The 2000 Families: Migration Histories of Turks in Europe project explores migration processes, the multi-generational transmission of social, cultural, religious and economic resources, values and behavior. The research is targeted Turkish migrant and non-migrant families, their members in European countries and those who did not migrate to European countries or returned to Turkey, and involves survey interviews with approximately 6000 family members across three generations.

The study consists of three parts: Family Tree (Pilot and Main), Proxy interviews (Pilot and Main) and Personal interviews (Pilot and Main).

I. Information on first generation man (IKE): male ancestor is migrant or non-migrant; still alive; place of birth; year of birth (age); ethnic family origin; left his place of birth for more than five years; migration within Turkey; country of first destination; place of first destination (NUTS); year or age of internal migration; year or age of international emigration; ever moved to Europe for more than five years and country; year or age of moving; country of current (last) residence; duration of stay in Europe; number of siblings; place in the rank; age; sex of siblings; sibling moved to Europe between 1960-1974; emigration motive(s); spouse is alive; emigration(s) of spouse; year of emigration(s) of spouse; current (last) marriage was his first marriage; end of the first marriage; arranged marriage; year of marriage; ethnic family origin of spouse; spouse is (was) a relative; religion of spouse (or partner); highest level of education; first main job (ISCO-88 and ISEI); job title of current or last job (ISCO-88 and ISEI); kind of job; occupation of the father of IKE (ISCO-88 and ISEI); religion (denomination); left the country before he died; age or year of death; country of death; legal marital status at time of death; information on IKE´s children, grandchildren and great grandchildren.

Additionally coded was: children code; grandchildren code; rank number of children, grandchildren and great grandchildren; generation.

II. 1. Information about respondent and migration history: migration status; year of first migration; age of first migration; country of current stay (NUTS); name of the city, town or village; degree of urbanization; city is usual place of living; name of the nearest city; usual place of living, degree of urbanization, nearest city, and country of usual place of living; place of birth, and degree of urbanization; nearest city to place of birth; country of place of birth; respondent left his country for at least one year and number of countries; destination countries; age of migration; main reason for moving; regularly movement between two countries; names of these two countries;

1. Achieved education and occupation: completed education or still in education; literacy; age when finished education; country in which the respondent finished his education; highest level of education; information on first occupation and current (or last) occupation (ISCO-88 und ISEI); country of first job; occupational status; number of supervised employees; ethnic or national origin of the person who directly manages (managed) the respondent in this current or last job; number of Turkish colleagues; working hours; usual take home pay; currency; covered period of payment.

2. Family: marriage and fertility: legal marital status; stable relationship; living together with a partner; number of marriages; age when first married; end of the first marriage due to death of a partner or divorce; divorced; age when first marriage ended; age or year of first divorce; age when married current or most recent spouse; number of children; sex and age of these children.

3. Family relations: year of birth of mother and father; parents are alive; living together with parents; country of current stay; frequency of contact with parents; distance to the living place of parents; frequency of provided advice and financial support for own parents in the last 12 months; frequency of received support and financial support; attitude towards intergenerational relations and gender roles; responsible person for family finances.

4. Attachment to Turkey and to the country and identity: Turkish citizenship; feeling connected to people from Turkey; portion of friends with Turkish background; citizenship of the country of residence; feeling connected with country nationals; preferred country to win the Eurovision Song Contest;...

European Union Imports from Turkey was US$106.42 Billion during 2024, according to the United Nations COMTRADE database on international trade. European Union Imports from Turkey - data, historical chart and statistics - was last updated on July of 2025.

Standard Eurobarometer 90 survey (EB90), was carried out between 8 and 22 November 2018 in 34 countries or territories: the 28 European Union (EU) Member States, five candidate countries (the Former Yugoslav Republic of Macedonia, Turkey, Montenegro, Serbia and Albania) and the Turkish Cypriot Community in the part of the country that is not controlled by the government of the Republic of Cyprus. The survey includes topics such as the European political situation and the economy (perception of the current situation and expectations for the future). It analyses how Europeans perceive their political institutions, both national governments and parliaments, the EU and its institutions as well as their main concerns. It also examines people's attitudes on European citizenship and on issues linked to the priorities of the European Commission, notably free movement and the euro.

The results by volumes are distributed as follows:

• Volume A: Countries
• Volume AA: Groups of countries
• Volume A' (AP): Trends
• Volume AA' (AAP): Trends of groups of countries
• Volume B: EU/socio-demographics
• Volume B' (BP) : Trends of EU/ socio-demographics
• Volume C: Country/socio-demographics ---- Researchers may also contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

The Turkish web corpus MaCoCu-tr 1.0 was built by crawling the ".tr" 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.

Turkish Airlines: AS: International Flights: Europe data was reported at 45,369,748.000 Unit/km th in 2017. This records a decrease from the previous number of 47,590,992.000 Unit/km th for 2016. Turkish Airlines: AS: International Flights: Europe data is updated yearly, averaging 36,721,737.000 Unit/km th from Dec 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 47,590,992.000 Unit/km th in 2016 and a record low of 20,646,524.000 Unit/km th in 2009. Turkish Airlines: AS: International Flights: Europe data remains active status in CEIC and is reported by Turkish Airlines, Incorporation. The data is categorized under Global Database’s Turkey – Table TR.TA011: Airlines Statistics: Turkish Airlines .

Standard Eurobarometer 91 survey (EB91), was carried out in 34 countries or territories: the 28 European Union (EU) Member States, five candidate countries (the Former Yugoslav Republic of Macedonia, Turkey, Montenegro, Serbia and Albania) and the Turkish Cypriot Community in the part of the country that is not controlled by the government of the Republic of Cyprus. The fieldwork took place between the 7th of June and the 25th of June 2019 in the EU28 Member States and between the 7th of June and the 1st of July 2019 in the other countries and territories. The survey includes topics such as the European political situation and the economy (perception of the current situation and expectations for the future). It analyses how Europeans perceive their political institutions, both national governments and parliaments, the EU and its institutions as well as their main concerns. It also examines people's attitudes on European citizenship and on issues linked to the priorities of the European Commission, notably free movement and the euro.

The results by volumes are distributed as follows:

• Volume A: Countries
• Volume AA: Groups of countries
• Volume A' (AP): Trends
• Volume AA' (AAP): Trends of groups of countries
• Volume B: EU/socio-demographics
• Volume B' (BP) : Trends of EU/ socio-demographics
• Volume C: Country/socio-demographics ---- Researchers may also contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

Standard Eurobarometer 85 survey (EB85), was carried out between 21 and 31 May 2016 in 34 countries or territories: the 28 European Union (EU) Member States, five candidate countries (the Former Yugoslav Republic of Macedonia, Turkey, Montenegro, Serbia and Albania) and the Turkish Cypriot Community in the part of the country that is not controlled by the government of the Republic of Cyprus. The survey includes topics such as the European political situation, Europeans' main concerns and the perception of the economic situation. It analyses how Europeans perceive their political institutions, both national governments and parliaments, the EU and its institutions. It also examines people's attitudes on European citizenship and on issues linked to the priorities of the European Commission, notably support to investment, the euro, free movement of people, trade and migration issues.
The results by volumes are distributed as follows:

• Volume A: Countries
• Volume AA: Groups of countries
• Volume A' (AP): Trends
• Volume AA' (AAP): Trends of groups of countries
• Volume B: EU/socio-demographics
• Volume B' (BP) : Trends of EU/ socio-demographics
• Volume C: Country/socio-demographics

Researchers may also contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

Turkish makam acapella sections dataset is sung by professional singers and is a collection of recordings of compositions from the vocal form şarkı. They are selected to be the same as the recordings in version two of http://compmusic.upf.edu/turkish-sarki The main intention is to provide acapella counterpart to polyphonic recordings. THE DATASET Audio music content The collection has annotations with section, lyrics phrases and lyrics words. Each section, lyrics word and lyrical phrase is aligned to its corresponding segment in the audio. Annotations of secitons (aranağme, zemin etc.) are taken from https://github.com/MTG/turkish_makam_section_dataset FORMAT: All annotations in TextGrid (used in Praat) turkish-makam-acapella-sections-dataset-2.0.zip is organised by artist and makam_acapella-master_1.0.zip is organised by musicbrainz ID. Using this dataset Please cite one of the following publications if you use the dataset in your work: Dzhambazov, G., & Serra X. (2015). Modeling of Phoneme Durations for Alignment between Polyphonic Audio and Lyrics. Sound and Music Computing Conference 2015. Or Dzhambazov, G., Şentürk S., & Serra X. (2015). Searching Lyrical Phrases in A-Capella Turkish Makam Recordings. 16th International Society for Music Information Retrieval (ISMIR) Conference We are interested in knowing if you find our datasets useful! If you use our dataset please email us at mtg-info@upf.edu and tell us about your research. Contact If you have any questions or comments about the dataset, please feel free to write to us. Georgi Dzhambazov Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain georgi

Standard Eurobarometer 88 survey (EB87), was carried out between 5 and 19 November 2017 in 34 countries or territories: the 28 European Union (EU) Member States, five candidate countries (the Former Yugoslav Republic of Macedonia, Turkey, Montenegro, Serbia and Albania) and the Turkish Cypriot Community in the part of the country that is not controlled by the government of the Republic of Cyprus. The survey includes topics such as the European political situation and the economy (perception of the current situation and expectations for the future). It analyses how Europeans perceive their political institutions, both national governments and parliaments, the EU and its institutions as well as their main concerns. It also examines people's attitudes on European citizenship and on issues linked to the priorities of the European Commission, notably free movement, the euro and migration.

The results by volumes are distributed as follows:

• Volume A: Countries
• Volume AA: Groups of countries
• Volume A' (AP): Trends
• Volume AA' (AAP): Trends of groups of countries
• Volume B: EU/socio-demographics
• Volume B' (BP) : Trends of EU/ socio-demographics
• Volume C: Country/socio-demographics ---- Researchers may also contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

This dataset provides values for INFLATION RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This repository contains additional analyses and the full dataset of the RE-DEFINE Turkish randomised controlled. The study aimed to evaluate the effectiveness and cost-effectiveness of Self-Help Plus (SH+), a group self-help intervention developed by the WHO to reduce distress. In this trial, SH+ was tested as a preventative intervention to lower the incidence of mental disorders in asylum seekers and refugees with psychological distress resettled in Turkey. This prospective, multicentre, randomised, rater-blinded, parallel-group study followed participants over a period of 12 months. Six hundred asylum seekers and refugees screening positive on the General Health Questionnaire (≥3), but without a formal diagnosis of any mental disorders according to the Mini International Neuropsychiatric Interview, were randomly allocated to SH+ or to enhanced treatment-as-usual. The primary outcome was a lower incidence of mental disorders at 6 month follow-up. Secondary outcomes included the evaluation of psychological symptoms, functioning, well-being, treatment acceptability and indicators of intervention cost-effectiveness. The trial received ethical clearance from the local Ethics Committees of the participating sites, as well as from the WHO Ethics Committee. All participants provided informed consent before screening and before study inclusion (a two-step procedure). The dataset is provided in form of an Excel spreadsheet containing all relevant variables to analyze the pre-specified outcomes of the European RE-DEFINE randomized controlled trial. A legend guiding the interpretation of labels for each variable is embedded in the Excel file. An XML file reporting metadata is also provided;

OTMM Makam Recognition Dataset This repository hosts the dataset designed to test makam recognition methodologies on Ottoman-Turkish makam music. It is composed of 50 recordings from each of the 20 most common makams in CompMusic Project's Dunya Ottoman-Turkish Makam Music collection. Currently, the dataset is the largest makam recognition dataset. Please cite the publication below if you use this dataset in your work: Karakurt, A., Şentürk S., & Serra X. (2016). MORTY: A Toolbox for Mode Recognition and Tonic Identification. 3rd International Digital Libraries for Musicology Workshop. New York, USA The recordings are selected from commercial recordings carefully such that they cover diverse musical forms, vocal/instrumentation settings, and recording qualities (e.g. historical vs contemporary). Each recording in the dataset is identified by a 16-character long unique identifier called MBID, hosted in MusicBrainz. The makam and the tonic of each recording are annotated in the file annotations.json. The audio-related data in the test dataset is organized by each makam in the folder data. Due to copyright reasons, we are unable to distribute the audio. Instead, we provide the predominant melody of each recording, computed by a state-of-the-art predominant melody extraction algorithm optimized for OTMM culture. These features are saved as text files (with the paths data/[makam]/[mbid].pitch) of a single column that contains the frequency values. The timestamps are removed to reduce the filesizes. The step size of the pitch track is 0.0029 seconds (an analysis window of 128 samples hop size of an mp3 with 44100 Hz sample rate), with which one can recompute the timestamps of samples. Moreover, the metadata of each recording is available in the repository, crawled from MusicBrainz using an open source tool developed by us. The metadata files are saved as data/[makam]/[mbid].json. For reproducibility purposes, we note the version of all tools we have used to generate this dataset in the file algorithms.json. A complementary toolbox for this dataset is MORTY, which is a mode recognition and tonic identification toolbox. It can be used and optimized for any modal music culture. Further details are explained in the publication above. For more information, please contact the authors. Errata April 2020: We replaced 2 recordings, which do not exist in CompMusic Dunya makam corpus, with their instrumental versions. We also patched the dunya_uid of a recording, which is a redirection to the MusicBrainz ID. None of the annotations has changed. (PR #1) November 2016: We discovered several discrepancies in the tonic annotations while merging human and machine annotations to create the otmm_tonic_dataset. Please refer to the repo for further explanation. We advise to use the tonic annotations in otmm_tonic_dataset. License This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

CorefUD is a collection of previously existing datasets annotated with coreference, which we converted into a common annotation scheme. In total, CorefUD in its current version 1.1 consists of 21 datasets for 13 languages. The datasets are enriched with automatic morphological and syntactic annotations that are fully compliant with the standards of the Universal Dependencies project. All the datasets are stored in the CoNLL-U format, with coreference- and bridging-specific information captured by attribute-value pairs located in the MISC column. The collection is divided into a public edition and a non-public (ÚFAL-internal) edition. The publicly available edition is distributed via LINDAT-CLARIAH-CZ and contains 17 datasets for 12 languages (1 dataset for Catalan, 2 for Czech, 2 for English, 1 for French, 2 for German, 2 for Hungarian, 1 for Lithuanian, 2 for Norwegian, 1 for Polish, 1 for Russian, 1 for Spanish, and 1 for Turkish), excluding the test data. The non-public edition is available internally to ÚFAL members and contains additional 4 datasets for 2 languages (1 dataset for Dutch, and 3 for English), which we are not allowed to distribute due to their original license limitations. It also contains the test data portions for all datasets. When using any of the harmonized datasets, please get acquainted with its license (placed in the same directory as the data) and cite the original data resource too. Compared to the previous version 1.0, the version 1.1 comprises new languages and corpora, namely Hungarian-KorKor, Norwegian-BokmaalNARC, Norwegian-NynorskNARC, and Turkish-ITCC. In addition, the English GUM dataset has been updated to a newer and larger version, and the conversion pipelines for most datasets have been refined (a list of all changes in each dataset can be found in the corresponding README file).

This data set contains the TL equivalent of USD and EU currencies on a monthly basis for the years 2018-2022. The website https://evds2.tcmb.gov.tr/ has been taken as a reference.

They are 5 columns: Date, USD ,EU , Year , Month .

Date Indicates the date information to which the data belongs. It consists of Year-Month information. USD : How much is $1 in 1TL at Date? EU : How much is 1 EU in TL at Date?

The Turkish web corpus MaCoCu-tr 2.0 was built by crawling the ".tr" and ".cy" internet top-level domains in 2021, 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. 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 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.

This Eurobarometer survey includes questions on behaviours and attitudes in relation to organ donation and transplantation. It is based on a Eurobarometer survey of 26,788 European citizens carried out in October 2009 in the 27 European Union Member States, as well as 3,504 interviews in the candidate countries (Croatia, Turkey and the Former Yugoslav Republic of Macedonia) and the Turkish Cypriot Community.

The results by volumes are distributed as follows:

• Volume A: Countries
• Volume AA: Groups of countries
• Volume A' (AP): Trends
• Volume AA' (AAP): Trends of groups of countries
• Volume B: EU/socio-demographics
• Volume B' (BP) : Trends of EU/ socio-demographics
• Volume C: Country/socio-demographics ---- Researchers may also contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

Description Utrecht in Figures is the online database of the municipality of Utrecht. Here the Research & Advice department publishes figures about the city of Utrecht and the Utrecht districts and neighbourhoods. With Utrecht in Figures we want to show everyone interested in Utrecht how things are actually going in Utrecht. Utrecht in Figures is divided into themes. Within the theme 'Population development (Population)' you will find information about the following 27 'indicators': - population development (births, deaths, relocations) - population development per 1000 inhabitants - births per 1000 women (15 to 44 years) - average length of stay by address in years - marriages and divorces - migration 0 to 3 years old - migration 0 to 17 years old - migration 4 to 11 years old - migration 12 to 17 years old - migration 18 to 24 years old - migration 25 to 34 years - migration 35 to 54 years inclusive - migration 55 years and older - migration men - migration total - migration women - migration; Moroccan background - migration; Dutch background - migration; non-Dutch background - migration; other non-western background - migration; Sur./Antilles/Arub. background - migration; Turkish background - migration; western background (not Dutch) - length of stay at address by age - length of stay at address by migration background and age - settling in and leaving Utrecht by age - settling in and leaving Utrecht by migration background #### Number of indicators Number of indicators under this theme: 27 #### Source BRP, municipality of Utrecht #### Update frequency Annual #### Related datasets Other datasets that fall within this theme: - Indicators Population - Indicators Population-forecast (Population) - Indicators Households (Population) - Indicators Refugees and asylum seekers (Population)