PredictLeads Job Openings Data provides real-time hiring insights sourced directly from company websites, ensuring the highest level of accuracy and freshness. Unlike job boards that rely on aggregated listings, our dataset delivers unmatched granularity on job postings, salary trends, and workforce demand - making it a powerful tool for HR, talent acquisition, and market analysis.

Use Cases: ✅ Job Boards Enhancement – Improve job listings with, high-quality postings. ✅ HR Consulting – Analyze hiring trends to guide workforce planning strategies. ✅ Employment Analytics – Track job market shifts, salary benchmarks, and demand for skills. ✅ HR Operations – Optimize recruitment pipelines with direct employer-sourced data. ✅ Competitive Intelligence – Monitor hiring activities of competitors for strategic insights.

Key API Attributes:

• id (string, UUID) – Unique job posting identifier.
• title (string) – Job title as posted by the employer.
• description (string) – Full job description.
• url (string, URL) – Direct link to the job posting.
• first_seen_at (ISO 8601 date-time) – When the job was first detected.
• last_seen_at (ISO 8601 date-time) – When the job was last detected.
• contract_types (array of strings) – Employment type (e.g., full-time, contract).
• categories (array of strings) – Job categories (e.g., engineering, sales).
• seniority (string) – Job seniority level (e.g., manager, entry-level).
• salary_data (object) – Salary range, currency, and converted USD values.
• location_data (object) – City, country, and region details.
• tags (array of strings) – Extracted skills and keywords from job descriptions.

PredictLeads Docs: https://docs.predictleads.com/v3/guide/job_openings_dataset

The global Human Resource Management (HRM) tools market is experiencing robust growth, driven by the increasing need for efficient workforce management, automation of HR processes, and the rising adoption of cloud-based solutions. The market, estimated at $25 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $70 billion by 2033. This expansion is fueled by several key factors: the growing preference for cloud-based HRM systems offering scalability and cost-effectiveness, the increasing demand for analytics-driven insights to improve workforce productivity and decision-making, and the rising adoption of artificial intelligence (AI) and machine learning (ML) for tasks like recruitment and employee engagement. Significant investments by major players like ADP, Workday, and Oracle are further boosting market innovation and expansion. The market segmentation reveals substantial opportunities within the large enterprise segment, which accounts for a significant share of market revenue, due to their greater budget capacity and complex HR needs. However, the SME segment also presents significant potential for future growth, driven by the increasing affordability and accessibility of cloud-based solutions. While the North American market currently holds the largest market share, rapid digitalization in regions like Asia Pacific is expected to fuel significant regional growth in the coming years. The market's growth, however, faces certain constraints. Data security and privacy concerns remain a major challenge, alongside the high initial investment costs for some solutions, potentially hindering wider adoption among smaller businesses. The integration complexities of HRM tools with existing enterprise resource planning (ERP) systems can also impede implementation. Furthermore, resistance to change within organizations and the need for robust employee training are significant factors impacting market growth. Overcoming these challenges through enhanced security measures, affordable pricing models, and streamlined integration solutions is crucial for sustainable market growth and wider adoption. The continued innovation in areas like AI-powered recruitment, employee experience platforms, and predictive analytics will shape the future landscape of the HRM tools market.

The HR Analytics Software market is experiencing significant growth with a market size of USD 1349 million in 2025, driven by increasing demand for data-driven decision-making in HR processes. The market is projected to expand at a CAGR of 6.6% during the forecast period, reaching a value of USD 2142 million by 2033. The rising adoption of cloud-based and web-based solutions for HR analytics, along with the increasing need for talent management and workforce optimization, is driving the market's expansion. Key market players include HR Bakery, Optimity, IBM, PeopleStreme, Professional Advantage, Bullhorn, Flock, talentReef, Oracle, Viventium, Adrenalin, 360 Feedback, and others. The market is segmented based on type (cloud-based and web-based) and application (large enterprises and SMEs). The North American region holds a dominant market share, followed by Europe and Asia Pacific. The growing adoption of HR analytics by large enterprises and the increasing demand for data-driven HR practices in emerging economies contribute to the market's growth.

This activity was part of the Human Resources for Health 2030 Program (HRH2030), a global program to build the accessible, available, acceptable, and high-quality health workforce needed in low- and middle-income countries. HRH2030 aligns with the overall approach that supports the goals of other U.S. government strategies — achieving an AIDS-free generation by 2030, Ending Preventable Child and Maternal Deaths, and Family Planning 2020 — by strengthening health systems to deliver universal health coverage as part of the post-2015 Sustainable Development Goals.

This group includes the provision of infrastructure for hosting, data processing services and related activities, as well as search facilities and other portals for the Internet.

Market Overview The Integrated HR Service Delivery Solutions market is projected to expand at a CAGR of 4.9% from 2025 to 2033, reaching a value of million by 2033. This growth is driven by factors such as the increasing adoption of cloud-based and web-based solutions, the growing demand for centralized HR systems, and the need for improved employee experience. The market is segmented by application into large enterprises and SMEs, and by type into cloud-based and web-based solutions. Competitive Landscape The Integrated HR Service Delivery Solutions market is highly competitive, with a number of major players offering a wide range of solutions. The top companies in the market include SAP SuccessFactors, Meta4, Willis Towers Watson, Oracle, PeopleDoc, ServiceNow, and Dovetail Software. These companies offer a variety of solutions, including cloud-based and web-based HR platforms, employee self-service portals, and analytics and reporting tools. The market is also characterized by the presence of a number of regional and local players. Integrated HR Service Delivery Solutions: Unifying Workforce Management

The Croatian web corpus CLASSLA-web.hr 1.0 is based on the MaCoCu-hr 2.0 web corpus crawl (http://hdl.handle.net/11356/1806), which was additionally cleaned and enriched with linguistic and genre information. The CLASSLA-web.hr corpus is a part of the South Slavic CLASSLA-web corpus collection, which is the first collection of comparable corpora that encompasses the entire South Slavic language group.

The MaCoCu-hr 2.0 crawl was built by crawling the ".hr" internet top-level domain in 2021 and 2022, as well as extending the crawl dynamically to other domains. During the development of CLASSLA-web corpora, the MaCoCu web crawls were cleaned by removing paragraphs that are not in the target language, and by removing very short texts (less than 75 words or consisting only of paragraphs shorter than 70 characters). The corpus was also linguistically annotated with the CLASSLA-Stanza pipeline (https://github.com/clarinsi/classla). The linguistic processing involved tokenization, morphosyntactic annotation, and lemmatization. Additionally, the corpus was automatically annotated with genres using the Transformer-based X-GENRE classifier (https://huggingface.co/classla/xlm-roberta-base-multilingual-text-genre-classifier). The following genre categories are used: News, Information/Explanation, Promotion, Opinion/Argumentation, Instruction, Legal, Prose/Lyrical, Forum, Other and Mix.

The corpus is available in vertical format, as used by Sketch Engine and CWB concordancers. Information is provided on the text-, paragraph-, sentence- and token-level. Each text is accompanied by the following metadata: text id, title, url, domain, top-level domain (tld, e.g., "com"), and predicted genre category. Each text is divided into paragraphs that are accompanied by the following metadata: paragraph id, the automatically identified language of the text in the paragraph, and paragraph quality. For quality, labels, such as "short" or "good" are assigned based on paragraph length, URL and stopword density via the jusText tool (https://corpus.tools/wiki/Justext). Paragraphs are further divided into sentences that have as metadata their sentence id. Inside sentences, tokens are provided in tabular format with their linguistic annotation. Details about the structural and positional attributes are also given in the accompanying registry file which was used to install the corpus on the CLARIN.SI concordancers.

Notice and take down: Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please: (1) Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted. (2) Clearly identify the copyrighted work claimed to be infringed. (3) Clearly identify the material that is claimed to be infringing and information reasonably sufficient in order to allow us to locate the material. (4) Please write to the contact person for this resource whose email is available in the full item record. We will comply with legitimate requests by removing the affected sources from the next release of the corpus.

LinkedIn companies use datasets to access public company data for machine learning, ecosystem mapping, and strategic decisions. Popular use cases include competitive analysis, CRM enrichment, and lead generation.

Use our LinkedIn Companies Information dataset to access comprehensive data on companies worldwide, including business size, industry, employee profiles, and corporate activity. This dataset provides key company insights, organizational structure, and competitive landscape, tailored for market researchers, HR professionals, business analysts, and recruiters.

Leverage the LinkedIn Companies dataset to track company growth, analyze industry trends, and refine your recruitment strategies. By understanding company dynamics and employee movements, you can optimize sourcing efforts, enhance business development opportunities, and gain a strategic edge in your market. Stay informed and make data-backed decisions with this essential resource for understanding global company ecosystems.

Dataset Features

• timestamp: Represents the date and time when the company data was collected.
• id: Unique identifier for each company in the dataset.
• company_id: Identifier linking the company to an external database or internal system.
• url: Website or URL for more information about the company.
• name: The name of the company.
• about: Brief description of the company.
• description: More detailed information about the company's operations and offerings.
• organization_type: Type of the organization (e.g., private, public).
• industries: List of industries the company operates in.
• followers: Number of followers on the company's platform.
• headquarters: Location of the company's headquarters.
• country_code: Code for the country where the company is located.
• country_codes_array: List of country codes associated with the company (may represent various locations or markets).
• locations: Locations where the company operates.
• get_directions_url: URL to get directions to the company's location(s).
• formatted_locations: Human-readable format of the company's locations.
• website: The official website of the company.
• website_simplified: A simplified version of the company's website URL.
• company_size: Number of employees or company size.
• employees_in_linkedin: Number of employees listed on LinkedIn.
• employees: URL of employees.
• specialties: List of the company’s specializations or services.
• updates: Recent updates or news related to the company.
• crunchbase_url: Link to the company’s profile on Crunchbase.
• founded: Year when the company was founded.
• funding: Information on funding rounds or financial data.
• investors: Investors who have funded the company.
• alumni: Notable alumni from the company.
• alumni_information: Details about the alumni, their roles, or achievements.
• stock_info: Stock market information for publicly traded companies.
• affiliated: Companies or organizations affiliated with the company.
• image: Image representing the company.
• logo: URL of the official logo of the company.
• slogan: Company’s slogan or tagline.
• similar: URL of companies similar to this one.

Distribution

• Data Volume: 56.51M rows and 35 columns.
• Structure: Tabular format (CSV, Excel).

Usage

This dataset is ideal for:
- Market Research: Identifying key trends and patterns across different industries and geographies.
- Business Development: Analyzing potential partners, competitors, or customers.
- Investment Analysis: Assessing investment potential based on company size, funding, and industries.
- Recruitment & Talent Analytics: Understanding the workforce size and specialties of various companies.

Coverage

• Geographic Coverage: Global, with company locations and headquarters spanning multiple countries.
• Time Range: Data likely covers both current and historical information about companies.
• Demographics: Focuses on company attributes rather than demographics, but may contain information about the company's workforce.

License

CUSTOM

Please review the respective licenses below:

1. Data Provider's License
   • Bright Data Master Service Agreement

Who Can Use It

• Data Scientists: For building models, conducting research, or enhancing machine learning algorithms with business data.
• Researchers: For academic analysis in fields like economics, business, or technology.
• Businesses: For analysis, competitive benchmarking, and strategic development.
• Investors: For identifying and evaluating potential investment opportunities.

Dataset Name Ideas

• Global Company Profile Database
• **Business Intellige

.HR Whois Database, discover comprehensive ownership details, registration dates, and more for .HR TLD with Whois Data Center.

The Exit Interview Management Software market is experiencing robust growth, driven by the increasing need for organizations to gather actionable insights from departing employees. The market's expansion is fueled by several key factors. Firstly, the rising awareness of the importance of employee feedback in improving retention and organizational effectiveness is a significant driver. Secondly, the increasing adoption of cloud-based solutions simplifies data collection, analysis, and reporting, reducing the administrative burden on HR departments. Thirdly, the growing availability of advanced analytics features within these platforms allows for deeper understanding of attrition trends and their underlying causes, enabling proactive intervention strategies. The market segmentation, with a focus on large enterprises and SMEs utilizing both cloud and web-based solutions, reflects the diverse needs and technological capabilities of various organizations. While the market shows strong potential, certain restraints exist. These include the initial investment costs associated with implementing new software, resistance to change within organizations, and concerns around data security and privacy. However, the benefits of improved employee retention, reduced recruitment costs, and enhanced organizational learning are expected to outweigh these challenges. Given a projected CAGR (assume 15% based on industry average for similar SaaS solutions) and a 2025 market size (assume $500 million based on reasonable estimations for a niche SaaS market), we can anticipate substantial market expansion throughout the forecast period (2025-2033). Key players like Qualtrics, Retensa, and others are constantly innovating, adding features like advanced analytics and integration with HR systems, further propelling market growth. The regional distribution is expected to be heavily concentrated in North America and Europe initially, with Asia-Pacific showing strong growth potential in the later forecast years.

The Croatian web corpus MaCoCu-hr 1.0 was built by crawling the ".hr" internet top-level domain in 2021, extending the crawl dynamically to other domains as well (https://github.com/macocu/MaCoCu-crawler).

Considerable efforts were devoted into cleaning the extracted text to provide a high-quality web corpus. This was achieved by removing boilerplate (https://corpus.tools/wiki/Justext) and near-duplicated paragraphs (https://corpus.tools/wiki/Onion), discarding very short texts as well as texts that are not in the target language. The dataset is characterized by extensive metadata which allows filtering the dataset based on text quality and other criteria (https://github.com/bitextor/monotextor), making the corpus highly useful for corpus linguistics studies, as well as for training language models and other language technologies.

Each document is accompanied by the following metadata: title, crawl date, url, domain, file type of the original document, distribution of languages inside the document, and a fluency score (based on a language model). The text of each document is divided into paragraphs that are accompanied by metadata on the information whether a paragraph is a heading or not, metadata on the paragraph quality and fluency, the automatically identified language of the text in the paragraph, and information whether the paragraph contains personal information.

This action has received funding from the European Union's Connecting Europe Facility 2014-2020 - CEF Telecom, under Grant Agreement No. INEA/CEF/ICT/A2020/2278341. This communication reflects only the author’s view. The Agency is not responsible for any use that may be made of the information it contains.

The offboarding software market is experiencing robust growth, driven by the increasing need for streamlined employee departure processes and the desire to improve employee experience and data security during transitions. The market, estimated at $2 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $6 billion by 2033. This expansion is fueled by several key factors. Firstly, larger enterprises are increasingly adopting cloud-based and web-based solutions to manage the complexities of offboarding a large workforce efficiently and securely. Secondly, SMEs are also embracing these technologies to optimize their HR processes, realizing the cost and time savings associated with automated offboarding. Thirdly, the rising focus on data security and compliance regulations is driving demand for sophisticated offboarding solutions that ensure the protection of sensitive company information during employee departures. Finally, improvements in user experience and the integration of offboarding software with other HR systems further enhances market appeal. The market is segmented by deployment type (cloud-based and web-based) and user type (large enterprises and SMEs). While cloud-based solutions currently dominate, the web-based segment is witnessing significant growth due to its flexibility and cost-effectiveness. Large enterprises represent a significant portion of the market due to their higher adoption rates and budgetary capacity. However, the increasing awareness among SMEs about the benefits of offboarding software is fostering considerable growth in this segment. Geographic distribution shows North America and Europe currently hold the largest market share, driven by early adoption and established HR technology infrastructure. However, the Asia-Pacific region is poised for significant growth in the coming years due to increasing digitalization and a burgeoning workforce. Despite these positive trends, factors like high initial investment costs and the need for employee training can potentially restrain market expansion.

This data release links fish survey data from a suite of programs in the Chesapeake Bay watershed to the NHDPlus High Resolution Region 02 networks, hereafter referred to as NHDPlusHR. The data set contains site name, survey program, coordinates of sample, ancillary information such as sample date and site location information where available, and HR Permanent Identifier. It also includes a confidence classification category for each of NHD assignment based on a set of pre-determined rules. In total there were 15 confidence categories ranging from high confidence to low confidence. We caution the use of sampling points which were given anything other than "high" confidence in their assignment to a given NHD catchment/flowline to avoid spurious/inappropriate attribution of geospatial data to fish data samples represented herein and refer the potential user to the Confidence Dictionary.csv which describes the criteria for each confidence category.

The Croatian web corpus MaCoCu-hr 2.0 was built by crawling the ".hr" internet top-level domain in 2021 and 2022, extending the crawl dynamically to other domains as well. The crawler is available at https://github.com/macocu/MaCoCu-crawler.

Considerable effort was devoted into cleaning the extracted text to provide a high-quality web corpus. This was achieved by removing boilerplate (https://corpus.tools/wiki/Justext) and near-duplicated paragraphs (https://corpus.tools/wiki/Onion), discarding very short texts as well as texts that are not in the target language. Furthermore, samples from the largest 1,500 domains were manually checked and bad domains, such as machine-translated domains, were removed. The dataset is characterized by extensive metadata which allows filtering the dataset based on text quality and other criteria (https://github.com/bitextor/monotextor), making the corpus highly useful for corpus linguistics studies, as well as for training language models and other language technologies.

In XML format, each document is accompanied by the following metadata: title, crawl date, url, domain, file type of the original document, distribution of languages inside the document, and a fluency score based on a language model. The text of each document is divided into paragraphs that are accompanied by metadata on the information whether a paragraph is a heading or not, metadata on the paragraph quality (labels, such as “short” or “good”, assigned based on paragraph length, URL and stopword density via the jusText tool - https://corpus.tools/wiki/Justext) and fluency (score between 0 and 1, assigned with the Monocleaner tool - https://github.com/bitextor/monocleaner), the automatically identified language of the text in the paragraph, and information whether the paragraph contains sensitive information (identified via the Biroamer tool - https://github.com/bitextor/biroamer).

As opposed to the previous version, this version has more accurate metadata on languages of the texts, which was achieved by using Google's Compact Language Detector 2 (CLD2) (https://github.com/CLD2Owners/cld2), a high-performance language detector supporting many languages. Other tools, used for web corpora creation and curation, have been updated as well, resulting in an even cleaner, as well as larger corpus.

The corpus can be easily read with the prevert parser (https://pypi.org/project/prevert/).

Notice and take down: Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please: (1) Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted. (2) Clearly identify the copyrighted work claimed to be infringed. (3) Clearly identify the material that is claimed to be infringing and information reasonably sufficient in order to allow us to locate the material. (4) Please write to the contact person for this resource whose email is available in the full item record. We will comply with legitimate requests by removing the affected sources from the next release of the corpus.

This action has received funding from the European Union's Connecting Europe Facility 2014-2020 - CEF Telecom, under Grant Agreement No. INEA/CEF/ICT/A2020/2278341. This communication reflects only the author’s view. The Agency is not responsible for any use that may be made of the information it contains.

The Data Processing and Hosting Services industry has transformed over the past decade, with the growth of cloud computing creating new markets. Demand surged in line with heightened demand from banks and a rising number of mobile connections across Europe. Many companies regard cloud computing as an innovative way of reducing their operating costs, which has led to the introduction of new services that make the sharing of data more efficient. Over the five years through 2025, revenue is expected to hike at a compound annual rate of 4.3% to €113.5 billion, including a 5.6% jump in 2025. Industry profit has been constrained by pricing pressures between companies and regions. Investments in new-generation data centres, especially in digital hubs like Frankfurt, London, and Paris, have consistently outpaced available supply, underlining the continent’s insatiable appetite for processing power. Meanwhile, 5G network roll-outs and heightened consumer expectations for real-time digital services have made agile hosting and robust cloud infrastructure imperative, pushing providers to invest in both core and edge data solutions. Robust growth has been fuelled by rapid digitalisation, widespread cloud adoption, and exploding demand from sectors such as e-commerce and streaming. Scaling cloud infrastructure, driven by both established giants, like Amazon Web Services (AWS), Microsoft Azure and Google Cloud and nimble local entrants, has allowed the industry to keep pace with unpredictable spikes in online activity and increasingly complex data needs. Rising investment in data centre capacity and the proliferation of high-availability hosting have significantly boosted operational efficiency and market competitiveness, with revenue growth closely tracking the boom in cloud and streaming services across the continent. Industry revenue is set to grow moving forward as European businesses incorporate data technology into their operations. Revenue is projected to boom, growing at a compound annual rate of 10.3% over the five years through 2030, to reach €185.4 billion. Growth is likely to be assisted by ongoing cloud adoption, accelerated 5G expansion, and soaring investor interest in hyperscale and sovereign data centres. Technical diversification seen in hybrid cloud solutions, edge computing deployments, and sovereign clouds, will create significant opportunities for incumbents and disruptors alike. Pricing pressures, intensified by global hyperscalers’ economies of scale and assertive licensing strategies, will pressurise profit, especially for smaller participants confronting rising capital expenditure and compliance costs.

The global Education HR Software market, valued at $700.7 million in 2025, is poised for significant growth driven by the increasing need for efficient and streamlined HR processes within educational institutions. The rising adoption of cloud-based solutions, coupled with the growing demand for better teacher and staff management, are key factors fueling market expansion. K-12 schools represent a substantial segment, followed by higher education, reflecting the diverse applications of this software across the educational spectrum. The market's growth is further propelled by the need for improved data analysis capabilities to aid in strategic decision-making related to staffing, compensation, and talent acquisition. Increased government initiatives promoting digitalization in education are also contributing to the market's upward trajectory. While challenges such as data security concerns and the high initial investment costs for implementation can act as restraints, the long-term benefits of improved efficiency and reduced administrative burden are expected to outweigh these concerns. The competitive landscape is characterized by a mix of established players and emerging startups, each offering a range of features and functionalities catering to specific needs within the education sector. The market is expected to witness continuous innovation, with new features such as integrated payroll systems and advanced analytics becoming increasingly common. Looking ahead, the forecast period (2025-2033) anticipates sustained growth, driven by technological advancements, such as AI-powered recruitment tools and automated workflows, enhancing efficiency and reducing manual tasks. The increasing adoption of mobile-friendly solutions will further boost market penetration, enabling seamless access to HR data from various devices. Geographic expansion, particularly in developing economies with growing educational infrastructure, presents substantial growth opportunities. The integration of Education HR software with other educational platforms, such as learning management systems (LMS), will also contribute to its wider adoption. Strategic partnerships and mergers and acquisitions are likely to shape the competitive landscape, leading to increased consolidation and innovation within the market.

Multilingual (BG, CS, DA, DE, EL, EN, ET, ES, FI, FR, GA, HR, HU, IS, IT, LT, LV, MK, MT, NL, NB, NN, NO, PL, PT, RO, SK, SL, SQ, SV) corpus based on the content of health/covid-19-related websites. The total number of Tus is 3571981. bg-cs 1813 bg-da 1527 bg-de 1621 bg-el 1994 bg-es 1744 bg-et 1813 bg-fi 1290 bg-fr 1849 bg-ga 141 bg-hr 1480 bg-hu 2004 bg-is 232 bg-it 1927 bg-lt 1675 bg-lv 1410 bg-mt 532 bg-nb 253 bg-nl 1709 bg-pl 1944 bg-pt 1484 bg-ro 2073 bg-sk 2020 bg-sl 1588 bg-sv 1281 cs-da 1881 cs-de 2149 cs-el 1804 cs-es 2244 cs-et 2111 cs-fi 1734 cs-fr 1944 cs-ga 146 cs-hr 1824 cs-hu 2232 cs-is 242 cs-it 2414 cs-lt 1746 cs-lv 1802 cs-mt 580 cs-nb 364 cs-nl 2124 cs-pl 2204 cs-pt 1886 cs-ro 2376 cs-sk 2192 cs-sl 1917 cs-sv 1720 da-de 2035 da-el 1517 da-es 2221 da-et 2281 da-fi 1868 da-fr 1883 da-ga 131 da-hr 1541 da-hu 1957 da-is 461 da-it 2395 da-lt 1756 da-lv 1553 da-mt 633 da-nb 751 da-nl 2286 da-pl 1821 da-pt 2035 da-ro 1861 da-sk 1808 da-sl 1753 da-sv 2046 de-el 1971 de-es 22620 de-et 2238 de-fi 1861 de-fr 214584 de-ga 146 de-hr 1758 de-hu 2556 de-is 450 de-it 111602 de-lt 1896 de-lv 1645 de-mt 669 de-nb 489 de-nl 9163 de-pl 2448 de-pt 2678 de-ro 2358 de-sk 2485 de-sl 2220 de-sv 1868 el-es 1892 el-et 1918 el-fi 1320 el-fr 2072 el-ga 158 el-hr 1475 el-hu 1852 el-is 244 el-it 1997 el-lt 1633 el-lv 1558 el-mt 629 el-nb 243 el-nl 1765 el-pl 1838 el-pt 1495 el-ro 1962 el-sk 1873 el-sl 1928 el-sv 1316 en-bg 52594 en-cs 61063 en-da 52923 en-de 277414 en-el 82914 en-es 415299 en-et 69595 en-fi 51187 en-fr 495612 en-ga 3729 en-hr 54593 en-hu 61307 en-is 8826 en-it 206566 en-lt 56987 en-lv 78112 en-mk 733 en-mt 20524 en-nb 9613 en-nl 93236 en-nn 5840 en-no 4141 en-pl 96702 en-pt 70002 en-ro 63914 en-sk 58288 en-sl 80759 en-sq 1338 en-sv 69774 es-et 2631 es-fi 2082 es-fr 10020 es-ga 151 es-hr 1670 es-hu 2176 es-is 489 es-it 26549 es-lt 2094 es-lv 1688 es-mt 686 es-nb 753 es-nl 2637 es-pl 2587 es-pt 4836 es-ro 2610 es-sk 2180 es-sl 1882 es-sv 2212 et-fi 2142 et-fr 2360 et-ga 164 et-hr 1885 et-hu 2288 et-is 467 et-it 2781 et-lt 1981 et-lv 1759 et-mt 589 et-nb 700 et-nl 2575 et-pl 2097 et-pt 2246 et-ro 2242 et-sk 2154 et-sl 1966 et-sv 2073 fi-fr 1735 fi-ga 158 fi-hr 1309 fi-hu 2518 fi-is 472 fi-it 2250 fi-lt 1609 fi-lv 1595 fi-mt 543 fi-nb 735 fi-nl 2093 fi-pl 2451 fi-pt 1894 fi-ro 1685 fi-sk 1628 fi-sl 1523 fi-sv 1768 fr-ga 187 fr-hr 1491 fr-hu 2044 fr-is 497 fr-it 122075 fr-lt 1612 fr-lv 1380 fr-mt 548 fr-nb 613 fr-nl 10043 fr-pl 2157 fr-pt 3702 fr-ro 2234 fr-sk 1914 fr-sl 1651 fr-sv 1778 ga-hr 126 ga-hu 133 ga-is 62 ga-it 188 ga-lt 107 ga-lv 134 ga-mt 152 ga-nb 31 ga-nl 133 ga-pl 146 ga-pt 143 ga-ro 155 ga-sk 128 ga-sl 109 ga-sv 95 hr-hu 1992 hr-is 27 hr-it 1881 hr-lt 1605 hr-lv 1449 hr-mt 607 hr-nb 75 hr-nl 1686 hr-pl 1748 hr-pt 1481 hr-ro 1783 hr-sk 1789 hr-sl 1585 hu-is 250 hu-it 3608 hu-lt 2108 hu-lv 1847 hu-mt 657 hu-nb 369 hu-nl 2106 hu-pl 3104 hu-pt 1902 hu-ro 2472 hu-sk 3403 hu-sl 3670 hu-sv 1678 is-it 496 is-lt 241 is-lv 246 is-mt 83 is-nb 333 is-nl 466 is-pl 244 is-pt 470 is-ro 246 is-sk 244 is-sl 240 is-sv 470 it-lt 2124 it-lv 1829 it-mt 814 it-nb 759 it-nl 2685 it-pl 2741 it-pt 2584 it-ro 2857 it-sk 2353 it-sl 3678 it-sv 2207 lt-lv 1701 lt-mt 539 lt-nb 365 lt-nl 1854 lt-pl 2078 lt-pt 1845 lt-ro 2174 lt-sk 1971 lt-sl 1821 lt-sv 1687 lv-mt 643 lv-nb 339 lv-nl 1679 lv-pl 1777 lv-pt 1511 lv-ro 1814 lv-sk 1770 lv-sl 1702 lv-sv 1364 mt-nb 162 mt-nl 638 mt-pl 806 mt-pt 730 mt-ro 814 mt-sk 745 mt-sl 741 mt-sv 562 nb-nl 730 nb-pl 482 nb-pt 853 nb-ro 487 nb-sk 451 nb-sl 485 nb-sv 873 nl-pl 2005 nl-pt 2262 nl-ro 2110 nl-sk 2016 nl-sl 1868 nl-sv 2060 pl-pt 2085 pl-ro 2872 pl-sk 2386 pl-sl 2100 pl-sv 1845 pt-ro 2432 pt-sk 2224 pt-sl 1795 pt-sv 2144 ro-sk 2590 ro-sl 2171 ro-sv 1788 sk-sl 2085 sk-sv 1737 sl-sv 1694

Contents of http://www.hcr.hr website downloaded, aligned on document and segment level and converted into parallel corpus

This dataset has been created within the framework of the European Language Resource Coordination (ELRC) Connecting Europe Facility - Automated Translation (CEF.AT) actions SMART 2014/1074 and SMART 2015/1091. For further information on the project: http://lr-coordination.eu.

Website of the U.S. Office of Personnel Management (OPM). OPM works to recruit, retain and honor a world-class Federal workforce