCS application, a relational database application with embedded analytical tools, available via the The Rodent Brain Workbench (www.rbwb.org). Sponsors: NeSys Research and Database development is supported by The Research Council of Norway, The European Community (grants QLRT-2000-02256 and QLG3-CT 1999-00763), The Norwegian Consortium for High Performance Computing, and The Jahre Foundation.

Abstract copyright UK Data Service and data collection copyright owner.The National Pupil Database (NPD) is one of the richest education datasets in the world. It is a longitudinal database which links pupil characteristics to information about attainment for those who attend schools and colleges in England. There are a range of data sources in the NPD providing detailed information about children's education at different stages (pre-school, primary and secondary education and further education). Pupil level information was first collected in January 2002 as part of the Pupil Level Annual Schools Census (PLASC). The School Census replaced the PLASC in 2006 for secondary schools and in 2007 for nursery, primary and special schools. The School Census is carried out three times a year in the spring, summer and autumn terms (January, May and October respectively) and provides the Department for Education with both pupil and school-level data. The NPD is available through the UK Data Archive in three tiers. Tiers two and three are the most sensitive and must be accessed via the Archive's safe room, whereas tier four can be accessed remotely through the Archive's Secure Lab. Tier two contains individual pupil level data which is identifiable and sensitive. Individual pupil level extracts include sensitive information about pupils and their characteristics, including items described as 'sensitive personal data' within the UK Data Protection Act 1998 which have been recoded to become less sensitive. Examples of sensitive data items include ethnic group major, ethnic group minor, language group major, language group minor, Special Educational Needs and eligibility for Free School Meals. Tier three represents aggregated school level data which is identifiable and sensitive. Included are aggregated extracts of school level data from the Department of Education's School Level Database which include items described as 'sensitive personal data' within the Data Protection Act 1998 and could include small numbers and single counts. For example, there is 1 white boy eligible for Free School Meals in school x who did not achieve level 4 in English and maths at Key Stage 2. Tier four represents less sensitive data than tiers two and three. Included are individual pupil level extracts that do not contain information about pupils and their characteristics which are considered to be identifying or described as sensitive personal data within the Data Protection Act 1998. For example, the extracts may include information about pupil attainment, prior attainment, progression and pupil absences but do not include any identifying data items like names and addresses and any information about pupil characteristics other than gender. Extracts from the NPD are also available directly from the Department of Education through GOV.UK's National pupil database: apply for a data extract web page. The fourth edition (September 2017) includes a data file and documentation for the year 2016.

Dataset description:

The Encyclopedia of Domains (TED) is a joint effort by CATH (Orengo group) and the Jones group at University College London to identify and classify protein domains in AlphaFold2 models from AlphaFold Database version 4, covering over 188 million unique sequences and 324 million domain assignments.

In this data release, we will be making available to the community a table of domain boundaries and additional metadata on quality (pLDDT, globularity, number of secondary structures), taxonomy and putative CATH SuperFamily or Fold assignments for all 324 million domains in TED100.

For all chains in the TED-redundant dataset, the attached file contains boundaries predictions, consensus level and information on the TED100 representative.

Additionally, an archive with chain-level consensus domain assignments are available for 21 model organisms and 25 global health proteomes:

For both TED100 and TEDredundant we provide domain boundaries predictions outputted by each of the three methods employed in the project (Chainsaw, Merizo, UniDoc).

We are making available 7,427 novel folds PDB files, identified during the TED classification process with an annotation table sorted by novelty.

Please use the gunzip command to extract files with a '.gz' extension.

CATH annotations have been assigned using the FoldSeek algorithm applied in various modes and the FoldClass algorithm, both of which are used to report significant structural similarity to a known CATH domain.
Note: The TED protocol differs from that of our standard CATH Assignment protocol for superfamily assignment, which also involves HMM-based protocols and manual curation for remote matches.

This dataset contains:

• ted_214m_per_chain_segmentation.tsv
  The file contains all 214M protein chains in TED with consensus domain boundaries and proteome information in the following columns.
  1. AFDB_model_ID: chain identifier from AFDB in the format AF-
• ted_365m_domain_boundaries_consensus_level.tsv.gz
  The file contains all domain assignments in TED100 and TED-redundant (365M) in the format:
  1. TED_ID: TED domain identifier in the format AF-
• ted_100_324m.domain_summary.cath.globularity.taxid.tsv and novel_folds_set.domain_summary.tsv are header-less with the following columns separated by tabs (.tsv).
• ted_324m_seq_clustering.cathlabels.tsv
  The file contains the results of the domain sequences clustering with MMseqs2.
  Columns:
  1. Cluster_representative
  2. Cluster_member
  3. CATH code assignment if available i.e. 3.40.50.300 for a domain with a homologous match or 3.20.20 for a domain matching at the fold level in the CATH classification
  4. CATH assignment type - either Foldseek-T, Foldseek-H or Foldclass
• novel_folds_set.domain_summary.tsv is sorted by novelty.
  1. ted_id - TED domain identifier in the format AF-
• Domain assignments for TED redundant using single-chain and multi-chain consensus in ted_redundant_39m.multichain.consensus_domain_summary.taxid.tsv and ted_redundant_39m.singlechain.consensus_domain_summary.taxid.tsv
  The files contain a header with the following fields. Each column is tab-separated (.tsv).
  1. TED_redundant_id - TED chain identifier in the format AF-
• and ted_redundant_39m.singlechain.consensus_domain_summary.taxid.tsv
  The file contains a header with the following fields. Each column is tab-separated (.tsv).
  1. TED_redundant_id - TED chain identifier in the format AF-
• novel_folds_set_models.tar.gz contains PDB files of all novel folds identified in TED100.
• All per-tool domain boundaries predictions are in the same format with the following columns.
  1. TED_chainID - TED chain identifier in the format AF-
• Domain boundaries predictions share the same format, with each segment separated by '_' and segment boundaries (start,stop) separated by '-'
  
  i.e.domain prediction by Merizo for AF-A0A000-F1-model_v4
  AF-A0A000-F1-model_v4 e8872c7a0261b9e88e6ff47eb34e4162 394 2 10-52_289-394,53-288 0.90077
  
  Merizo predicts one continuous domain and a discontinuous domain,
  Domain1 (discontinuous): 10-52_289-394
  segment1: 10-52
  segment2: 289-394
  Domain 2 (continuous):
  segment 1: 53-288
• ted-tools-main.zip - copy of the https://github.com/psipred/ted-tools repository, containing tools and software used to generate TED.
• cath-alphaflow-main.zip - copy of CATH-AlphaFlow, used to generate globularity scores for TED domains.
• ted-web-master.zip - copy of TED-web, containing code to generate the web interface of TED (https://ted.cathdb.info)
• gofocus_data.tar.bz2 - GOFocus model weights