The Slovenian territory played a crucial role in the past serving as gateway for several human migrations. Previous studies used Slovenians as a source population to interpret different demographic events happened in Europe but not much is known about the genetic background and the demographic history of this population. Here, we analyzed genome-wide data from 96 individuals to shed light on the genetic role and history of the Slovenian population. Y chromosome diversity splits into two major haplogroups R1b and R1a with the latter suggesting a genetic contribution from the steppe. Slovenian individuals are more closely related to Northern and Eastern European populations than Southern European populations even though they are geographically closer. This pattern is confirmed by an admixture and clustering analysis. We also identified a single stream of admixture events between the Slovenians with Sardinians and Russians around ∼2630 BCE (2149-3112). Using ancient samples, we found a significant admixture in Slovenians using Yamnaya and the early Neolithic Hungarians as sources, dated around ∼1762 BCE (1099-2426) suggesting a strong contribution from the steppe to the foundation of the observed modern genetic diversity. Finally, we looked for signals of selection in candidate variants and we found significant hits in HERC2 and FADS responsible for blue eye color and synthesis of long-chain unsaturated fatty acids, respectively, when Slovenians were compared to Southern Europeans. While the comparison was done with Eastern Europeans, we identified significant signals in PKD2L1 and IL6R which are genes associated with taste and coronary artery disease, respectively.

Slovenia Biodiversity: Tax Revenue: % of Total Tax Revenue data was reported at 0.149 % in 2022. This records a decrease from the previous number of 0.159 % for 2021. Slovenia Biodiversity: Tax Revenue: % of Total Tax Revenue data is updated yearly, averaging 0.165 % from Dec 1995 (Median) to 2022, with 28 observations. The data reached an all-time high of 0.247 % in 2016 and a record low of 0.015 % in 1995. Slovenia Biodiversity: Tax Revenue: % of Total Tax Revenue data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Slovenia – Table SI.OECD.ESG: Environmental: Environmentally Related Tax Revenue: Cross Cutting Domains: OECD Member: Annual.

Slovenia has particular climatic, soil, geographic and historical conditions that lead to long tradition of plum cultivation and use. In this work, a set of 11 SSR and three universal cpDNA markers, as well as flow cytometry, were used to (1) evaluate the genetic diversity of 124 accessions of the three Prunus species (P. domestica L., P. cerasifera Ehrh., and P. spinosa L.), (2) investigate the possible involvement of P. cerasifera and P. spinosa species in P. domestica origin, (3) study the genetic relationships and variability among the most typical P. domestica accessions present in Slovenia. Ten haplotypes of cpDNA were identified and clustered into three groups according to the Neighbor-Joining analysis (NJ). All 11 SSR primer pairs were polymorphic, revealing 116 unique genotypes. A total of 328 alleles were detected with an average value of 29.82 alleles per locus, showing relatively high diversity. Bayesian analysis of genetic structure was used to identify two ancestral populations in the analyses of all three species as well as in a separate set consisting of P. domestica material only. Principal Coordinate Analysis (PCoA) showed that accessions clustered largely in agreement with Bayesian analysis. Neighbor-Joining analysis grouped 71 P. domestica accessions into three clusters with many subgroups that exhibited complex arrangement. Most accessions clustered in agreement with traditional pomological groups, such as common prunes, mirabelle plums and greengages. In this study, the analyses revealed within P. domestica pool valuable local landraces, such as traditional prunes or bluish plums, which seem to be highly interesting from a genetic point of view. Moreover, complementary approaches allowed us to distinguish between the three species and to gain insights into the origin of plum. The results will be instrumental in understanding the diversity of Slovenian plum germplasm, improving the conservation process, recovering local genotypes and enriching existing collections of plant genetic resources.

The Global Register of Introduced and Invasive Species (GRIIS) presents validated and verified national checklists of introduced (alien) and invasive alien species at the country, territory, and associated island level.

Checklists are living entities, especially for biological invasions given the growing nature of the problem. GRIIS checklists are based on a published methodology and supported by the Integrated Publishing Tool that jointly enable ongoing improvements and updates to expand their taxonomic coverage and completeness.

Phase 1 of the project focused on developing validated and verified checklists of countries that are Party to the Convention on Biological Diversity (CBD). Phase 2 aimed to achieve global coverage including non-party countries and all overseas territories of countries, e.g. those of the Netherlands, France, and the United Kingdom.

All kingdoms of organisms occurring in all environments and systems are covered.

Checklists are reviewed and verified by networks of country or species experts. Verified checklists/ species records, as well as those under review, are presented on the online GRIIS website (www.griis.org) in addition to being published through the GBIF Integrated Publishing Tool.

This digital archive contains interview data with the Hungarian minority speakers in Slovenia. The archive consists of audio files (available in .wav format) containing individual and focus group interviews with the speakers of four different age groups. All interview files were named using a special coding system. Each file name includes: a) a country where the research was conducted; b) the speech community studied; c) the form of an interview; d) the type of the target group; e) the age group and gender; f) the date of the interview (DDMMYEAR). For the full list of files, as well as code values please refer to the descriptions under “ELDIAdata: Metadata”.

The latter is especially well-studied and and therefore given for comparison. For each country, diversity at the family, genus and species level is given. We first give the number of taxa at each level from Fauna Europaea [77] and after the diagonal slash from other overview publications screened (when available, a list of these publications is given in the Method section). In case of missing or incomplete data at the country level (e.g., no publication considering all species within the order Amphipoda), a dash "–" is given.Overview of the hitherto published diversity of Amphipoda in Switzerland, neighboring countries of Switzerland (Austria, Germany, Italy, France) as well as Slovenia.

Given the limited success of past and current conservation efforts, an alternative approach is to preserve tissues and genomes of targeted organisms in cryobanks to make them accessible for future generations. Our pilot preservation project aimed to obtain, expertly identify, and permanently preserve a quarter of the known spider species diversity shared between Slovenia and Switzerland, estimated at 275 species. We here report on the faunistic part of this project, which resulted in 324 species (227 in Slovenia, 143 in Switzerland) for which identification was reasonably established. This material is now preserved in cryobanks, is being processed for DNA barcoding, and is available for genomic studies.

Slovenia Biodiversity: Tax Revenue: USD: Transport data was reported at 0.503 USD mn in 2022. This records a decrease from the previous number of 0.701 USD mn for 2021. Slovenia Biodiversity: Tax Revenue: USD: Transport data is updated yearly, averaging 0.327 USD mn from Dec 1994 (Median) to 2022, with 29 observations. The data reached an all-time high of 10.315 USD mn in 2018 and a record low of 0.000 USD mn in 1994. Slovenia Biodiversity: Tax Revenue: USD: Transport data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Slovenia – Table SI.OECD.ESG: Environmental: Environmentally Related Tax Revenue: Cross Cutting Domains: OECD Member: Annual.

In Slovenia, the distribution of stores across different platforms presents a dynamic picture of the market. WooCommerce, as a leading platform, hosts 9.51K stores, accounting for 57.16% of the total store count in the region. This is closely followed by Shopify, which supports 2.55K stores, representing 15.33% of the region's total. Custom Cart makes a significant contribution with 1.9K stores, or 11.43% of the total. The chart underscores the diversity and preferences of store owners in Slovenia regarding their choice of platform.

Information system for collecting of data on invasive alien species (IAS) in Slovenia. The system includes web application and mobile application Invazivke. The system connects several other systems that gathers data on IAS.

Abstract

In December 2023, UNHCR partnered with the Slovenian Migration Institute of the Science and Research Centre of the Slovenian Academy of Sciences and Arts (SMI ZRC SAZU) to conduct a Multi-Sector Needs Assessment (MSNA). The assessment aimed to gain an up-to-date and in-depth understanding of the vulnerabilities, needs, and access to services for refugees from Ukraine residing in Slovenia. The dataset provides insights for government stakeholders, humanitarian organizations, and donors to ensure evidence-based programming in support of the socio-economic inclusion of refugees in Slovenia.

Geographic coverage

Slovenia

Analysis unit

Household and Individual

Universe

Refugees from Ukraine and from other countries residing in Slovenia at the time of data collection.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey employed a non-probability sampling approach using availability and purposive sampling methods to reach refugees residing in Slovenia. Households were selected based on their availability during the survey period, aiming to reflect diverse profiles across multiple locations.

Mode of data collection

Face-to-face [f2f]

Research instrument

Structured household questionnaire covering displacement history, legal status, household demographics, health, education, employment, housing, access to services, and protection concerns.

na = alleles number; Hap/Ind = haplotype/accessions ratio; HE = genetic diversity; Inor = normalised Shannon-Weaver index. Iberian Peninsula and Italy; Italy, Portugal, Spain; Central-northern Europe: Austria, Germany, The Netherlands; Eastern Europe: Georgia, Poland, Slovakia, Ukraine; South-eastern Europe: Albania, Bulgaria, Croatia, Hungary, Moldavia, Romania, Slovenia, Hungary.

Slovenian Restaurant Market Outlook

According to our latest research, the Slovenian restaurant market size reached USD 1.14 billion in 2024, reflecting robust consumer demand and a vibrant culinary scene. The market is projected to expand at a CAGR of 5.2% from 2025 to 2033, reaching an estimated USD 1.77 billion by the end of the forecast period. This growth is driven by evolving consumer preferences, increasing tourism, and a dynamic shift towards experiential dining, marking Slovenia as a rising star in the European foodservice landscape.

A primary growth factor for the Slovenian restaurant market is the country’s burgeoning tourism sector, which has been experiencing record arrivals and overnight stays in recent years. Slovenia’s unique geographical position, nestled between Italy, Austria, Hungary, and Croatia, makes it a melting pot for culinary influences and a favored destination for food lovers. The government’s strategic promotion of Slovenia as a gastronomic destination, highlighted by its recognition as the European Region of Gastronomy, has further spurred growth. High-profile events, such as the Michelin Guide’s expansion into Slovenia, have elevated the nation’s restaurant profile on the global stage, attracting discerning diners and culinary enthusiasts from across the world. This influx of international visitors, combined with a growing local appreciation for diverse dining experiences, has stimulated demand across all restaurant segments.

Another significant driver is the evolving lifestyle and consumption patterns of the Slovenian population, particularly among younger demographics and urban dwellers. There is a marked shift towards dining out, fueled by rising disposable incomes, an expanding middle class, and a growing preference for convenience and quality. The proliferation of social media and food-related digital content has amplified the appeal of restaurants, cafés, and bistros, encouraging consumers to seek out novel and authentic culinary experiences. Furthermore, the increasing adoption of technology in the restaurant sector, such as online reservations, digital menus, and contactless payments, has enhanced customer convenience and operational efficiency, fostering further market expansion.

The Slovenian restaurant market is also benefiting from a strong emphasis on sustainability and local sourcing. Restaurateurs are increasingly prioritizing organic, locally sourced ingredients, and sustainable practices, in response to rising consumer awareness about health, wellness, and environmental impact. This trend is particularly evident in the proliferation of contemporary and fusion cuisines, which blend traditional Slovenian flavors with modern culinary techniques and global influences. The integration of sustainability into restaurant operations not only appeals to environmentally conscious diners but also aligns with Slovenia’s broader national strategy of promoting green tourism and sustainable economic development.

Regionally, Central Slovenia, which includes the capital Ljubljana, stands out as the dominant hub for restaurant activity, accounting for the largest market share. The region’s strategic location, vibrant business environment, and status as a cultural and tourist center make it the epicenter of culinary innovation and diversity. Other regions, such as Drava and Littoral–Inner Carniola, are also witnessing accelerated growth, driven by rising tourism and investment in hospitality infrastructure. Each region offers unique gastronomic experiences, reflecting local traditions and specialties, thereby contributing to the overall dynamism and resilience of the Slovenian restaurant market.

Type Analysis

The type segment of the Slovenian restaurant market encompasses Casual Dining, Fine Dining, Quick Service, Cafés & Bistros, and Others, each catering to distinct consumer preferences and dining occasions. Casual dining restaurants form the backbone of the market, offering a relaxed atmosphere, diverse menus, and moderate pricing that appeal to families, groups, and everyday diners. These establishments have seen steady growth, driven by increasing urbanization and the rising trend of social dining. Fine dining, while representing a smaller market share, has gained prominence due to Slovenia’s growing reputation in the international culinary scene. The rise of Michelin-starred restaurants and gastronomic events has positioned fine dining as a key driv

To increase the accessibility and diversity of easy reading in Slovenian and to create a prototype system that automatically simplifies texts in Slovenian, we prepared a dataset for the Slovenian language that contains aligned simple and complex sentences, which can be used for further development of models for simplifying texts in Slovenian.

Dataset is a .json file that usually contains one complex ("kompleksni") and one simplified sentence ("enostavni") per row. However, if a complex sentence contains a lot of information we translated this sentence into more than one simplified sentences. Vice versa, more complex sentences can be translated into one simplified sentence if some information is given through more than one complex sentences but we summarised them into one simplified one.

Additional file 2: Table S2. Description of global goat breeds with SNP genotypes available in the DRYAD repository.

Background: The Slovenian territory is geographically positioned between the Alps, Adriatic Sea, Pannonian basin and the Dinaric Mountains and, as such, has served as a passageway for various populations in different periods of time. Turbulent historic events and diverse geography of the region have produced a diverse contemporary population whose genetic analysis could provide insight into past demographic events.Aim: The aims of this study were to characterize the Slovenian mitochondrial gene pool at the micro-geographic level and to compare it with surrounding populations.Subjects and methods: A total of 402 individuals from five Slovenian regions were analysed in this study by typing HVR I, HVR II and coding region polymorphisms of mtDNA.Results: Analysis revealed 47 haplogroups and sub-haplogroups, the most common of which were H*, H1, J1c, T2 and U5a. Intra-population comparisons revealed a sharp gradient of the J1c haplogroup between Slovenian regions, with a peak frequency of 24.5% being observed in the population of the Littoral Region.Conclusion: The sharp gradient of the J1c haplogroup between Slovenian regions is in line with the archaeological horizon known as Impressed Ware culture and could, therefore, represent a genetic trace of the early Neolithic expansion route along the East Adriatic coastal region.

In Slovenia, the estimated sales amount across various store categories provides key insights into the market's dynamics. Apparel, as a prominent category, generates significant sales, totaling $96.51M, which is 12.65% of the region's total sales in this sector. Sports follows with robust sales figures, achieving $68.91M in sales and comprising 9.03% of the region's total. Beauty & Fitness contributes a considerable amount to the regional market, with sales of $49.90M, accounting for 6.54% of the total sales in Slovenia. This breakdown highlights the varying economic impacts of different categories within the region, showcasing the diversity and strengths of each sector.

Additional file 1: Table S1. The samples of 478 Drežnica goats were collected at 26 Slovenian breeders/farms listed below together with their addresses.

This chart offers an insightful look at the store count by category in Slovenia. Leading the way is Apparel, with 695 stores, which is 17.00% of the total stores in the region. Next is Home & Garden, contributing 552 stores, or 13.50% of the region's total. Beauty & Fitness also has a notable presence, with 484 stores, making up 11.84% of the store count in Slovenia. This breakdown provides a clear picture of the diverse retail landscape in Slovenia, showcasing the variety and scale of stores across different categories.

This set of maps shows the representation of 152 vascular plant families within forest, grassland, scrub and wetland plant communities across Europe. We used a set of 816,005 vegetation plots from the European Vegetation Archive (EVA; Chytrý et al. 2016), and for each plot, we calculated the relative species richness of individual plant families (number of species belonging to the family divided by the total number of species in the plot). The relative species richness was then mapped, averaged across all plots in 50 km × 50 km UTM grid cells, for each family and broad habitat groups – forests, grasslands, scrub and wetlands.

Here we provide the maps as (i) images (PDF files) and (ii) tables (XLSX files) containing the source data to be used for visualization in R or GIS software.

The colour scale in the maps was generated from the data using the k-means algorithm. For each map, the distribution of the values and the minimum, maximum and median values are shown. The maps only show grid cells containing at least five vegetation plots. Of these grid cells, those with mean relative species richness of the mapped family equal to zero are in grey. For each map, we also provide the number of genera/species included, the Shannon diversity index for the combination of the family and the habitat group, and the number of plots included (both absolute and relative to the total number of plots in the habitat group). The Shannon diversity index is based on species occurrences in vegetation plots: higher values indicate that the family is represented by many species with relatively even occurrence frequencies, while lower values indicate that it is represented by few species or some species is much more frequent than the others. For the combinations of families and habitat groups not represented in the dataset, blank maps were generated and the respective columns in tables filled with NAs. For further details, see Večeřa et al. (2021) Journal of Vegetation Science https://doi.org/10.1111/jvs.13035.

Contents:

Families_maps_all.pdf – a file containing maps of mean relative species richness of all the families

Families_maps_one-by-one.rar – separate files of individual families

Families_attribute_table_data.xlsx – a matrix of mean relative species richness for vascular plant families (columns) in 50 km × 50 km UTM grid cells (rows) in forests, grasslands, scrub and wetlands (four sheets); a family is considered absent (the mean relative species richness equal to zero) only in grid cells containing at least five vegetation plots; grid cells with no data or containing less than five plots are marked NA.

Colour_scale_codes.xlsx – a table of colours (HEX and RGB definitions) used for generating colour scales in the maps, with colour-scale codes (0–7) that may be used in combination with the file Families_attribute_table_colour-codes.xlsx to generate maps with the same colour representation as the original maps.

Families_attribute_table_colour-codes.xlsx – a matrix of colour-scale codes representing eight classes of the mean relative species richness (columns) in 50 km × 50 km UTM grid cells (rows) in forests, grasslands, scrub and wetlands (four sheets). In combination with the file Colour_scale_codes.xlsx, this table enables to reproduce the maps with the original colour representation. A family is considered absent (colour-scale code equal to zero) only in grid cells containing at least five vegetation plots; grid cells with no data or containing less than five plots are marked NA.

Europe_CGRS_grid.rar – a shapefile of the 50 km × 50 km UTM grid for Europe (originally available at: https://www.eea.europa.eu/data-and-maps/data/common-european-chorological-grid-reference-system-cgrs) to which data from the files Families_attribute_table_data.xlsx and Families_attribute_table_colour-codes.xlsx may be joined.