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Asiantuntijakysely kaupunkibiotooppien monimuotoisuudesta ja kaupunkibiotooppikartta Suomen pääkaupunkiseudulla / Expert Questionnaire Results Regarding Biodiversity of Urban Biotopes and an Urban Biotope Map in Helsinki Metropolitan Area, Finland
<p>(in English below)</p> <p><strong>ASIANTUNTIJAKYSELY KAUPUNKIBIOTOOPPIEN MONIMUOTOISUUDESTA JA KAUPUNKIBIOTOOPPIKARTTA SUOMEN PÄÄKAUPUNKISEUDULLA</strong></p> <p><strong>Tausta ja tavoitteet</strong></p> <p>Luonnonsuojelu on keskittynyt perinteisesti Suomessa tiettyihin lajeihin ja luontotyyppeihin, mikä ei välttämättä kuvaa erilaisten kaupunkiympäristöjen ekologisia arvoja kattavasti. Lisäksi tulisi huomioida eliöyhteisöjä, jotka turvaavat kestävän ja monimuotoisen ekosysteemien toiminnallisuuden.</p> <p>Tämän asiantuntijakyselyn tavoitteena oli selvittää, miten erilaiset kaupunkibiotoopit tukevat erilaisia ekologisten yhteisöjen monimuotoisuutta kuvaavia tekijöitä eri eliöryhmillä. Nämä <em>monimuotoisuuden laatutekijät</em> kuvaavat yhdessä biotooppien roolia kaupunkiluonnon monimuotoisuudessa, toiminnallisuudessa ja täten epäsuorasti myös mm. ekosysteemipalvelujen tarjoamisessa. Kyselyn tuloksia voidaan hyödyntää ekologisten arvojen turvaamiseksi paremmin osana kaupunkisuunnittelua tai kaupunkiluonnon monimuotoisuuden kattavan turvaamisen perustana. Kysely keskittyi pääkaupunkiseudun (Helsinki, Espoo, Vantaa, Kauniainen) biotooppeihin. Lisäksi asiantuntijat antoivat arvionsa biotooppipisteytyksen sovellettavuudesta muualla Suomessa.</p> <p>Kysely on kuvattu Terra-lehdessä (Jalkanen & Vierikko 2022) sekä Jalkanen ym. (2020).</p> <p><strong>Menetelmät</strong></p> <p><em>Kaupunkibiotooppien pisteytys</em></p> <p>Aineisto kerättiin internetkyselyllä 5.10.-21.11.2016 välisenä aikana. Kysely lähetettiin 38 paikalliselle lajiasiantuntijalle (Luonnontieteellisessä keskusmuseossa, Helsingin yliopistossa, Suomen ympäristökeskuksessa, ympäristökonsulttiyrityksissä ja luontojärjestöissä), joista 24 osallistui kyselyyn.</p> <p>Mukana olleet asiantuntijat (suluissa heidän lajiryhmänsä):</p> <ul> <li>Heidi Björklund (Linnut)</li> <li>Tea von Bonsdorff (Sienet, muut kuin käävät)</li> <li>Eero Haapanen (Nisäkkäät, muut kuin lepakot)</li> <li>Nina Hagner-Wahlsten (Lepakot)</li> <li>Jari Kaitila (Perhoset)</li> <li>Jarkko Korhonen (Sienet, muut kuin käävät)</li> <li>Jaakko Kullberg (Perhoset)</li> <li>Eeva-Maria Kyheröinen (Lepakot)</li> <li>Esa Lammi (Putkilokasvit)</li> <li>Riku Lumiaro (Nisäkkäät, muut kuin lepakot)</li> <li>Sampsa Malmberg (Kovakuoriaiset)</li> <li>Ilpo Mannerkoski (Kovakuoriaiset)</li> <li>Olli Manninen (Käävät)</li> <li>Heikka Marttila-Tornio (Matelijat & sammakkoeläimet)</li> <li>Juho Paukkunen (Pistiäiset)</li> <li>Terhi Ryttäri (Putkilokasvit)</li> <li>Jarmo Saarikivi (Matelijat & sammakkoeläimet)</li> <li>Hannu Sarvanne (Linnut)</li> <li>Keijo Savola (Käävät)</li> <li>Ilkka Teräs (Pistiäiset)</li> <li>Stephen Venn (Kovakuoriaiset)</li> <li>Tarmo Virtanen (Perhoset)</li> <li>Terhi Wermundsen (Lepakot)</li> <li>Rauno Yrjölä (Linnut)</li> </ul> <p>Asiantuntijat pisteyttivät kyselyssä 68 biotooppia sen mukaan, kuinka ne tukevat heidän lajiryhmiensä eri ominaisuustekijöitä. Jokainen biotooppi arvioitiin erikseen kunkin tekijän näkökulmasta. Pisteet annettiin 5-portaisella asteikolla (0–4; 0 alin). Asiantuntijoita ohjeistettiin miettimäään koko vuodenaikaiskiertoa (arvioimaan biotooppien merkitystä siis myös esim. talvehtimisen kannalta). Kysely perustui Vierikon ym. (2014) biotooppiluokitteluun seuraavin muutoksin:</p> <ul> <li>Metsäbiotoopit jaettiin kahteen ikäluokkaan (30–100-vuotiaat ja yli 100-vuotiaat metsät)</li> <li>Piha-alueet jaettiin päällystettyihin ja maavaraisiin</li> <li>Tiiviiden pientaloalueiden ja townhouse-alueiden pihat lisättiin omana biotooppinaan</li> <li>Kalliolaet, -rinteet ja -seinämät sekä kivikot yhdistettiin samaksi biotoopiksi (paljaat kalliopinnat)</li> <li>Uimarannat, kanaalit ja rantaterassit yhdistettiin samaksi biotoopiksi (rakennetut rannat)</li> <li>Kiviseinät ja lintuluodot poistettiin</li> <li>Viherseinät lisättiin omana biotooppinaan</li> </ul> <p>Biotooppien merkitystä kysyttiin seuraavissa eliöyhteisöjen monimuotoisuuden laatua kuvaavissa kategorioissa:</p> <ol> <li>Lajirikkaus</li> <li>Vaatelias lajisto</li> <li>Biomassa</li> <li>Runsaus</li> <li>Tasaisuus</li> <li>Uniikkius</li> <li>Seudullinen edustavuus</li> <li>Herkkyys ihmissyntyisiä häiriöitä kohtaan</li> <li>Kytkeytyvyys</li> </ol> <p>Asiantuntijat arvioivat lisäksi omien vastaustensa luotettavuutta, erikseen jokaisen tekijän kohdalla. Tässä aineistossa biotooppien pisteet on painotettu nousevan painokertoimen mukaan, jotta luotettavat vastaukset korostuvat epäluotettavia voimakkaammin. Luotettavuuskertoimet ovat 0, 1, 2, 4 ja 8, mitkä tarkoittavat “erittäin epäluotettavia”, “epäluotettavia”, “jonkin verran epäluotettavia”, ”melko luotettavia” ja ”erittäin luotettavia” vastauksia.</p> <p> <em>Biotooppipisteiden sovellettavuus muualla Suomessa</em></p> <p>Kyselyn jälkeen asiantuntijat arvioivat työpajassa, kuinka hyvin heidän vastauksiaan voi soveltaa muissa suomalaisissa kaupungeissa. Työpaja pidettiin 29.11.2016. Tarkka kysymyksenasettelu oli:</p> <p><em>Tämä kysely on laadittu Etelä-Suomen ja erityisesti pääkaupunkiseudun (Helsinki, Espoo, Vantaa, Kauniainen) näkökulmasta. Kuinka hyvin kyselyn tulokset kuvaavat eliöryhmäsi lajistoa muiden Suomen maakuntien kaupungeissa? Vastaa asteikolla 0–10 (0: tämän kyselyn tuloksia ei voi soveltaa lainkaan ko. maakunnan kaupunkeihin, 10: kyselyn tulokset soveltuvat erittäin hyvin ko. maakunnan kaupunkiluontoon). Vastaa myös, kuinka luotettavina vastauksiasi voidaan pitää asteikolla 0-3 (0: hyvin epäluotettavina, 3: hyvin luotettavina). <strong>Vastaa oman eliöryhmäsi näkökulmasta.</strong></em></p> <p>Tässä aineistossa vastaukset näytetään alkuperäisinä, eli luotettavia vastauksia ei korosteta erikseen kuten biotooppipisteytyksessä.</p> <p><strong>Aineistot:</strong></p> <p>Aineistot ovat suomeksi (etuliite ”FIN”) ja englanniksi (”ENG”). Aineisto sisältää:</p> <ul> <li>Monimuotoisuuden laatutekijöiden luonnehdinnat ja pisteytysohjeet (.pdf)</li> <li>Kaupunkibiotooppien luonnehdinnat (.pdf)</li> <li>Kaupunkibiotooppien pisteytys taulukkona (.xlsx)</li> <li>Taulukko vastausten sovellettavuudesta muualla Suomessa (.xlsx)</li> </ul> <p>Taulukkotiedostoissa monimuotoisuuden laatutekijän tai eliöryhmän perässä oleva numero viittaa asiantuntijaan (esim. lintuja koskevissa sarakkeissa ”Lajirikkaus 1” ja ”Vaateliaat lajit 1” viittaavat saman lintuasiantuntijan vastauksiin). Asiantuntijoiden vastaukset on listattu satunnaisjärjestyksessä. Kaikki asiantuntijat ovat suostuneet heidän vastaustensa ja nimiensä julkaisemiseen kirjallisesti.</p> <p><strong>KAUPUNKIBIOTOOPPIKARTTA</strong></p> <p>Kansiossa "FIN_Kaupunkibiotooppikartta" on rasterimuotoinen kartta pääkaupunkiseudun kaupunkibiotoopeista paikkatietomuodossa. Kartta on luotu 2021 eri paikkatietolähteistä (ks. Jalkanen ym. 2020). Kaupunkibiotooppikartta mahdollistaa esimerkiksi monimuotoisuusarvojen tarkastelun pääkaupunkiseudulla yhdessä asiantuntijakyselyn tulosten kanssa. HUOM! Karttaa ei ole tarkoitettu sellaisenaan suunnittelukäyttöön. Minkäänlaisia takuita tulosten oikeellisuudesta, virheettömyydestä tai käytettävyydestä ei myönnetä.</p> <p>Kansiossa on seuraavat tiedostot:</p> <ul> <li>Kaupunkibiotooppikartta (.tif) (CRS: EPSG 3902)</li> <li>Kaupunkibiotooppikartan soluarvojen selitykset (.xlsx). Kaupunkibiotooppikartta käsittää 53 biotooppia/maanpeiteluokkaa, eli kaikkia asiantuntijakyselyn biotooppeja ei ole pystytty koostamaan kartalle.</li> <li>Kaupunkibiotooppikartan koostamisen ja lähtöaineistojen kuvaus (.pdf)</li> </ul> <p><strong>Kiitokset: </strong>Kiitämme Silviya Korpiloa, Susanna Lehvävirtaa ja Stephen Venniä avusta englanninnosten kanssa. </p> <p><strong>Viittausohje:</strong> Jalkanen, J. & Vierikko, K. (2022) Asiantuntijakysely kaupunkibiotooppien monimuotoisuudesta sekä kaupunkibiotooppikartta Suomen pääkaupunkiseudulla [Aineisto] https://doi.org/10.5281/zenodo.6563190</p> <p>Aineistoon tulee viitata käytettäessä.</p> <p><strong>Viitteet: </strong></p> <ul> <li>Jalkanen, J. & Vierikko, K. (2022) Viheralueiden elonkirjo – Asiantuntijakysely ja luonnon monimuotoisuuden laatumittaristo kaupunkisuunnittelun tueksi. <em>Terra</em> 134: 207–223. https://doi.org/10.30677/terra.120163</li> <li>Jalkanen, J., Vierikko, K. & Moilanen, A. (2020) Spatial prioritization for urban Biodiversity Quality using biotope maps and expert opinion. <em>Urban Forestry & Urban Greening</em> 49: 126586. https://doi.org/10.1016/j.ufug.2020.126586.</li> <li>Vierikko, K., Niemelä, J., Salminen, J., Jalkanen, J. & Tamminen, N. 2014: Helsingin kestävä viherrakenne –Miten turvata kestävä viherrakenne ja kaupunkiluonnon monimuotoisuus tiivistyvässä kaupunkirakenteessa. Helsingin kaupunkisuunnitteluviraston yleissuunnitteluosaston selvityksiä 2014:27. Helsinki. 132 s.</li> </ul> <p> </p> <p><strong>EXPERT QUESTIONNAIRE RESULTS REGARDING BIODIVERSITY OF URBAN BIOTOPES AND AN URBAN BIOTOPE MAP IN HELSINKI METROPOLITAN AREA, FINLAND</strong></p> <p><strong>Background and aim:</strong></p> <p>Finnish biodiversity conservation has traditionally focused on certain species and biotopes, which does not necessarily describe urban areas’ ecological values in a comprehensive manner. In addition, focus should be put on ecological communities that enable resilient and diverse ecosystem functioning.</p> <p>The aim of this questionnaire was to determine how different urban biotopes support different biodiversity quality attributes of ecological communities of different higher taxonomic groups. Together these attributes describe biotopes’ support for sustainable urban ecosystem functioning and, thus, indirectly for ecosystem services provisioning. The results can be used to better preserve ecological values in urban planning and as a basis for comprehensive urban biodiversity conservation. The questionnaire focused on biotopes found in the Helsinki Metropolitan Area (HMA; municipal cities of Helsinki, Espoo, Vantaa, and Kauniainen), Southern Finland. In addition, the applicability of the scoring elsewhere in Finnish cities was evaluated.</p> <p>The questionnaire is described in Terra (Jalkanen & Vierikko 2022; in Finnish with English abstract) and in Jalkanen et al. (2020).</p> <p><strong>Methods:</strong></p> <p><em>Scoring of urban biotopes</em></p> <p>The data was collected using an online questionnaire during 5.10.-21.11.2016. It was sent to 38 local taxonomic experts (from Finnish Museum of Natural History, University of Helsinki, Finnish Environment Institute, environmental consultant firms, and local environmental NGOs), out of which 24 replied.</p> <p>Experts who participated were (their taxon):</p> <ul> <li>Heidi Björklund (Birds)</li> <li>Tea von Bonsdorff (Fungi, other than polypores)</li> <li>Eero Haapanen (Mammals, other than bats)</li> <li>Nina Hagner-Wahlsten (Bats)</li> <li>Jari Kaitila (Butterflies)</li> <li>Jarkko Korhonen (Fungi, other than polypores)</li> <li>Jaakko Kullberg (Butterflies)</li> <li>Eeva-Maria Kyheröinen (Bats)</li> <li>Esa Lammi (Vascular plants)</li> <li>Riku Lumiaro (Mammals, other than bats)</li> <li>Sampsa Malmberg (Beetles)</li> <li>Ilpo Mannerkoski (Beetles)</li> <li>Olli Manninen (Polypores)</li> <li>Heikka Marttila-Tornio (Herpetofauna)</li> <li>Juho Paukkunen (Hymenoptera)</li> <li>Terhi Ryttäri (Vascular Plants)</li> <li>Jarmo Saarikivi (Herpetofauna)</li> <li>Hannu Sarvanne (Birds)</li> <li>Keijo Savola (Polypores)</li> <li>Ilkka Teräs (Hymenoptera)</li> <li>Stephen Venn (Beetles)</li> <li>Tarmo Virtanen (Butterflies)</li> <li>Terhi Wermundsen (Bats)</li> <li>Rauno Yrjölä (Birds)</li> </ul> <p>In the questionnaire, the experts scored 68 local urban biotopes in terms of how well they support different biodiversity attributes of their taxonomic group. Each biotope was scored separately for every attribute. Scores were given on a 5-rank scale (0–4; 0 being the lowest). Experts were advised to consider all seasons (e.g., to consider the importance of the biotope also for wintering). We used the biotope classification from the expert questionnaire by Vierikko et al. (2014) with the following modifications:</p> <ul> <li>Forest biotopes were further divided into two age classes (30-100 y. and over 100 y.)</li> <li>Yards were further divided into sealed and bare yards</li> <li>Densely-built residential gardens & townhouse gardens were added as a new biotope</li> <li>Outcrops, ledges, and rocky grounds were combined to one biotope (bare rocks)</li> <li>Beaches, canals, and terraced embankments were combined to one biotope (artificial shores)</li> <li>Stone walls and bird-colonized islets were excluded</li> <li>Green walls were added as a new biotope</li> </ul> <p>The relevance of biotopes was evaluated for the following Biodiversity Quality attributes that describe or relate to the diversity of urban ecological communities:</p> <ol> <li>Species richness</li> <li>Habitat specialist species</li> <li>Biomass</li> <li>Abundance</li> <li>Evenness</li> <li>Uniqueness</li> <li>Regional representativeness</li> <li>Sensitivity towards anthropogenic disturbance</li> <li>Connectivity</li> </ol> <p>In addition, experts gave overall self-evaluated confidence rates for their answers concerning each attribute. In the data, biotopes’ scores are weighted by an increasing confidence coefficient in order to emphasize confident answers over non-confident ones. Confidence coefficients are 0, 1, 2, 4, and 8 that refer to ‘very unconfident’, ‘unconfident’, ‘somewhat unconfident’, ‘somewhat confident’, and ‘very confident’ answers, respectively.</p> <p><em>Applicability of the urban biotope scores elsewhere in Finland</em></p> <p>After the questionnaire, experts assessed how applicable their answers are elsewhere in Finland. This task was done in an expert workshop on 29.11.2016. The exact question, given to the experts, was:</p> <p><em>This questionnaire is designed from Southern Finnish, and especially from the Helsinki Metropolitan area (cities of Helsinki, Espoo, Vantaa, Kauniainen) perspective. How well do the questionnaire results describe the species assemblages of your taxon found in cities in other Finnish provinces? Answer on a scale of 0-10 (0: the results of this questionnaire are completely inapplicable to the cities in the given province, 10: the results of the questionnaire apply very well to the urban nature of the given province). Also evaluate the confidence of your answers at the scale of 0-3 (0: very unconfident, 3: very confident). <strong>Answer from the point of view of your own taxon.</strong></em></p> <p>In the data, the expert answers are reported as they were given, i.e., confident answers are not emphasized over non-confident ones like with biotope scores.</p> <p><strong>Data:</strong></p> <p>Data is provided in Finnish (files starting with ‘FIN’) and in English (‘ENG’). This data set includes:</p> <ul> <li>Biodiversity Quality attribute descriptions and scoring instructions (.pdf)</li> <li>Urban biotope descriptions (.pdf)</li> <li>Table of urban biotope scores (.xlsx)</li> <li>Table of the applicability of biotope scores elsewhere in Finland (.xlsx)</li> </ul> <p>In the tables, the Arabic number after the attribute or taxon name refers to the corresponding expert (i.e., under birds (‘Richness 1’ and ‘Specialist species 1’ refer to the answers of the same bird expert). Experts’ answers are listed in random order. All experts have agreed on publication of their answers and names using written informed consent.</p> <p><strong>URBAN BIOTOPE MAP</strong></p> <p>The folder ’ENG_UrbanBiotopeMap’ includes a raster-type GIS layer of urban biotopes in the Helsinki Metropolitan area. The map has been compiled from several GIS sources (see Jalkanen et al. 2020). The urban biotope map allows for example spatial analyses of biodiversity values together with the expert questionnaire results. OBS! The map is not meant for planning purposes. No warrant about the correctness, flawlessness, or feasibility of the results is given.</p> <p>The folder includes the following files:</p> <ul> <li>The urban biotope map (.tif) (CRS: EPSG 3902)</li> <li>The explanation of the cell values of the urban biotope map (.xlsx). The urban biotope map comprises of 53 different biotopes/land cover types, i.e., every biotope asked in the questionnaire are not found in the map.</li> </ul> <p><strong>Acknowledgements:</strong> We thank Silviya Korpilo, Susanna Lehvävirta, and Stephen Venn for help with the English translations.</p> <p><strong>How to cite: </strong>Jalkanen, J. & Vierikko, K. (2022) Expert questionnaire results regarding biodiversity of urban biotopes and an urban biotope map in Helsinki Metropolitan Area, Finland [Data set] https://doi.org/10.5281/zenodo.6563190</p> <p>Data should be properly cited when used.</p> <p><strong>References:</strong></p> <ul> <li>Jalkanen, J. & Vierikko, K. (2022) Viheralueiden elonkirjo – Asiantuntijakysely ja luonnon monimuotoisuuden laatumittaristo kaupunkisuunnittelun tueksi (Biodiversity in urban green spaces: Expert questionnaire about urban Biodiversity Quality to support urban planning) . <em>Terra</em> 134: 207–223. https://doi.org/10.30677/terra.120163 [In Finnish with English abstract.]</li> <li>Jalkanen, J., Vierikko, K. & Moilanen, A. (2020) Spatial prioritization for urban Biodiversity Quality using biotope maps and expert opinion. <em>Urban Forestry & Urban Greening</em> 49: 126586. https://doi.org/10.1016/j.ufug.2020.126586.</li> <li>Vierikko, K., Salminen, J., Niemelä J., Jalkanen, J. & Tamminen, N. 2014: Sustainable green infrastructure of Helsinki – urban ecological research report and recommendations for the Helsinki master plan 2050. Strategic Planning Office of the City Planning Department of the City of Helsinki. Research report. [In Finnish with English abstract.] Available in: <a href="https://www.hel.fi/hel2/ksv/julkaisut/yos_2014-27.pdf">https://www.hel.fi/hel2/ksv/julkaisut/yos_2014-27.pdf</a> (Cited 16.1.2018). 132 pp.</li> </ul> <p> </p>
IoT Application Generation Module Expert Review Documents and Results
<p>The ZIP file contains all study documents that have been used to perform an expert review of the IoT Application Generation Module which has been developed as an extension to the eSPACE end-user authoring tool.</p> <p>It also contains the results of our study which has been performed with 6 participants (see Expert Review Results Data.pdf).</p>
Data for 'VespaG: Expert-guided protein language models enable accurate and blazingly fast fitness prediction'
<div>Datasets used for development of VespaG and VespaG predictions generated with <a href="https://github.com/JSchlensok/VespaG">https://github.com/JSchlensok/VespaG</a>. </div> <div> </div> <div>Uploads contain:</div> <div> <ol> <li><strong>Performance</strong> summaries for ProteinGym [1]:<br>- Spearman and Pearson correlation for VespaG: <em>proteingym_performance_vespag.csv </em>(columns: 'DMS_id', 'Spearman', 'Pearson')<br>- Spearman correlation for evaluated methods VespaG, GEMME [2], VESPA [3], TranceptEVE [4], AlphaMissense [5], PoET [6]: <em>proteingym_spearman_allmethods.csv </em>(columns: 'DMS_id', 'Trancept EVE-L', 'VESPA', 'VespaG', 'GEMME', 'AlphaMissense', 'PoET', 'UniProt_ID', 'coarse_selection_type' (function), 'taxon')</li> <li><strong>Fasta</strong> files with sequences for all train sets (<em>vespag_fasta_training_datasets.zip</em> with seq_all9k.fasta, seq_human5k.fasta, seq_droso4k.fasta, seq_ecoli2k.fasta, seq_virus1k.fasta) and test set (<em>proteingym_217.fasta</em>)</li> <li><strong>VespaG</strong> <strong>Predictions</strong> for test set: <em>vespag_proteingym_rawpreds_by_training_dataset.zip</em> with raw_preds_ecoli.csv, raw_preds_human.csv, raw_preds_virus.csv, raw_preds_all.csv, raw_preds_droso.csv (columns: 'DMS_id', 'mutation', 'DMS_score', 'VespaG'). Predictions are based on different training data, the final model VespaG was trained on a subset of the human proteome and <strong>raw VespaG predictions</strong> <strong>for</strong> <strong>the</strong> <strong>ProteinGym benchmark are in raw_preds_human.csv </strong>(used to calculate the performances above).</li> <li><strong>GEMME predictions</strong> for train sets: <em>vespag_proteingym_rawpreds_by_training_dataset.zip </em>with folders 'human', 'droso', 'ecoli', 'virus', 'all' for respective fasta file (each containing GEMME mutational landscape output files named '<em>ID' + '</em>_normPred_evolCombi.txt')</li> <li><strong>ESM-2</strong> <strong>embeddings</strong> [7] for test set (<em>proteingym_217_esm2.h5</em>)</li> </ol> </div> <div>For details on VespaG see:</div> <div> <div> <div>VespaG: Expert-guided protein Language Models enable accurate and blazingly fast fitness prediction</div> </div> <div>Celine Marquet, Julius Schlensok, Marina Abakarova, Burkhard Rost, Elodie Laine</div> <div>bioRxiv 2024.04.24.590982; doi: https://doi.org/10.1101/2024.04.24.590982</div> <div> </div> <div>For more information on data usage and generation please see <a href="https://github.com/JSchlensok/VespaG">https://github.com/JSchlensok/VespaG</a>.</div> <div> </div> <div>Abstract:</div> <div>Exhaustive experimental annotation of the effect of all known protein variants remains daunting and expensive, stressing the need for scalable effect predictions. We introduce VespaG, a blazingly fast single amino acid variant effect predictor, leveraging embeddings of protein Language Models as input to a minimal deep learning model. To overcome the sparsity of experimental training data, we created a dataset of 39 million single amino acid variants from the human proteome applying the multiple sequence alignment-based effect predictor GEMME as a pseudo standard-of-truth. Assessed against the ProteinGym Substitution Benchmark (217 multiplex assays of variant effect with 2.5 million variants), VespaG achieved a mean Spearman correlation of 0.48 +/- 0.01, matching state-of-the-art methods such as GEMME, TranceptEVE, PoET, AlphaMissense, and VESPA. VespaG reached its top-level performance several orders of magnitude faster, predicting all mutational landscapes of the human proteome in 30 minutes on a consumer laptop (12-core CPU, 16 GB RAM).</div> <div> </div> <div>[1] Notin, Pascal, et al. "ProteinGym: large-scale benchmarks for protein fitness prediction and design." <em>Advances in Neural Information Processing Systems</em> 36 (2024).<br>[2] Laine, Elodie, Yasaman Karami, and Alessandra Carbone. "GEMME: a simple and fast global epistatic model predicting mutational effects." <em>Molecular biology and evolution</em> 36.11 (2019): 2604-2619.</div> <div>[3] Marquet, Céline, et al. "Embeddings from protein language models predict conservation and variant effects." <em>Human genetics</em> 141.10 (2022): 1629-1647.</div> <div>[4] Notin, Pascal, et al. "TranceptEVE: Combining family-specific and family-agnostic models of protein sequences for improved fitness prediction." <em>bioRxiv</em> (2022): 2022-12.</div> <div>[5] Cheng, Jun, et al. "Accurate proteome-wide missense variant effect prediction with AlphaMissense." <em>Science</em> 381.6664 (2023): eadg7492.</div> <div>[6] Truong Jr, Timothy, and Tristan Bepler. "PoET: A generative model of protein families as sequences-of-sequences." <em>Advances in Neural Information Processing Systems</em> 36 (2024).</div> <div>[7] Lin, Zeming, et al. "Evolutionary-scale prediction of atomic-level protein structure with a language model." <em>Science</em>379.6637 (2023): 1123-1130.</div> </div>
Urban Agriculture and Health: Insights from Anonymized Expert Interviews on Spatial Planning in Greater Lomé, Togo
<p>Transcripts of anonymized interviews with 11 urban planning experts in Greater Lomé on the subject of urban agriculture, health and spatial planning.</p>
Figure 1 in Expert bioblitzes facilitate non-native fish tracking and interagency partnerships
Figure 1. Sampling locations for ten Fish Slam events (2012–2019). Counties shaded in green were sampled from 2012–2019; blue in 2017; purple and orange in 2019. Sampling in counties shaded in yellow is being planned for 2020.
Fig. 7 in Un approccio multitaxa ed expert based per l'individuazione delle aree prioritarie per la conservazione della biodiversità nel Verbano Cusio Ossola
Fig. 7 - Aree importanti (in verde) e aree peculiari (in rosso) per il gruppo tematico Mammiferi. I codici identificativi sono gli stessi indicati alla sezione Risultati; inoltre, MA18=praterie, MA19=boschi. / Important (green) and Peculiar (red) Areas for Mammals. Identification Codes are as reported in text (Results); besides, MA18=pastures (orange), MA19=woods (deep green).
Fig. 2 in Un approccio multitaxa ed expert based per l'individuazione delle aree prioritarie per la conservazione della biodiversità nel Verbano Cusio Ossola
Fig. 2 - Aree importanti (in verde) e aree peculiari (in rosso) per il gruppo tematico Flora e vegetazione. I codici identificativi sono gli stessi indicati alla sezione Risultati. / Important (green) and Peculiar (red) Areas for Flora and Vegetation. Identification Codes are as reported in text (Results).
Fig. 5 in Un approccio multitaxa ed expert based per l'individuazione delle aree prioritarie per la conservazione della biodiversità nel Verbano Cusio Ossola
Fig. 5 - Aree importanti (in verde) e aree peculiari (in rosso) per il gruppo tematico Anfibi e Rettili. I codici identificativi sono gli stessi indicati alla sezione Risultati. / Important (green) and Peculiar (red) Areas for Amphibians and Reptiles. Identification Codes are as reported in text (Results).
Fig. 1 in Un approccio multitaxa ed expert based per l'individuazione delle aree prioritarie per la conservazione della biodiversità nel Verbano Cusio Ossola
Fig. 1 - Sistema di Aree protette, SIC/ZSC e ZPS del VCO (in viola) con individuazione delle Aree protette vicine delle Province di Vercelli (colore rosa) e Novara (colore azzurro), Canton Vallese (CH, colore verde) e del futuro Parco Nazionale del Locarnese in Canton Ticino (CH, colore grigio). / Protected Areas, SCIs/SACs and SPAs network of the province of VCO (purple), showing nearby Protected Areas lying in Italy (provinces of Vercelli in pink and of Novara in light blue) and in Switzerland (Vallis Canton in light green and Tessin Canton in grey, showing below the boundaries of the future National Park of Locarno region).
Fig. 9 in Un approccio multitaxa ed expert based per l'individuazione delle aree prioritarie per la conservazione della biodiversità nel Verbano Cusio Ossola
Fig. 9 - Aree prioritarie per la conservazione della biodiversità del VCO, individuate dalla sovrapposizione di almeno due strati di Aree importanti e che comprendono anche le Aree peculiari parzialmente incluse o adiacenti. I codici identificativi sono riportati in Tabella 2. / Priority Areas for Biodiversity Conservation in VCO resulting from the overlay of at least 2 Important Areas' layers; partially included or adjoining Peculiar Areas were retained. Identification Codes are as reported in Table 2.
Fig. 4 in Un approccio multitaxa ed expert based per l'individuazione delle aree prioritarie per la conservazione della biodiversità nel Verbano Cusio Ossola
Fig. 4 - Aree importanti (in verde) e aree peculiari (in rosso) per il gruppo tematico cenosi acquatiche e pesci. I codici identificativi sono gli stessi indicati alla sezione Risultati. / Important (green) and Peculiar (red) Areas for Aquatic Ecosystems and Fish. Identification Codes are as reported in text (Results).
Fig. 8 in Un approccio multitaxa ed expert based per l'individuazione delle aree prioritarie per la conservazione della biodiversità nel Verbano Cusio Ossola
Fig. 8 - Rappresentazione degli strati individuati dai diversi gruppi tematici: il colore più chiaro rappresenta la presenza di un solo strato, colori progressivamente più scuri indicano la sovrapposizione crescente di strati. / Layers depicting Important and Peculiar Areas as defined by experts for all themes light colour indicates a single layer darker colours indicate overlay of 2 and more layers.
Fig. 12 in Un approccio multitaxa ed expert based per l'individuazione delle aree prioritarie per la conservazione della biodiversità nel Verbano Cusio Ossola
Fig. 12 - Grado di sovrapposizione tra il sistema di Aree protette e Siti Rete Natura 2000 e le Aree prioritarie per la conservazione della biodiversità del VCO. / Degree of overlay between Protected Areas and Natura 2000 Sites (purple) and Priority Areas for Biodiversity Conservation of VCO (green dots).
Figure 1. (a) Classical set and (b) Fuzzy set 2.3.-An Efficient Expert System Generator for Qualitative Feed-Back Loop Analysis
<p>A membership function is a curve that represents the degree of points which belong to the<br> specific fuzzy variable. Selecting the appropriate membership function plays an essential rule in<br> design of a fuzzy logic controller. The shape of membership function could be defined based on the<br> simplicity, convenience, speed and efficiency. Many different membership functions are introduced<br> in the literatures such as triangular, trapezoidal and Gaussian. The membership function which<br> represented in figure 1(b) is a trapezoidal type.</p>
Figure 2. Causal loop diagram for the problem situation-An Efficient Expert System Generator for Qualitative Feed-Back Loop Analysis
<p>The problem situation could be represented in the following form of a causal loop diagram<br> as shown in Figure 2.</p>
Expert-based literature review on RRI indicators for science education assessment: PERFORM analysis matrix
<p>The document contains the main variables and categories of analysis of the expert-based literature review conducted as part of the assessment impact developed in the PERFORM project. This literature review globally aimed to identify and characterize assessment frameworks used in the context of science learning and engagement with young people. Specifically, it examined the operationalization of: i) RRI values and process requirements, ii) transversal competences, iii) experiential aspects, and iv) cognitive aspects. In doing that assessment gaps and challenges where identified relevant to the context of PERFORM and, more broadly, to the development of science education assessments incorporating the RRI dimension. By assessment framework we refer to a set of interlinked criteria, practices and concepts providing a systematic way of data collection, analysis and interpretation to the study of science learning and engagement. The template for data collection was organised in different sections approaching the following specific review questions and sub-questions:</p> <ol> <li><em>What assessment frameworks can be identified in the selected sample?</em> <ol> <li>On which disciplines are they based?</li> <li>What is being assessed in these frameworks?</li> <li>How it is the evaluation conducted?</li> <li>What are the challenges of each approach for assessing science learning and engagement?</li> </ol> </li> <li><em>How are transversal competences, RRI and emotional factors included in these frameworks?</em> <ol> <li>How are these notions operationalised?</li> <li>What kinds of evaluation indicators are applied for data collection, if any?</li> </ol> </li> </ol>
MACE ("Multiple Alternatives-Criteria-Experts") tool
<p>This dataset contains the underlying data for the following publication: A group decision making tool for assessing climate policy risks against multiple criteria, Heliyon, https://doi.org/10.1016/j.heliyon.2018.e00588. Full details of methods used to create the dataset and provided within this publication<em>.</em></p>
Dataset: Climate experts' views on geoengineering depend on their beliefs about climate change impacts
<p>This is the dataset and corresponding do-files to reproduce the main results from the Paper "Climate experts’ views on geoengineering depend on their beliefs about climate change impacts" and from the Supplemenatary Information.</p> <p>This fith versions incorporates additional work done after journal review.</p>
Confirmation Bias in Web-Based Search: A Randomized Online Study on the Effects of Expert Information and Social Tags on Information Search and Evaluation
<p>ABSTRACT</p> <p>Background: The public typically believes psychotherapy to be more effective than pharmacotherapy for depression treatments. This is not consistent with current scientific evidence, which shows that both types of treatment are about equally effective.</p> <p>Objective: The study investigates whether this bias towards psychotherapy guides online information search and whether the bias can be reduced by explicitly providing expert information (in a blog entry) and by providing tag clouds that implicitly reveal experts’ evaluations.</p> <p>Methods: A total of 174 participants completed a fully automated Web-based study after we invited them via mailing lists. First, participants read two blog posts by experts that either challenged or supported the bias towards psychotherapy. Subsequently, participants searched for information about depression treatment in an online environment that provided more experts’ blog posts about the effectiveness of treatments based on alleged research findings. These blogs were organized in a tag cloud; both psychotherapy tags and pharmacotherapy tags were popular. We measured tag and blog post selection, efficacy ratings of the presented treatments, and participants’ treatment recommendation after information search.</p> <p>Results: Participants demonstrated a clear bias towards psychotherapy (mean 4.53, SD 1.99) compared to pharmacotherapy (mean 2.73, SD 2.41; <em>t</em><sub>173</sub>=7.67, <em>P</em><.001, <em>d</em>=0.81) when rating treatment efficacy prior to the experiment. Accordingly, participants exhibited biased information search and evaluation. This bias was significantly reduced, however, when participants were exposed to tag clouds with challenging popular tags. Participants facing popular tags challenging their bias (n=61) showed significantly less biased tag selection (<em>F</em><sub>2,168</sub>=10.61, <em>P</em><.001, partial eta squared=0.112), blog post selection (<em>F</em><sub>2,168</sub>=6.55, <em>P</em>=.002, partial eta squared=0.072), and treatment efficacy ratings (<em>F</em><sub>2,168</sub>=8.48, <em>P</em><.001, partial eta squared=0.092), compared to bias-supporting tag clouds (n=56) and balanced tag clouds (n=57). Challenging (n=93) explicit expert information as presented in blog posts, compared to supporting expert information (n=81), decreased the bias in information search with regard to blog post selection (<em>F</em><sub>1,168</sub>=4.32, <em>P</em>=.04, partial eta squared=0.025). No significant effects were found for treatment recommendation (<em>P</em>s>.33).</p> <p>Conclusions: We conclude that the psychotherapy bias is most effectively attenuated—and even eliminated—when popular tags implicitly point to blog posts that challenge the widespread view. Explicit expert information (in a blog entry) was less successful in reducing biased information search and evaluation. Since tag clouds have the potential to counter biased information processing, we recommend their insertion.</p>
Data set for the PLOS ONE paper Expecto transitio: Exploring non-experts' techno-economic expectations of the energy future
<p>Raw data file (SPSS and .cv versions) consisting of all data used for the Plos One publication.</p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.