As of 2023, roughly 5.03 million out of Finland's total population of 5.6 million were of Finnish origin. After Finnish and European origins, the largest inhabitant groups in Finland were of Asian and African origin.

This dataset tracks annual diversity score from 1991 to 2023 for Finland Elementary School vs. Ohio and South-Western City School District

The data originates from national monitoring on the effects of the Finnish agri-environment support scheme (MYTVAS). In 2001, 2005, and 2010 a total of 52 one km2 quadrates situated in four geographical regions in Finland were sampled using stratified random sampling. Distance between the quadrates was one km, in most cases 10 km. In each 1 km2 quadrate, vascular plants were recorded in up to 12 separate 50 m long and 1 m wide transect lines (with total monitored area of 50 m2). The transect lines located in the centre of the habitat patch, at least 50 m distance from each other. The habitats were open and semi-open uncultivated patches. The data of same 580 transects has been studied in all the study years. The methods are described in the following five publications:

Jauni, M. & Helenius, J., 2008. Putkilokasvien monimuotoisuus maatalousalueilla 2001-2006. In: Kuussaari, M., Heliölä, J., Tiainen, J., Helenius, J. (Eds.), Maatalouden ympäristötuen merkitys luonnon monimuotoisuudelle ja maisemalle. MYTVAS-loppuraportti 2000-2006 (Significance of the Finnish agri-environment support scheme for biodiversity and landscape: Final report 2000-2006, in Finnish). Suomen ympäristö 4/2008: 23-49.

Pakkanen, H. & Helenius, J. 2004. Kasvien monimuotoisuuden seuranta - toimenpiteiden vaikutukset pientareilla ja suojakaistoilla. In: Kuussaari, M., Tiainen, J., Helenius, J., Hietala-Koivu, R. & Heliölä, J. (Eds.). Maatalouden ympäristötuen merkitys luonnon monimuotoisuudelle ja maisemalle. MYTVAS-seurantatutkimus 2000–2003 (Significance of the Finnish agri-environment support scheme for biodiversity and landscape: Results of the MYTVAS project 2000-2003, in Finnish). Suomen ympäristö 709: 30-43.

Kuussaari, M., Heliölä, J. & Luoto, M. 2004. Farmland biodiversity indicators and monitoring in Finland. In: Groom, G. (Ed.). Developments in Strategic Landscape Monitoring for the Nordic Countries. Nordic Council of Ministers. ANP 705: 28-40.

The Major Histocompatibility Complex (MHC, 6p21) codes for traditional HLA and other host response related genes. The polymorphic HLA-DRB1 gene in MHC Class II has been associated with several complex diseases. In this study we focus on MHC haplotype structures in the Finnish population. We explore the variability of extended HLA-DRB1 haplotypes in relation to the other traditional HLA genes and a selected group of MHC class III genes. A total of 150 healthy Finnish individuals were included in the study. Subjects were genotyped for HLA alleles (HLA-A, -B, -DRB1, -DQB1, and -DPB1). The polymorphism of TNF, LTA, C4, BTNL2 and HLA-DRA genes was studied with 74 SNPs (single nucleotide polymorphism). The C4A and C4B gene copy numbers and a 2-bp silencing insertion at exon 29 in C4A gene were analysed with quantitative genomic realtime-PCR. The allele frequencies for each locus were calculated and haplotypes were constructed using both the traditional HLA alleles and SNP blocks. The most frequent Finnish A∼B∼DR -haplotype, uncommon in elsewhere in Europe, was A*03∼B*35∼DRB1*01∶01. The second most common haplotype was a common European ancestral haplotype AH 8.1 (A*01∼B*08∼DRB1*03∶01). Extended haplotypes containing HLA-B, TNF block, C4 and HLA-DPB1 strongly increased the number of HLA-DRB1 haplotypes showing variability in the extended HLA-DRB1 haplotype structures. On the contrary, BTNL2 block and HLA-DQB1 were more conserved showing linkage with the HLA-DRB1 alleles. We show that the use of HLA-DRB1 haplotypes rather than single HLA-DRB1 alleles is advantageous when studying the polymorphisms and LD patters of the MHC region. For disease association studies the HLA-DRB1 haplotypes with various MHC markers allows us to cluster haplotypes with functionally important gene variants such as C4 deficiency and cytokines TNF and LTA, and provides hypotheses for further assessment. Our study corroborates the importance of studying population-specific MHC haplotypes.

Introduction

Welcome to the Finnish General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of Finnish speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Finnish communication.

Curated by FutureBeeAI, this 30 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade Finnish speech models that understand and respond to authentic Finnish accents and dialects.

Speech Data

The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Finnish. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple Finnish speech and language AI applications:

The dataset contains data on the representation of the 16 bumblebee (Bombus spp.) species collected from 55 sampling points in urban landscapes of six Finnish cities (Helsinki, Espoo, Tampere, Turku, Kuopio, and Joensuu) during summer 2022. The estimated geographic area of the sampling campaign covered 44 160 km2. The total sample size is 512 specimens, from which 338 are females (workers and queens) and 174 are males. The dataset contains information on the collection time and the associated plant species from which bumblebees were sampled. Associated plants were Finnish native wildflowers and ornamental plant species flowering in cities during the collection time. The sampling campaign was performed as a part of the “CarPLANT” project supported by the Maj and Tor Nessling Foundation [grant 202200067].

Finland has a healthy diversity in the artificial intelligence (AI) technologies used by its businesses and organizations. Machine learning, the most simple form of AI, was the most used AI technology with over ** percent of respondents reporting their organization as using it. The least used was reinforcement learning, a hybrid of machine learning and deep learning and quite a specialized field.

It has previously been demonstrated that the advance of the Neolithic Revolution from the Near East through Europe was decelerated in the northernmost confines of the continent, possibly as a result of space and resource competition with lingering Mesolithic populations. Finland was among the last domains to adopt a farming lifestyle, and is characterized by substructuring in the form of a distinct genetic border dividing the northeastern and southwestern regions of the country. To explore the origins of this divergence, the geographical patterns of mitochondrial and Y-chromosomal haplogroups of Neolithic and Mesolithic ancestry were assessed in Finnish populations. The distribution of these uniparental markers revealed a northeastern bias for hunter-gatherer haplogroups, while haplogroups associated with the farming lifestyle clustered in the southwest. In addition, a correlation could be observed between more ancient mitochondrial haplogroup age and eastern concentration. These results coupled with prior archeological evidence suggest the genetic northeast/southwest division observed in contemporary Finland represents an ancient vestigial border between Mesolithic and Neolithic populations undetectable in most other regions of Europe.

Welcome to the Finnish Extraction Type Prompt-Response Dataset, a meticulously curated collection of 1500 prompt and response pairs. This dataset is a valuable resource for enhancing the data extraction abilities of Language Models (LMs), a critical aspect in advancing generative AI.

Dataset Content

This extraction dataset comprises a diverse set of prompts and responses where the prompt contains input text, extraction instruction, constraints, and restrictions while completion contains the most accurate extraction data for the given prompt. Both these prompts and completions are available in Finnish language.

These prompt and completion pairs cover a broad range of topics, including science, history, technology, geography, literature, current affairs, and more. Each prompt is accompanied by a response, providing valuable information and insights to enhance the language model training process. Both the prompt and response were manually curated by native Finnish people, and references were taken from diverse sources like books, news articles, websites, and other reliable references.

This dataset encompasses various prompt types, including instruction type, continuation type, and in-context learning (zero-shot, few-shot) type. Additionally, you'll find prompts and responses containing rich text elements, such as tables, code, JSON, etc., all in proper markdown format.

Prompt Diversity

To ensure diversity, this extraction dataset includes prompts with varying complexity levels, ranging from easy to medium and hard. Additionally, prompts are diverse in terms of length from short to medium and long, creating a comprehensive variety. The extraction dataset also contains prompts with constraints and persona restrictions, which makes it even more useful for LLM training.

Response Formats

To accommodate diverse learning experiences, our dataset incorporates different types of responses depending on the prompt. These formats include single-word, short phrase, single sentence, and paragraph type of response. These responses encompass text strings, numerical values, and date and time, enhancing the language model's ability to generate reliable, coherent, and contextually appropriate answers.

Data Format and Annotation Details

This fully labeled Finnish Extraction Prompt Completion Dataset is available in JSON and CSV formats. It includes annotation details such as a unique ID, prompt, prompt type, prompt length, prompt complexity, domain, response, response type, and rich text presence.

Quality and Accuracy

Our dataset upholds the highest standards of quality and accuracy. Each prompt undergoes meticulous validation, and the corresponding responses are thoroughly verified. We prioritize inclusivity, ensuring that the dataset incorporates prompts and completions representing diverse perspectives and writing styles, maintaining an unbiased and discrimination-free stance.

The Finnish version is grammatically accurate without any spelling or grammatical errors. No copyrighted, toxic, or harmful content is used during the construction of this dataset.

Continuous Updates and Customization

The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. Ongoing efforts are made to add more assets to this dataset, ensuring its growth and relevance. Additionally, FutureBeeAI offers the ability to gather custom extraction prompt and completion data tailored to specific needs, providing flexibility and customization options.

License

The dataset, created by FutureBeeAI, is now available for commercial use. Researchers, data scientists, and developers can leverage this fully labeled and ready-to-deploy Finnish Extraction Prompt-Completion Dataset to enhance the data extraction abilities and accurate response generation capabilities of their generative AI models and explore new approaches to NLP tasks.

The Finnish Landrace breed of chickens (in Finnish suomalainen maatiaiskana) traces its origins to almost 1,000 years ago. Today, remnant populations of phenotypically distinct lineages are maintained by a network of volunteer hobbyists in Finland, managed by Natural Resources Institute Finland (Luke). Guided by a prior Major Histocompatibility Complex B-locus haplotype study, we sought now to characterize genetically Finnish Landrace chickens using denser genomic sampling. A new panel of 101 selectively neutral SNP sites was used to interrogate genetic variation in 192 individuals sampled from 13 putatively distinctive population units. Individuals partitioned into K = 11 genetic clusters characterized by high levels of genetic diversity, strong patterns of genetic structure and low levels of inbreeding. Evidence of an undocumented genetic lineage was also discovered. Facilitated by an inexpensive SNP assay, this study shows that the genetic integrity of the Finnish Landrace persists and represents a rich resource of natural (adaptive) genomic variation.

Welcome to the Finnish Brainstorming Prompt-Response Dataset, a meticulously curated collection of 2000 prompt and response pairs. This dataset is a valuable resource for enhancing the creative and generative abilities of Language Models (LMs), a critical aspect in advancing generative AI.

Dataset Content

This brainstorming dataset comprises a diverse set of prompts and responses where the prompt contains instruction, context, constraints, and restrictions while completion contains the most accurate response list for the given prompt. Both these prompts and completions are available in Finnish language.

These prompt and completion pairs cover a broad range of topics, including science, history, technology, geography, literature, current affairs, and more. Each prompt is accompanied by a response, providing valuable information and insights to enhance the language model training process. Both the prompt and response were manually curated by native Finnish people, and references were taken from diverse sources like books, news articles, websites, and other reliable references.

This dataset encompasses various prompt types, including instruction type, continuation type, and in-context learning (zero-shot, few-shot) type. Additionally, you'll find prompts and responses containing rich text elements, such as tables, code, JSON, etc., all in proper markdown format.

Prompt Diversity

To ensure diversity, our brainstorming dataset features prompts of varying complexity levels, ranging from easy to medium and hard. The prompts also vary in length, including short, medium, and long prompts, providing a comprehensive range. Furthermore, the dataset includes prompts with constraints and persona restrictions, making it exceptionally valuable for LLM training.

Response Formats

Our dataset accommodates diverse learning experiences, offering responses across different domains depending on the prompt. For these brainstorming prompts, responses are generally provided in list format. These responses encompass text strings, numerical values, and dates, enhancing the language model's ability to generate reliable, coherent, and contextually appropriate answers.

Data Format and Annotation Details

This fully labeled Finnish Brainstorming Prompt Completion Dataset is available in both JSON and CSV formats. It includes comprehensive annotation details, including a unique ID, prompt, prompt type, prompt length, prompt complexity, domain, response, and the presence of rich text.

Quality and Accuracy

Our dataset upholds the highest standards of quality and accuracy. Each prompt undergoes meticulous validation, and the corresponding responses are thoroughly verified. We prioritize inclusivity, ensuring that the dataset incorporates prompts and completions representing diverse perspectives and writing styles, maintaining an unbiased and discrimination-free stance.

The Finnish version is grammatically accurate without any spelling or grammatical errors. No copyrighted, toxic, or harmful content is used during the construction of this dataset.

Continuous Updates and Customization

The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. We continuously work to expand this dataset, ensuring its ongoing growth and relevance. Additionally, FutureBeeAI offers the flexibility to curate custom brainstorming prompt and completion datasets tailored to specific requirements, providing you with customization options.

License

This dataset, created by FutureBeeAI, is now available for commercial use. Researchers, data scientists, and developers can leverage this fully labeled and ready-to-deploy Finnish Brainstorming Prompt-Completion Dataset to enhance the creative and accurate response generation capabilities of their generative AI models and explore new approaches to NLP tasks.

What’s Included

Introducing the Finnish Shopping List Image Dataset - a diverse and comprehensive collection of handwritten text images carefully curated to propel the advancement of text recognition and optical character recognition (OCR) models designed specifically for the Finnish language.

Containing more than 2000 images, this Finnish OCR dataset offers a wide distribution of different types of shopping list images. Within this dataset, you'll discover a variety of handwritten text, including sentences, and individual item name words, quantity, comments, etc on shopping lists. The images in this dataset showcase distinct handwriting styles, fonts, font sizes, and writing variations.

To ensure diversity and robustness in training your OCR model, we allow limited (less than three) unique images in a single handwriting. This ensures we have diverse types of handwriting to train your OCR model on. Stringent measures have been taken to exclude any personally identifiable information (PII) and to ensure that in each image a minimum of 80% of space contains visible Finnish text.

The images have been captured under varying lighting conditions, including day and night, as well as different capture angles and backgrounds. This diversity helps build a balanced OCR dataset, featuring images in both portrait and landscape modes.

All these shopping lists were written and images were captured by native Finnish people to ensure text quality, prevent toxic content, and exclude PII text. We utilized the latest iOS and Android mobile devices with cameras above 5MP to maintain image quality. Images in this training dataset are available in both JPEG and HEIC formats.

In addition to the image data, you will receive structured metadata in CSV format. For each image, this metadata includes information on image orientation, country, language, and device details. Each image is correctly named to correspond with the metadata.

This metadata serves as a valuable resource for understanding and characterizing the data, aiding informed decision-making in the development of Finnish text recognition models.

We are committed to continually expanding this dataset by adding more images with the help of our native Finnish crowd community.

If you require a customized OCR dataset containing shopping list images tailored to your specific guidelines or device distribution, please don't hesitate to contact us. We have the capability to curate specialized data to meet your unique requirements.

Additionally, we can annotate or label the images with bounding boxes or transcribe the text in the images to align with your project's specific needs using our crowd community.

This image dataset, created by FutureBeeAI, is now available for commercial use.

Leverage this shopping list image OCR dataset to enhance the training and performance of text recognition, text detection, and optical character recognition models for the Finnish language. Your journey to improved language understanding and processing begins here.

BackgroundIn Finland, the first infections caused by the 2009 pandemic influenza A(H1N1) virus were identified on May 10. During the next three months almost all infections were found from patients who had recently traveled abroad. In September 2009 the pandemic virus started to spread in the general population, leading to localized outbreaks and peak epidemic activity was reached during weeks 43–48.Methods/ResultsThe nucleotide sequences of the hemagglutinin (HA) and neuraminidase (NA) genes from viruses collected from 138 patients were determined. The analyzed viruses represented mild and severe infections and different geographic regions and time periods. Based on HA and NA gene sequences, the Finnish pandemic viruses clustered in four groups. Finnish epidemic viruses and A/California/07/2009 vaccine virus strain varied from 2–8 and 0–5 amino acids in HA and NA molecules, respectively, giving a respective maximal evolution speed of 1.4% and 1.1%. Most amino acid changes in HA and NA molecules accumulated on the surface of the molecule and were partly located in antigenic sites. Three severe infections were detected with a mutation at HA residue 222, in two viruses with a change D222G, and in one virus D222Y. Also viruses with change D222E were identified. All Finnish pandemic viruses were sensitive to oseltamivir having the amino acid histidine at residue 275 of the neuraminidase molecule.ConclusionsThe Finnish pandemic viruses were quite closely related to A/California/07/2009 vaccine virus. Neither in the HA nor in the NA were changes identified that may lead to the selection of a virus with increased epidemic potential or exceptionally high virulence. Continued laboratory-based surveillance of the 2009 pandemic influenza A(H1N1) is important in order to rapidly identify drug resistant viruses and/or virus variants with potential ability to cause severe forms of infection and an ability to circumvent vaccine-induced immunity.

The data originates from national monitoring on the effects of the Finnish agri-environment support scheme (MYTVAS, see Kuussaari et al. 2004). In 2001 and 2005, in total 58 one km2 quadrates situated in four geographical regions in Finland were sampled using stratified random sampling. Distance between the quadrates was in most cases 10-20 km. In each 1 km2 quadrate, bumblebees and solitary bees were collected using so-called 'yellow traps'.

The methods are described in the following publications:

Paukkunen, J., Heliölä, J. & Kuussaari, M. 2008. Mesipistiäisten monimuotoisuus maatalousalueilla. In: Kuussaari, M., Heliölä, J., Tiainen, J., Helenius, J. (Eds.). Maatalouden ympäristötuen merkitys luonnon monimuotoisuudelle ja maisemalle. MYTVAS-loppuraportti 2000-2006 (Significance of the Finnish agri-environment support scheme for biodiversity and landscape: Final report 2000-2006, in Finnish). Suomen ympäristö 4/2008: 70-91.

Kuussaari, M., Heliölä, J. & Luoto, M. 2004. Farmland biodiversity indicators and monitoring in Finland. In: Groom, G. (Ed.). Developments in Strategic Landscape Monitoring for the Nordic Countries. Nordic Council of Ministers. ANP 705: 28-40.

Heliölä, J., Söderman, G., Kuussaari, M. & Paukkunen, J. 2004. Mesipistiäisten monimuotoisuus. In: Kuussaari, M., Tiainen, J., Helenius, J., Hietala-Koivu, R. & Heliölä, J. (Eds.). Maatalouden ympäristötuen merkitys luonnon monimuotoisuudelle ja maisemalle. MYTVAS-seurantatutkimus 2000–2003 (Significance of the Finnish agri-environment support scheme for biodiversity and landscape: Results of the MYTVAS project 2000-2003, in Finnish). Suomen ympäristö 709: 82-91.

Additional publications using the wild bee data of this project:

Kivinen, S., Luoto, M., Kuussaari, M. & Helenius, J. 2006. Multi-species richness of boreal agricultural landscapes: effects of climate, habitat, soil and geographical location. Journal of Biogeography 33: 862-875.

Paukkunen, J., Heliölä, J. & Kuussaari, M. 2007. Maatalousympäristön kimalaisten elinympäristöt ja kannankehitys Suomessa. Teoksessa:: Salonen, J., Keskitalo, M. & Segerstedt, M. (toim.): Peltoluonnon ja viljelyn monimuotoisuus. Maa- ja elintarviketalouden tutkimuskeskuksen julkaisuja 110: 289-312.

In Finland, the distribution of stores across different platforms presents a dynamic picture of the market. WooCommerce, as a leading platform, hosts 12.7K stores, accounting for 36.44% of the total store count in the region. This is closely followed by Custom Cart, which supports 10.68K stores, representing 30.66% of the region's total. Shopify makes a significant contribution with 6.66K stores, or 19.13% of the total. The chart underscores the diversity and preferences of store owners in Finland regarding their choice of platform.

The data originates from national monitoring on the effects of the Finnish agri-environment support scheme (MYTVAS, see Kuussaari et al. 2004). In 2001, 2005, and 2010 in a total of 58 one km2 quadrates situated in four geographical regions in Finland were sampled using stratified random sampling. Distance between the quadrates was in most cases 10-20 km. In each 1 km2 quadrate, butterflies and moths were recorded using the 'Pollard walk' method in 20 separate 50 m long transect lines (total transect length 1 km/site). The transect lines located at least 50 m distance from each other. They were placed in open and semi-open uncultivated habitat patches, including patches of semi-natural grasslands, field margins, road verges and forest edges.

The methods are described in the following publications:

Heliölä, J. & Kuussaari, M. 2008. Perhoskantojen seuranta maatalousalueilla vuosina 2001-2006. In: Kuussaari, M., Heliölä, J., Tiainen, J., Helenius, J. (Eds.). Maatalouden ympäristötuen merkitys luonnon monimuotoisuudelle ja maisemalle. MYTVAS-loppuraportti 2000-2006 (Significance of the Finnish agri-environment support scheme for biodiversity and landscape: Final report 2000-2006, in Finnish). Suomen ympäristö 4/2008: 50-69.

Kuussaari, M., Heliölä, J., Luoto, M. & Pöyry, J. 2007. Determinants of local species richness of diurnal Lepidoptera in boreal agricultural landscapes. Agriculture, Ecosystems and Environment 122: 366-376.

Kuussaari, M., Heliölä, J. & Luoto, M. 2004. Farmland biodiversity indicators and monitoring in Finland. In: Groom, G. (Ed.). Developments in Strategic Landscape Monitoring for the Nordic Countries. Nordic Council of Ministers. ANP 705: 28-40.

Kuussaari, M. & Heliölä, J. 2004. Perhosten monimuotoisuus eteläsuomalaisilla maatalousalueilla. In: Kuussaari, M., Tiainen, J., Helenius, J., Hietala-Koivu, R. & Heliölä, J. (Eds.). Maatalouden ympäristötuen merkitys luonnon monimuotoisuudelle ja maisemalle. MYTVAS-seurantatutkimus 2000–2003 (Significance of the Finnish agri-environment support scheme for biodiversity and landscape: Results of the MYTVAS project 2000-2003, in Finnish). Suomen ympäristö 709: 44-81.

Additional publications using the butterfly data of this project include:

Ekroos, J. & Kuussaari, M. 2011. Landscape context affects the relationship between local and landscape species richness of butterflies in semi-natural habitats. Ecography, in press.

Ekroos, J., Heliölä, J. & Kuussaari, M. 2010: Homogenization of lepidopteran communities in intensively cultivated agricultural landscapes. Journal of Applied Ecology 47:459-467.

Kivinen, S., Luoto, M., Kuussaari, M. & Helenius, J. 2006. Multi-species richness of boreal agricultural landscapes: effects of climate, habitat, soil and geographical location. Journal of Biogeography 33: 862-875.

Vegetation survey of environmental fallows was carried out in Uusimaa, Pohjois-Pohjanmaa and Pirkanmaa regions in summers 2010 and 2011. The species richness and composition of vascular plants as well as vegetation structure were surveyed on 229 environmental fallows. The data was collected on 1 x 12.5 m-sized transect lines. Background information on the study fields was collected through a farmer questionnaire and interviews. The methods are described in the following publication: Toivonen, M. 2010. Luonnonhoitopellot maatalousympäristön luonnon monimuotoisuuden edistämisessä (Enhancing biodiversity of agricultural landscapes through environmental fallows, in Finnish). Pro gradu. Maataloustieteiden laitos, Helsingin yliopisto. 63 p.

The data originate from national monitoring on the effects of the Finnish agri-environment support scheme (MYTVAS). The data was collected in 1995, 1997, 1998 and 1999 in four different watershed areas in Finland. 225 boundaries were preselected from aerial photographs to find the sites located next to waterways and open areas, and with no or only minor occurences of trees or shrubs. In each site, vascular plant species coverages were assessed in one to five 0.25 m2 (0.5 x 0.5 m) quadrates 20 m apart from one another. In 1998, another dataset was collected by estimating the abundances of plant species in the whole boundary area. Soil samples were taken in each site for soil analysis.

The methods have been described in the following publications:

Tarmi, S. & Helenius, J. 2002. Maatalouden ympäristöohjelman mukaisten piennarten ja suojakaistojen toteutuminen ja niiden kasviyhteisöjen monimuotoisuus (Vegetation in boundaries and buffer zones - realization of boundaries and buffer zones obliged in agri-environmental programme and diversity of vascular plant communities, in Finnish). Helsingin yliopisto. Soveltavan biologian laitoksen julkaisuja 9. 35 p.

Tarmi, S., Helenius, J. & Hyvönen, T. 2009. Importance of edaphic, spatial and management factors for plant communities of field boundaries. Agriculture, Ecosystems and Environment 131: 201–206.

Tarmi, S., Tuuri, H. & Helenius, J. 2002. Plant communities of field boundaries in Finnish farmland. Agricultural and food science in Finland 11: 121-135.

This study explores the landscape of diversity leadership in Finnish comprehensive schools through the experiences and reflections of principals in the Helsinki area. Employing qualitative thematic analysis of principal interviews, we identified five key categories capturing the challenges and complexities principals navigate in leading diverse school communities: supporting structures for diversity, practical arrangements in school management, social encounters in schools, managemental issues concerning teachers and staff, and principals’ self-reflection. Principals emphasized the importance of establishing clear structures and practices to support diversity and inclusion, while also recognizing the need for flexibility and adaptability to meet the varied needs of diverse learners. They had to deal with practical challenges in managing certain aspect of diversity, constraints of municipal policies and resources, and the complexities of building positive relationships among diverse communities. Principals also engaged in critical self-reflection on their own identities, assumptions, and leadership practices in relation to diversity. Findings underscore the multifaceted nature of diversity leadership, encompassing structural, practical, social, professional, and personal dimensions. The study highlights the need for more systemic support, contextualized approaches, and professional development to build capacity for effective diversity leadership in increasingly diverse Finnish schools.

We extracted genomic DNA from whole blood using a BioSprint 15 DNA Blood Kit (Qiagen, Ref. 940017) and a Kingfisher magnetic particle processor. Individual male black grouse were genotyped at 12 autosomal microsatellite loci (BG6, BG15, BG16, BG18, BG19, BG20 (Piertney & Höglund, 2001); TTT1, TTD2, TTD3 (Caizergues et al., 2001); TUD6, TUT3, TUT4, (Segelbacher et al., 2000). The adults were additionally genotyped at TTT2 (Caizergues et al., 2001) bringing the total number of microsatellites genotyped in adults and chicks to 13 and 12 respectively. Microsatellite genotyping was performed following the protocol described in (Lebigre et al., 2007).