The Structural Classification of Proteins (SCOP) database is a comprehensive ordering of all proteins of known structure, according to their evolutionary and structural relationships. Protein domains in SCOP are hierarchically classified into families, superfamilies, folds and classes. The continual accumulation of sequence and structural data allows more rigorous analysis and provides important information for understanding the protein world and its evolutionary repertoire. SCOP participates in a project that aims to rationalize and integrate the data on proteins held in several sequence and structure databases. As part of this project, starting with release 1.63, we have initiated a refinement of the SCOP classification, which introduces a number of changes mostly at the levels below superfamily. The pending SCOP reclassification will be carried out gradually through a number of future releases. In addition to the expanded set of static links to external resources, available at the level of domain entries, we have started modernization of the interface capabilities of SCOP allowing more dynamic links with other databases.

CCA: Connected Component Analysis, SCPS: Spectral Clustering.

The global data center cooling market is set to reach USD 49.2B by 2035, driven by AI, IoT, cloud demand, and a 14.3% CAGR from 2025 to 2035.

US Tariff Impact on Market

US tariffs on semiconductor components used in data center chips could impact the overall cost of production. As the demand for GPUs and other advanced chips used in data centers grows, tariffs on components such as processors, memory units, and storage chips could raise production costs.

This price increase may be passed onto end consumers, particularly large data centers, which account for 64.1% of the market. Given the growing importance of data processing in sectors like BFSI (which accounts for 23.0% of the market), these tariffs could slow down investments in upgrading existing infrastructure.

While the North American market currently leads, the rising costs could lead to increased competition from global manufacturers, reducing the market share in the U.S. However, as demand for high-performance computing continues, these short-term challenges may be offset by long-term growth driven by the increasing reliance on cloud services and data-intensive applications.

US Tariff Impact on Sectors

• GPU Chips: 4%-6%
• Data Center Chips (General): 5%-7%
• Semiconductor Components: 3%-5%

Economic Impact

Tariffs on semiconductor components could increase production costs for data center chips, raising prices across sectors, particularly in large data centers. This would impact enterprises relying on large-scale data storage and processing, particularly in high-demand sectors like BFSI, potentially slowing the pace of infrastructure upgrades and investments.

Geographical Impact

North America, which currently leads the market with 38.4% share, may face slowed growth due to higher prices caused by tariffs on imported components. The U.S. could experience reduced competitiveness in the global market, as manufacturers in other regions with fewer tariffs could offer more affordable alternatives.

Business Impact

Businesses in the data center chip sector may face lower profit margins due to increased production costs from tariffs. Companies might be forced to pass the increased costs onto customers, which could affect demand, particularly among smaller enterprises or those in price-sensitive industries, potentially slowing market growth.

➤➤ Request sample reflecting US tariffs @ https://market.us/report/data-center-chip-market/free-sample/

SUPERFAMILY is a library of profile hidden Markov models that represent all proteins of known structure. The library is based on the SCOP classification of proteins: each model corresponds to a SCOP domain and aims to represent the entire SCOP superfamily that the domain belongs to. SUPERFAMILY is based at the University of Bristol, UK.

Supplementary Dataset 1. PDB IDs for the CATH and SCOP data. This dataset stores the PDB IDs for the CATH and SCOP data used in the analysis.

Analysis of ‘CDTFA SCOP Teams’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/51acd32f-5540-4ef1-873d-54ff9be68026 on 27 January 2022.

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

CDTFA Statewide Compliance & Outreach Program (SCOP) Team Areas These boundaries are used by CDTFA staff in for in-state accounts.

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

The cloud database and DBaaS market size is projected to be valued at US$ 18,611.2 million in 2023 and is expected to rise to US$ 69,806.5 million by 2033. The sales of cloud databases and DBaaS are anticipated to expand at a significant CAGR of 14.1% during the forecast period. Various factors propelling the demand for Cloud Database and DBaaS market are discussed below.

Scope of the Report

Demand for data center power management in Japan is expected to expand at a CAGR of 9.2% through 2034. A valuation of US$ 955.4 million is anticipated for the data center power management industry in Japan in 2024. It is estimated to generate US$ 2,298.3 million in revenue from data center power systems by 2034.

Category-wise insights

Scope of the Report

Information

The diverse publicly available compound/bioactivity databases constitute a key resource for data-driven applications in chemogenomics and drug design. Analysis of their coverage of compound entries and biological targets revealed considerable differences, however, suggesting benefit of a consensus dataset. Therefore, we have combined and curated information from five esteemed databases (ChEMBL, PubChem, BindingDB, IUPHAR/BPS and Probes&Drugs) to assemble a consensus compound/bioactivity dataset comprising 1144803 compounds with 10915362 bioactivities on 5613 targets (including defined macromolecular targets as well as cell-lines and phenotypic readouts). It also provides simplified information on assay types underlying the bioactivity data and on bioactivity confidence by comparing data from different sources. We have unified the source databases, brought them into a common format and combined them, enabling an ease for generic uses in multiple applications such as chemogenomics and data-driven drug design.

The consensus dataset provides increased target coverage and contains a higher number of molecules compared to the source databases which is also evident from a larger number of scaffolds. These features render the consensus dataset a valuable tool for machine learning and other data-driven applications in (de novo) drug design and bioactivity prediction. The increased chemical and bioactivity coverage of the consensus dataset may improve robustness of such models compared to the single source databases. In addition, semi-automated structure and bioactivity annotation checks with flags for divergent data from different sources may help data selection and further accurate curation.

Structure and content of the dataset

The dataset was created using the Konstanz Information Miner (KNIME) (https://www.knime.com/) and was exported as a CSV-file and a compressed CSV-file.

Except for the canonical SMILES columns, all columns are filled with the datatype ‘string’. The datatype for the canonical SMILES columns is the smiles-format. We recommend the File Reader node for using the dataset in KNIME. With the help of this node the data types of the columns can be adjusted exactly. In addition, only this node can read the compressed format.

Column content:

• ChEMBL ID, PubChem ID, IUPHAR ID: chemical identifier of the databases
• Target: biological target of the molecule expressed as the HGNC gene symbol
• Activity type: for example, pIC₅₀
• Assay type: Simplification/Classification of the assay into cell-free, cellular, functional and unspecified
• Unit: unit of bioactivity measurement
• Mean columns of the databases: mean of bioactivity values or activity comments denoted with the frequency of their occurrence in the database, e.g. Mean C = 7.5 *(15) -> the value for this compound-target pair occurs 15 times in ChEMBL database
• Activity check annotation: a bioactivity check was performed by comparing values from the different sources and adding an activity check annotation to provide automated activity validation for additional confidence
  • no comment: bioactivity values are within one log unit;
  • check activity data: bioactivity values are not within one log unit;
  • only one data point: only one value was available, no comparison and no range calculated;
  • no activity value: no precise numeric activity value was available;
  • no log-value could be calculated: no negative decadic logarithm could be calculated, e.g., because the reported unit was not a compound concentration
• Ligand names: all unique names contained in the five source databases are listed
• Canonical SMILES columns: Molecular structure of the compound from each database
• Structure check: To denote matching or differing compound structures in different source databases
  • match: molecule structures are the same between different sources;
  • no match: the structures differ;
  • 1 source: no structure comparison is possible, because the molecule comes from only one source database.
• Source: From which databases the data come from

The global data governance market is estimated to be valued at USD 4.1 billion in 2024. Over the projection period, it is expected to witness a CAGR of 18.5% and reach a total industry size of USD 22.5 billion by 2034.

Country-wise Insights

Category-wise Insights

US Tariff Impact on Market

US tariffs on key components of modular data centers, such as servers, cooling systems, and power units, could raise the overall cost of production, affecting the affordability of these data center solutions. As large enterprises, which account for 65.3% of the market, require scalable and cost-effective solutions, the increased costs could lead to a slowdown in demand, particularly for small and medium enterprises that may struggle with higher operational expenses.

However, the growing demand for flexible and energy-efficient data center solutions, driven by IT and telecommunications, could help mitigate the impact of tariff-induced price hikes. Larger enterprises may also seek alternative sourcing strategies to reduce costs, but the short-term impact could affect growth in the modular data center market.

US Tariff Impact on Sectors

• Modular Data Center Solutions: 4%-6%
• Cooling Systems: 5%-7%
• IT Infrastructure: 3%-5%

Economic Impact

Tariffs could increase production costs for modular data center components, raising prices for consumers. This could affect both large enterprises and SMEs, especially in regions with high cost sensitivity. Higher prices may slow the adoption of modular data centers, particularly for businesses with tight IT infrastructure budgets.

Geographical Impact

North America, the dominant region, will experience the most significant impact from tariffs due to its reliance on imported data center components. These increased costs may reduce demand in the U.S., slowing the growth of modular data centers, particularly in industries like IT and telecommunications that rely on cost-efficient solutions.

Business Impact

Companies in the modular data center market may face margin compression due to increased component costs from tariffs. Larger enterprises may absorb the costs, but SMEs could be adversely affected by price increases, resulting in lower adoption rates. This could also slow growth in North America's highly competitive data center market.

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Package ‘dcGOR’ (version 1.0.3) including source code, documentation and data. (GZ)

The cancer registry software market is projected to be worth US$ 87.4 million in 2024. The market is anticipated to reach US$ 236.0 million by 2034. The market is further expected to surge at a CAGR of 10.4% during the forecast period 2024 to 2034.

Country-wise Insights

Category-wise Insights

Report Scope

This offer includes high-quality carbon emission datasets for a broad range of finished consumer products. Designed for Scope 3 accounting and cradle-to-gate analysis, this dataset enables companies to assess and report the CO₂ impact of purchased goods and downstream emissions.

The data covers categories such as electronics, appliances, furniture, textiles, and packaging – with detailed emission factors reflecting real-world product compositions and manufacturing pathways. This allows for supplier-independent benchmarking, identification of high-impact product categories, and integration into product carbon footprint calculations.

In light of evolving reporting standards such as ISO 14067, GHG Protocol, CBAM, and CSRD, companies need reliable Scope 3 data to fulfill disclosure requirements and quantify their reduction potential. This dataset helps sustainability teams and procurement departments make informed decisions based on comparable, harmonized carbon data.

Available via API, CSV download, or the sustamize Data Platform.

For more details, please visit: https://docs.sustamizer.com/knowledge-hub/database-overview/consumer-products

DOMMINO is a comprehensive structural database on macromolecular interactions. As of June, 2011, it contains more than 407,000 binary interactions. The distinctive features of DOMMINO are: # Automated updates: DOMMINO is fully automated and is designed to update itself on a weekly basis, one day after a PDB weekly update. Thus, the community will be able to study macromolecular interactions almost immediately after they are released by PDB. # Coverage of non-domain mediated interactions: In addition to domain-domain and domain-peptide interactions the database characterizes the interaction between domains and unstructured protein regions that are not parts of a domain, such as inter-domain linkers and N- and C-termini. The interactions that involve the latter unstructured parts of proteins have been included to the database for the first time providing additional ~186,000 interactions (~45% of the total number of interactions, as of June, 2011). # Coverage of new structural domains: DOMMINO employs one of the most accurate structural classifications of proteins, SCOP. In addition to the existing SCOP-annotated domains, we employ a state-of-the-art machine learning approach to classify newer protein structures into existing SCOP families. With the progress of structural genomics, we do not expect a significant growth of the number of structurally novel folds or protein families and therefore our method allows covering almost all new protein structures. In total, using this predictive approach has allowed us to add more than 261,000 new interactions, almost twice as many as existing SCOP-annotated interactions. # The web-interface is designed to give the user a possibility of a flexible search as well as the capability to study macromolecular interactions in a PDB structure at the interaction network level and at the individual interface level. The web interface of the DOMMINO database includes a comprehensive list of help topics linked to the specific actions. In addition, we have designed a step-by-step tutorial that covers all aspects of working with the data from DOMMINO using the web interface.

Database Security Market is expected to grow at a high CAGR during the forecast period 2023-2030 | DataM Intelligence

This data for global, regional (EU-27), and country-specific (G20 member countries) energy and emission pathways required to achieve a defined carbon budget of under 450 Gt/CO2, developed to limit the mean global temperature rise to 1.5°C, over 50% likelihood. The data were calculated with the 1.5°C sectorial pathways of the One Earth Climate Model—an integrated energy assessment model devised at the University of Technology Sydney (UTS). The data consist of the following six zip-folder datasets (refer to Section 2 for an explanation of the data): 1. Appendix folder: Each file contains one worksheet, which summarizes the overall 1.5°C scenario. 2. Sector folder (XLSX): Each file contains one worksheet, which summarizes the industry sectors analysed. 3. Sector folder (CSV): The data contained are the same as those described in point 2. 4. Sector emissions folder: Each file contains one worksheet, which summarizes the total annual emissions for each industry sector. 5. Scope emissions folder (XLSX): Each file contains one worksheet, which summarizes the total annual emissions for each industry sector—with the additional specificity of emission scope. 6. Scope emissions folder (CSV): The data contained are the same as those described in point 5. Methods The data consist of the following six zipped dataset folders, each containing 21 separate files for each of the areas assessed. 1. Appendix zip folder: contains 21 XLSX files. Each file contains one worksheet, which summarizes the overall 1.5 °C scenario. This tab is called the ‘Appendix’ and contains: electricity generation (TWh/a), transport—final energy (PJ/a), heat supply and air conditioning (PJ/a), installed capacity (GW), final energy demand (PJ/a), energy-related CO2 emissions (million tons/a), and primary energy demand (PJ/a). 2. Sector zip folder (XLSX): contains 21 XLSX files. Each file contains one worksheet, which summarizes the industry sectors analysed. Key industry metrics are provided, such as the energy and carbon intensities of the GICS sectors analysed. Due to industry specificity—and the choice of methodology—the units of data vary between the different sectors. 3. Sector zip folder (CSV): contains 21 CSV files. The data contained are the same as those described in point 2. However, the data have been organized in a database layout and saved in the CSV file format, significantly improving data parsing. 4. Sector emission zip folder: contains 21 XLSX files. Each file contains one worksheet, which summarizes the total annual emissions (MtCO2/a) for each industry sector. 5. Scope emissions zip folder (XLSX): contains 21 XLSX files. Each file contains one worksheet, which summarizes the total annual emissions (MtCO2/a) for each industry sector—and specifies the emission scopes. This tab also provides an additional breakdown of emissions into the categories of CO2 and total GHG emissions. Two accounting methodologies are presented: (i) the OECM approach, which defines Scope 1 emissions as those related to heat and energy use; and (ii) the production-centric approach, which places the emission burden of other non-energy and Scope 3 emissions on the producer, because they are categorized as Scope 1 emissions. 6. Scope emissions zip folder (CSV): contains 21 CSV files. The data contained are the same as those described in point 5. However, the data have been organized in a database layout and saved in the CSV file format to improve data parsing. The six datasets are summarized in Table 1, with further information on the data presented in the following sub-sections. Table 1: Overview of the data files/datasets

Label

Name of data file/dataset

File types

Data repository and identifier (DOI or accession number)

Dataset 1

Appendix

XLSX

https://doi.org/10.5061/dryad.cz8w9gj82

Dataset 2

Sector_XLSX

XLSX

https://doi.org/10.5061/dryad.cz8w9gj82

Dataset 3

Sector_CSV

CSV

https://doi.org/10.5061/dryad.cz8w9gj82

Dataset 4

Sector_Emission

XLSX

https://doi.org/10.5061/dryad.cz8w9gj82

Dataset 5

Scope_Emission_XLSX

XLSX

https://doi.org/10.5061/dryad.cz8w9gj82

Dataset 6

Scope_Emission_CSV

CSV

https://doi.org/10.5061/dryad.cz8w9gj82

1.1. Description of data parameters The datasets contain the following scenario input parameters: 1. Market development: current and assumed development of the demand by sector, such as cement produced, passenger kilometers travelled, or assumed market volume in US$2015 gross domestic product (GDP). 2. Energy intensity—activity based: energy use per unit of service and/or product; for example, in megajoules (MJ) per passenger kilometer travelled (MJ/pkm), MJ per ton of steel (MJ/ton steel), aluminum, or cement. 3. Energy intensity—finance based: energy use per unit of investment in MJ per US$ GDP (MJ/$GDP) contributed by, for example, the forestry or agricultural sector. The dataset contains the following scenario output parameters: 4. Carbon intensity: current and future carbon intensities per unit of product or service; for example, in tons of CO2 per ton of steel produced (tCO2/ton steel) or grams of carbon dioxide per passenger kilometer (gCO2/pkm). 5. Scope 1, 2, and 3 emissions: datasets for each of the industry sectors and countries analysed. In addition to the emissions data, the deviations of the emissions from those of the year 2019 are provided. 6. Country scenarios: complete country scenario datasets of historical data (2012, 2015–2020) and future projections (2025–2050 in 5-year increments). Energy demand and supply data by technology, fuel, and sector are provided, including the overall energy and carbon emissions balance of the country analysed. 1.2. Geographic resolution: country data provided The dataset contains data for the following 21 countries and regions: · Regions: global, EU-27 · Countries: G20 member countries—Canada, USA, Mexico, Brazil, Argentina, Germany, France, Italy, United Kingdom, Türkiye, Russian Federation, Saudi Arabia, South Africa, Indonesia, India, China, Japan, South Korea, and Australia 1.3. Sectorial resolution: industry sector data provided The dataset contains data for the following industry sectors: Agriculture & food processing, forestry & wood products, chemical industry, aluminum industry, construction and buildings, water utilities, textile & leather industry, steel industry, cement industry, transport sector (aviation: freight & passenger transport; shipping: freight & passenger transport; and road transport: freight & passenger transport). 1.4. Time resolution The scenario data are provided for the years 2017, 2018, 2019, 2020, 2025, 2030, 2035, 2040, 2045, and 2050.

Scope-3 Data Exchange Market Outlook

According to our latest research, the global Scope-3 Data Exchange market size reached USD 1.9 billion in 2024, reflecting a significant uptick in demand for advanced carbon accounting and sustainability reporting solutions across industries. The market is expected to register a robust CAGR of 18.7% through the forecast period, propelling the industry to an estimated USD 9.6 billion by 2033. This rapid growth is primarily driven by tightening regulatory requirements, heightened corporate focus on ESG (Environmental, Social, and Governance) initiatives, and the urgent need for accurate, transparent tracking of Scope-3 emissions across global supply chains.

One of the most significant growth factors for the Scope-3 Data Exchange market is the escalating pressure from regulatory bodies and stakeholders for comprehensive carbon disclosure. Governments worldwide are introducing stricter mandates for carbon reporting, especially pertaining to Scope-3 emissions, which encompass indirect emissions occurring in a company’s value chain. The European Union’s Corporate Sustainability Reporting Directive (CSRD), the U.S. SEC’s proposed climate disclosure rules, and similar regulations in Asia Pacific are compelling organizations to invest in robust data exchange platforms and software. This regulatory momentum is not only driving immediate demand but is also encouraging the development and adoption of innovative solutions that can seamlessly aggregate, validate, and exchange carbon data across complex, multi-tiered supply chains.

Another major growth driver is the increasing recognition among corporations that Scope-3 emissions often represent the largest portion of their carbon footprint, sometimes accounting for over 70% of total emissions. As a result, organizations are prioritizing end-to-end visibility and collaboration with suppliers, partners, and customers to capture accurate, real-time data. This has led to a surge in demand for integrated platforms and services capable of automating data collection, standardizing reporting formats, and enabling continuous improvement in sustainability performance. The integration of advanced technologies such as artificial intelligence, blockchain, and IoT sensors is further enhancing the reliability and scalability of Scope-3 data exchange solutions, making them indispensable tools for forward-thinking enterprises.

The evolving landscape of corporate sustainability and investor expectations is also contributing to the market’s expansion. Investors, consumers, and NGOs are increasingly scrutinizing companies’ climate strategies and carbon disclosures, pushing organizations to go beyond mere compliance. This shift is fostering a culture of transparency, accountability, and innovation, where Scope-3 data exchange platforms play a pivotal role in enabling organizations to set science-based targets, benchmark performance, and communicate progress effectively. As companies across manufacturing, energy, transportation, retail, and other sectors embrace digital transformation and ESG integration, the Scope-3 Data Exchange market is poised for sustained, long-term growth.

Regionally, North America and Europe are leading the adoption curve, driven by stringent regulations, proactive sustainability initiatives, and a mature technological ecosystem. However, Asia Pacific is emerging as a high-growth region, fueled by rapid industrialization, increasing regulatory alignment with global standards, and growing awareness of climate risks among Asian conglomerates. Latin America and the Middle East & Africa are also witnessing steady uptake, particularly among multinational corporations and export-oriented industries seeking to align with global best practices. As regional markets continue to evolve, cross-border data exchange standards and interoperability will become critical, further shaping the competitive dynamics and innovation trajectory of the Scope-3 Data Exchange market.

Component Analysis

The Scope-3 Data Exchange market is segmented by component into Software, Services, and Platforms, each playing a distinct role in enabling organizations to capture, manage, and report on indirect emissions data. Software solutions form the backbone of the market, offering specialized tools for carbon accounting, data integration, and workflow automation. These applications are designed to handle large volumes of disparate data from internal syst

CATH Domain Classification List (latest release) - protein structural domains classified into CATH hierarchy.