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Тематски Зборник Свакодневна култура у постсоцијалистичком периоду настао је као резултат научне сарадње Етнографског института САНУи Етнографског института и музеја БАН. Велике политичке и друштвене промене током деведесетих година 20.века озбиљно су се одразиле на свакодневну културу балканских земаља. Управо у периоду кризе, две суседне етнолошке установе, бугарска и српска, које више деценија током социјалисатичког периода нису сарађивале, отпочеле су заједнички рад на истраживању свакодневне културе. Зборник показује у ком правацу су се одвијали културни процеси у Србији и Бугарској, које су сличности и разлике међу њима, али и шта се дешава у постсоцијалистичком периоду у појединим сегментима свакодневне култура Словака, Руса и Македонаца. ; The Collection of Papers entitled ―Everyday Culture in post-socialist period is a result of collaboration between the Institute of Ethnography, Serbian Academy of Sciences and Arts, and the Institute of Ethnography and Museum, Bulgarian Academy of Sciences. The enormous political and social changes during the 1990's influenced also everyday culture of the Balkans states. After decades of hinder, and precisely in the period of crisis, two neighboring ethnological institutions, the Bulgarian and Serbian, started to cooperate together in the study of everyday culture.The Collection of Papers demonstrates the directions of the cultural processes in Serbia and Bulgaria, accentuating the differences and similarities among the two states, and also explains the deeds within certain segments of everyday cultures of Slovaks, Russians and Macedonians. ; Зборник радова Етнографског института САНУ 22 / Collection of Papers of the Institute of Ethnography SASA 22

The Macedonian-English parallel corpus MaCoCu-mk-en 1.0 was built by crawling the ".mk" and ".мкд" internet top-level domains in 2021, extending the crawl dynamically to other domains as well. All the crawling process was carried out by the MaCoCu crawler (https://github.com/macocu/MaCoCu-crawler). Websites containing documents in both target languages were identified and processed using the tool Bitextor (https://github.com/bitextor/bitextor). Considerable efforts were devoted into cleaning the extracted text to provide a high-quality parallel corpus. This was achieved by removing boilerplate and near-duplicated paragraphs and documents that are not in one of the targeted languages. Document and segment alignment as implemented in Bitextor were carried out, and BicleanerAI (https://github.com/bitextor/bicleaner-ai) and Bifixer (https://github.com/bitextor/bifixer) were used for fixing, cleaning, and deduplicating the final version of the corpus. While the TXT format consists solely of pairs of source and target segments (consisting of one or several sentences), each segment pair in the TMX format is accompanied by the following metadata: - source and target document URL; - quality score as provided by the tool BicleanerAI; - translation direction identification: the source segment in each segment pair was identified by using a probabilistic model; - personal information identification ("biroamer-entities"): segments containing personal information are flagged, so final users of the corpus can decide whether to use these segments; - language variants: the language variant of English (British or American) was identified for every segment pair on document and domain level. 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 collection consists of: Thirty million words of monolingual written data (Gujarati, Tamil, Hindi, Punjabi-news website articles); 600,000 words of monolingual spoken data (Hindi, Urdu, Punjabi, Bengali, Gujarati-radio broadcasts); 120,000 words of parallel data in each of English, Hindi, Urdu, Punjabi, Bengali and Gujarati (U.K. government leaflets).