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  • European Marine Science

  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Thyrring, Jakob; Wegeberg, Susse; Blicher, Martin E.; Krause-Jensen, Dorte; +6 Authors

    The data contains three supporting datasets: 1. Mid-intertidal data 2. Vertical transect data 3. GPS coordinates for all sites

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: ZENODO
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    Authors: Ausems, Anne; Kuepper, Nadja; Archuby, Diego; Braun, Christina; +15 Authors

    This data set describes the population dynamics of Wilson's Storm Petrels (Oceanites oceanicus) at King George Island (Isla 25 de Mayo, Antarctica) over a forty year period (1978 – 2020). It includes all available data on Wilson's Storm Petrels from two colonies: around the Argentinian Base Carlini (62°14′S, 58°40′W; CA, formerly called Base Jubany) and the Henryk Arctowski Polish Antarctic Station (62°09′S, 58°27′W; HA). Data on population productivity (number of nests, eggs, chicks and fledglings) was collected by regular visits to the colonies and searching for nest burrows, or monitoring of the egg or chick if found. Data on adult abundance and estimated age categories (i.e., presence of foot spots; Quillfeldt et al. (2000, doi:10.1007/s003000000167) were collected at CA by using the same size mistnet every study year in the same location within the breeding colony. Chicks were measured regularly (varying intervals depending on the study) at both CA and HA. Chick tarsus was measured using callipers (vernier or digital depending on the study year) to the nearest 0.1 mm, chick wing length was measured using wing rulers to the nearest 1 mm, and chick body mass was measured using mechanical or digital scales depending on the study year to the nearest 0.1 g. Chick growth rates were calculated based on the linear growth period following Ausems et al. (2020, doi:10.1016/j.scitotenv.2020.138768). Chick food loads (g) were recorded at CA and determined based on changes in chick body mass on consecutive days (Gladbach et al. (2009, doi:10.1007/s00300-009-0628-z); Kuepper et al. (2018, doi:10.1016/j.cbpa.2018.06.018). This study was further supported by the Erasmus+ programm and thee German Academic Exchange Service (DAAD)

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    PANGAEA
    Dataset . 2023
    Data sources: PANGAEA
    PANGAEA - Data Publisher for Earth and Environmental Science
    Collection . 2023
    License: CC BY SA
    Data sources: Datacite
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      PANGAEA
      Dataset . 2023
      Data sources: PANGAEA
      PANGAEA - Data Publisher for Earth and Environmental Science
      Collection . 2023
      License: CC BY SA
      Data sources: Datacite
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    Authors: Zhuang, Guang-Chao; Lin, Yu-Shih; Elvert, Marcus; Heuer, Verena B; +1 Authors
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    B2FIND
    Dataset . 2014
    Data sources: B2FIND
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    PANGAEA
    Dataset . 2014
    License: CC BY
    Data sources: PANGAEA
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    PANGAEA
    Dataset . 2014
    Data sources: PANGAEA
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ B2FINDarrow_drop_down
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      B2FIND
      Dataset . 2014
      Data sources: B2FIND
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      PANGAEA
      Dataset . 2014
      License: CC BY
      Data sources: PANGAEA
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      PANGAEA
      Dataset . 2014
      Data sources: PANGAEA
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Karjalainen, Olli; Luoto, Miska; Aalto, Juha; Etzelmüller, Bernd; +4 Authors

    This dataset contains spatial predictions of the potential environmental spaces for pingos, ice-wedge polygons and rock glaciers across the Northern Hemisphere permafrost areas. The potential environmental spaces, i.e. conditions where climate, topography and soil properties are suitable for landform presence, were predicted with statistical ensemble modelling employing geospatial data on environmental conditions at 30 arc-second resolution (~1 km). In addition to the baseline period (1950-2000), the predictions are provided for 2041-2060 and 2061-2080 using climate-forcing scenarios (Representative Concentration Pathways 4.5 and 8.5). The resulting dataset consists of five spatial predictions for each landform in GeoTIFF format.The data provide new information on 1) the fine-scale spatial distribution of permafrost landforms in the Northern Hemisphere, 2) the potential future alterations in the environmental suitability for permafrost landforms due to climate change, and 3) the circumpolar distribution of various ground ice types, and can 4) facilitate efforts to inventory permafrost landforms in incompletely mapped areas.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ PANGAEA - Data Publi...arrow_drop_down
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    B2FIND
    Dataset . 2020
    Data sources: B2FIND
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    PANGAEA
    Dataset . 2020
    License: CC BY
    Data sources: PANGAEA
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      B2FIND
      Dataset . 2020
      Data sources: B2FIND
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      PANGAEA
      Dataset . 2020
      License: CC BY
      Data sources: PANGAEA
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    Misurazioni orarie SMPS per Malin Head per l'anno 2019 Clima Chimica e Climate Change Network (AC3) Stazione: Testa di Malin (MLH) Le coordinate: 55º 22′ N, 7º 20′ O ALTITUDINE: 22 m s.l.m. Altezza di misura: 5 m Tipo di sito: fondo rurale Strumentazione: PALAS U200 SMPS Gamma di dati: 8-1200 nm (l'intestazione determina l'intervallo inferiore) Unità:[1/cm³] Tempo di consegna: TERMINI E CONDIZIONI Informazioni di base: Malin Head è l'attuale stazione meteorologica sinottica Met Eireann. Numero di serie dello strumento: Informazioni di calibrazione: Calibrato ogni 2 anni Погодинний вимірювання SMPS для Малін Голова на 2019 рік Хімія атмосфери та мережа змін клімату (AC3) Станція метро: Малін Голова (MLH) Географічні координати міста: 55° 22′ N, 7° 20′ ВТ ВИСОТА ПІДЙОМУ: 22 м с. Висота вимірювання: 5 м Тип сайту: сільський фон Вимірювальні прилади: ПАЛАС U200 SMPS Діапазон даних: 8-1200 нм (заголовок визначає нижній діапазон) Одиниці:[1/см³] Час роботи: СТВОРЕННЯ UTC Довідкова інформація: Малін Хед - існуюча синоптична метеорологічна станція Met Eireann. Серійний номер приладу: Інформація про калібрування: Калібрується кожні 2 роки Stundas SMPS mērījumi Malin Head 2019. gadam Atmosfēras ķīmijas un klimata pārmaiņu tīkls (AC3) Atrašanās vieta: Malin Head (MLH) Koordinātas: 55° 22′ ZIEMEĻU PLATUMA, 7° 20′ RIETUMU GARUMA, AUGSTUMS VIRS JŪRAS LĪMEŅA: 22 m asl Mērīšanas augstums: 5 m Vietnes tips: lauku vide Instrumenti: PALAS U200 SMPS Datu diapazons: 8–1200 nm (virsma nosaka zemāko diapazonu) Vienības:[1/cm³] Laiks: UTC UTC Vispārīga informācija: Malin Head ir esošā Met Eireann sinoptiskās meteoroloģijas stacija. Instrumenta sērijas numurs: Kalibrēšanas informācija: Kalibrēts ik pēc 2 gadiem Kejl fis-siegħa tal-SMPS għal Malin Head għas-sena 2019 Netwerk dwar il-Kimika u t-Tibdil fil-Klima (AC3) Stazzjon: Ras ta’ Malin (MLH) Koordinati: 55° 22′ N, 7° 20′ W ALTITUDNI: 22 m asl L-għoli tal-kejl: 5 m Tip ta’ sit: sfond rurali Strumentazzjoni: PALAS U200 SMPS Firxa ta’ dejta: 8–1200 nm (l-intestatura tiddetermina l-firxa aktar baxxa) Unitajiet:[1/cm³] Ħin: UTC Informazzjoni ta’ sfond: Malin Head huwa l-istazzjon eżistenti tal-meteoroloġija sinottika Met Eireann. Numru tas-serje tal-istrument: Informazzjoni ta’ kalibrazzjoni: Kalibrat kull sentejn Valandiniai SMPS matavimai Malin Head 2019 metams Atmosferos chemijos ir klimato kaitos tinklas (AC3) Stotelės: Malin Head (MLH) Koordinatės: 55° 22′ ŠIAURĖS PLATUMOS, 7° 20′ VAKARŲ ILGUMOS AUKŠTIS: 22 m asl Matavimo aukštis: 5 m Svetainės tipas: kaimo fonas Instrumentai: PALAS U200 SMPS Duomenų diapazonas: 8–1200 nm (antraštė nustato žemesnį diapazoną) Vienetų skaičius:[1/cm³] Laikas: UTC Pagrindinė informacija: Malin Head yra esama Met Eireann sinoptinės meteorologijos stotis. Priemonės serijos numeris: Kalibravimo informacija: Kalibruojama kas 2 metus Mediciones por hora de SMPS para Malin Head para el año 2019 Red de Química y Cambio Climático de la Atmósfera (AC3) De la estación: Malin Head (MLH) Coordenadas: 55.º 22′ N, 7.º 20′ O ALTURA: 22 m asl Altura de medición: 5 m Tipo de sitio: fondo rural Instrumentación: PALAS U200 SMPS Rango de datos: 8-1200 nm (la cabecera determina el rango inferior) Unidades:[1/cm³] Hora: UTC Información de antecedentes: Malin Head es la estación de meteorología sinóptica Met Eireann existente. Número de serie del instrumento: Información de calibración: Calibrado cada 2 años Medições horárias SMPS para Malin Head para o ano 2019 Rede de Química da Atmosfera e Alterações Climáticas (AC3) Estação: Cabeça de Malin (MLH) Coordenadas: 55.º 22′ N, 7.º 20′ W ALTITUDE: 22 m/sl Altura da medição: 5 m Tipo do site: contexto rural Instrumentação: PALAS U200 SMPS Gama de dados: 8-1200 nm (o cabeçalho determina o intervalo mais baixo) Unidades:[1/cm³] Hora: UTC Informações gerais: Malin Head é a estação de meteorologia sinóptica Met Eireann existente. Número de série do instrumento: Informações de calibração: Calibrado a cada 2 anos SMPS-metingen per uur voor Malin Head voor het jaar 2019 Netwerk van atmosfeerchemie en klimaatverandering (AC3) Locatie: Malin Hoofd (MLH) Coördinaten: 55° 22′ NOORDERBREEDTE, 7° 20′ WL HOOGTE: 22 m asl De hoogte van de meting: 5 m Soort site: landelijke achtergrond Instrumentatie: PALAS U200 SMPS Gegevensbereik: 8-1200 nm (header bepaalt een lager bereik) Eenheden:[1/cm³] Tijd: UTC Achtergrondinformatie: Malin Head is het bestaande synoptische meteorologisch station Met Eireann. Instrument serienummer: Kalibratie info: Om de 2 jaar gekalibreerd Почасови SMPS измервания за Malin Head за 2019 г. Мрежа за химия на атмосферата и изменение на климата (AC3) Станция: Malin Head (MLH) Координати: 55° 22′ С.Ш., 7° 20′ З.Д. ВИСОЧИНА:22 m asl Височина на измерване:5 м Тип на сайта:произход на селските райони Инструментална апаратура:PALAS U200 SMPS Обхват на данните:8—1200 nm (главата определя долен обхват) Единици:[1/cm³] Време:UTC Основна информация: Malin Head е съществуващата метеорологична станция Met Eireann. Сериен номер на инструмента: Информация за калибриране: Калибрирани на всеки 2 години Mesures horaires SMPS pour Malin Head pour l’année 2019 Réseau de chimie de l’atmosphère et du changement climatique (AC3) Station: Tête de Malin (MLH) Coordonnées: 55° 22′ N, 7° 20′ O ALTITUDE:22 m asl Hauteur de mesure:5 m Type de site:milieu rural Instrumentation:PALAS U200 SMPS Plage de données:8-1200 nm (l’en-tête détermine la plage inférieure) Unités:[1/cm³] Temps:UTC Informations générales: Malin Head est la station météorologique synoptique de Met Eireann. Numéro de série de l’instrument: Informations d’étalonnage: Calibré tous les 2 ans

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    Authors: Domagalski, Piotr; Sætran, Lars Roar;

    Herewith we present the extended 1Hz dataset of wind measurements from a Skipheia meteorological station on the island of Frøya on the western coast of Norway, Trondelag. The data binned in 10 min averages can be find at: https://doi.org/10.5281/zenodo.2557500 The site represents an exposed coastal wind climate with open sea, land and mixed fetch from various directions. UTM-coordinates of the Met-mast: 8.34251 E and 63.66638 N. See the map for details (NorwegianMapping Authority): https://www.norgeskart.no/#!?project=norgeskart&layers=1003&zoom=3&lat=7035885.49&lon=539601.41&markerLat=7077031.483032227&markerLon=170902.83203125&panel=searchOptionsPanel&sok=Titranveien Presented data were gathered between years 2009-2016. Data&hardware summary: Years 2009-2016: Mast2 equipped with 6 pairs of 2D sonic anemometers at 10, 16, 25, 40, 70, 100 m above the ground, independent temperature measurements at the same heights and near the ground; pressure and relative humidity from local meteostation (Sula, 20 km away). Years 2014-2016: Mast4 equipped with 2 pairs of 2D sonic anemometers at 40 and 100 m above the ground. The distance between the masts is 79 m. Data is binned in years and months and stored in a ‘*.txt’ tab-separated values file. Data column order is described in SkipheiaMast2_header.txt and SkipheiaMast4_header.txt, where WSx is the wind speed (m/s), WDx is the wind direction (360 deg), ATx is the air temperature (deg C) and x designates the instrument number. The instruments are numbered starting from the ground. Example: For Mast2 (6 pairs of anemometers, ground temperature + 6 temperature sensors on the mast) that means that AT0 is the ground temperature. WS1 and WS2 are wind speed records at 10 m level. WS3 and WS4 are wind speed records at 16 m. For Mast4 (2 pairs of anemometers) that means that WS1 and WS2 are wind speed records at 40 m level. WS3 and WS4 are wind speed records at 100 m. Detailed site description with wind climate description can be found in attached analysis: Site analysys.pdf. Additional information and analysis can be found in listed below works, using data from Frøya site: Bardal, L. M., & Sætran, L. R. (2016, September). Spatial correlation of atmospheric wind at scales relevant for large scale wind turbines. In Journal of Physics: Conference Series (Vol. 753, No. 3, p. 032033). IOP Publishing, doi:10.1088/1742-6596/753/3/032033, https://iopscience.iop.org/article/10.1088/1742-6596/753/3/032033/pdf Bardal, L. M., & Sætran, L. R. (2016). Wind gust factors in a coastal wind climate. Energy Procedia, 94, 417-424, https://doi.org/10.1016/j.egypro.2016.09.207 IEA Wind TCP Task 27 Compendium of IEA Wind TCP Task 27 Case Studies, Technical Report, Prepared by Ignacio Cruz Cruz, CIEMAT, Spain Trudy Forsyth, WAT, United States, October 2018; Chapter 1.8. https://community.ieawind.org/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=8afc06ec-bb68-0be8-8481-6622e9e95ae7&forceDialog=0 Domagalski, P., Bardal, L. M., & Sætran, L. Vertical Wind Profiles in Non-neutral Conditions-Comparison of Models and Measurements from Froya. Journal of Offshore Mechanics and Arctic Engineering, doi: 10.1115/1.4041816, http://offshoremechanics.asmedigitalcollection.asme.org/article.aspx?articleid=2711333&resultClick=3 Møller, M., Domagalski, P., & Sætran, L. R. (2019, October). Characteristics of abnormal vertical wind profiles at a coastal site. In Journal of Physics: Conference Series (Vol. 1356, No. 1, p. 012030). IOP Publishing. https://iopscience.iop.org/article/10.1088/1742-6596/1356/1/012030 Møller, M., Domagalski, P., and Sætran, L. R.: Comparing Abnormalities in Onshore and Offshore Vertical Wind Profiles, Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-40 , in review, 2019.

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    ZENODO
    Dataset . 2019
    Data sources: Datacite
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    ZENODO
    Dataset . 2019
    Data sources: Datacite
    ZENODO
    Dataset . 2019
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      ZENODO
      Dataset . 2019
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      ZENODO
      Dataset . 2019
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      Dataset . 2019
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    Authors: Bussmann, Ingeborg; Anselm, Norbert; Fischer, Philipp; von der Esch, Elisabeth;

    The main objective of this Sternfahrt-8, from 10th to 16th September 2021, was to assess the temporal variance of oceanographic real time data in the Elbe influence area of the German Bight (North Sea). Therefore, the participating Ships should repeat the same tracks for four days (see map). One ship (RV Uthörn) covered the western part between Cuxhaven and Heligoland, the second ship (RV Littorina) went to the northern part between Heligoland and Büsum and the third vessel (RV Ludwig Prandtl) should have covered the middle part of the study area, but due to vandalism damage it could not participate on the cruise. During the whole cruise chemical and physical data were recorded continuously along the tracks. Additionally, discrete water samples were taken on six stations along the way for further analysis in the laboratory. The latter data is not included in the present dataset, and can be accessed via https://doi.pangaea.de/10.1594/PANGAEA.963455. For more information about the MOSES campaign and the "Sternfahrten" cruises see article cited in references.

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    Authors: Schild, Laura; Kruse, Stefan; Heim, Birgit; Stieg, Amelie; +7 Authors

    Vegetation surveys were carried out in four different study areas in the Sakha Republic, Russia: in the mountainous region of the Verkhoyansk Range within the Oymyakonsky and Tomponsky District (Event EN21-201 - EN21-219), and in three lowland regions of Central Yakutia within the Churapchinsky, Tattinsky and the Megino-Kangalassky District (Event EN21220 - EN21264). The study area is located within the boreal forest biome that is underlain by permafrost soils. The aim was to record the projective ground vegetation in different boreal forest types studied during the RU-Land_2021_Yakutia summer field campaign in August and September 2021.Ground vegetation was surveyed for different vegetation types within a circular forest plot of 15m radius. Depending on the heterogeneity of the forest plot, multiple vegetation types (VA, VB, or VC) were chosen for the survey. The assignment of a vegetation type is always unique to a site. Their cover on the circular forest plot was recorded in percent.In total, 84 vegetation types at 58 forest plots were assessed. All data were collected by scientists form the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI) Germany, the University of Potsdam Germany, and the North-Easter Federal University of Yakutsk (NEFU) Russia.

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    PANGAEA
    Dataset . 2023
    Data sources: PANGAEA
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      PANGAEA
      Dataset . 2023
      Data sources: PANGAEA
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    Authors: Braun, Camrin; Arostegui, Martin; Farchadi, Nima; Alexander, Michael; +20 Authors

    Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery-dependent (conventional mark-recapture tags, fisheries observer records) and two fishery-independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage strengths of individual data types while statistically accounting for limitations, such as sampling biases.  Please see the README document ("README.md") and the accompanying published article: Braun, C. D., M. C. Arostegui, N. Farchadi, M. Alexander, P. Afonso, A. Allyn, S. J. Bograd, S. Brodie, D. P. Crear, E. F. Culhane, T. H. Curtis, E. L. Hazen, A. Kerney, N. Lezama-Ochoa, K. E. Mills, D. Pugh, N. Queiroz, J. D. Scott, G. B. Skomal, D. W. Sims, S. R. Thorrold, H. Welch, R. Young-Morse, R. Lewison. In press. Building use-inspired species distribution models: using multiple data types to examine and improve model performance. Ecological Applications. Accepted. DOI: < article DOI will be added when it is assigned >

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      Dataset . 2023
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      Dataset . 2023
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    Authors: Sánchez, Nicolás; Brüggemann, Daniel; Goldenberg, Silvan Urs;

    This data was collected as a part of a mesocosm study to investigate the ecosystem impacts of ocean alkalinity enhancement, within the EU H2020 OceanNETs project. Nine mesocosms were deployed in Taliarte Harbour (Gran Canaria, Spain) and were regularly sampled using integrated water samplers between 10th September-25th October 2021. A gradient design was used in this experiment with a total of nine different alkalinity concentrations. Seawater alkalinity ranged between ambient (0 µeq kg-1 added alkalinity, OAE0) and 2400 µeq kg-1 additional alkalinity (OAE2400). The alkalinity levels increased in equal intervals of 300 µeq kg-1 across nine mesocosms (OAE0, OAE300, OAE600, OAE900, OAE1200, OAE1500, OAE1800, OAE2100, OAE2400). This data set contains metazoan zooplankton biomass (µgC per L) from these nine mesocosms. Biomass was calculated based on zooplankton abundances transformed using carbon mass conversion factors. Metazoan zooplankton were sampled with apstein net (ø17cm, mesh size 55µm, 64.06285L) hauls taken every two days (except for days 5 and 9). Zooplankton were size fractioned and assessed in the correspondent size class (small: 55-200µm; medium: 200-500µm; large: 500µm-3mm). Within each size class, all organisms were counted and identified to the lowest possible taxonomic level, and developmental stages were differentiated where possible. Zooplankton abundances (individuals per L) converted to carbon biomass (µgC per L) using biomass conversion factors. Conversion factors are obtained from different sources (Sanchez et al. (in prep)). Briefly: i) metazoan zooplankton functional groups were sampled and measured for carbon biomass using an elemental analyser at specific points throughout the experiment, ii) individual zooplankton were photographed, measured, and their biovolumes and carbon masses derived using standard conversions cited in the literature, iii) zooplankton conversion factors from KOSMOS Gran Canaria 2019 (https://doi.pangaea.de/10.1594/PANGAEA.971765). The experiment, which lasted 33 days, was divided into four response phases (see Sánchez et al. (in prep)): i) pretreatment (days 1 to 4, treatment was implemented on day 4), ii) immediate (days 5-10), iii) shorter term (days 11-22), iv) longer term (days 23 to 33). This data set is associated to the submission by Paul et al. (in review) (https://doi.pangaea.de/10.1594/PANGAEA.966941), so we refer to this data set for basic parameters like water temperature, salinity, pH and carbonate chemistry, to avoid repetition.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Thyrring, Jakob; Wegeberg, Susse; Blicher, Martin E.; Krause-Jensen, Dorte; +6 Authors

    The data contains three supporting datasets: 1. Mid-intertidal data 2. Vertical transect data 3. GPS coordinates for all sites

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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ausems, Anne; Kuepper, Nadja; Archuby, Diego; Braun, Christina; +15 Authors

    This data set describes the population dynamics of Wilson's Storm Petrels (Oceanites oceanicus) at King George Island (Isla 25 de Mayo, Antarctica) over a forty year period (1978 – 2020). It includes all available data on Wilson's Storm Petrels from two colonies: around the Argentinian Base Carlini (62°14′S, 58°40′W; CA, formerly called Base Jubany) and the Henryk Arctowski Polish Antarctic Station (62°09′S, 58°27′W; HA). Data on population productivity (number of nests, eggs, chicks and fledglings) was collected by regular visits to the colonies and searching for nest burrows, or monitoring of the egg or chick if found. Data on adult abundance and estimated age categories (i.e., presence of foot spots; Quillfeldt et al. (2000, doi:10.1007/s003000000167) were collected at CA by using the same size mistnet every study year in the same location within the breeding colony. Chicks were measured regularly (varying intervals depending on the study) at both CA and HA. Chick tarsus was measured using callipers (vernier or digital depending on the study year) to the nearest 0.1 mm, chick wing length was measured using wing rulers to the nearest 1 mm, and chick body mass was measured using mechanical or digital scales depending on the study year to the nearest 0.1 g. Chick growth rates were calculated based on the linear growth period following Ausems et al. (2020, doi:10.1016/j.scitotenv.2020.138768). Chick food loads (g) were recorded at CA and determined based on changes in chick body mass on consecutive days (Gladbach et al. (2009, doi:10.1007/s00300-009-0628-z); Kuepper et al. (2018, doi:10.1016/j.cbpa.2018.06.018). This study was further supported by the Erasmus+ programm and thee German Academic Exchange Service (DAAD)

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    PANGAEA
    Dataset . 2023
    Data sources: PANGAEA
    PANGAEA - Data Publisher for Earth and Environmental Science
    Collection . 2023
    License: CC BY SA
    Data sources: Datacite
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      PANGAEA
      Dataset . 2023
      Data sources: PANGAEA
      PANGAEA - Data Publisher for Earth and Environmental Science
      Collection . 2023
      License: CC BY SA
      Data sources: Datacite
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    Authors: Zhuang, Guang-Chao; Lin, Yu-Shih; Elvert, Marcus; Heuer, Verena B; +1 Authors
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    B2FIND
    Dataset . 2014
    Data sources: B2FIND
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    PANGAEA
    Dataset . 2014
    License: CC BY
    Data sources: PANGAEA
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    PANGAEA
    Dataset . 2014
    Data sources: PANGAEA
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      B2FIND
      Dataset . 2014
      Data sources: B2FIND
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      PANGAEA
      Dataset . 2014
      License: CC BY
      Data sources: PANGAEA
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      PANGAEA
      Dataset . 2014
      Data sources: PANGAEA
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Karjalainen, Olli; Luoto, Miska; Aalto, Juha; Etzelmüller, Bernd; +4 Authors

    This dataset contains spatial predictions of the potential environmental spaces for pingos, ice-wedge polygons and rock glaciers across the Northern Hemisphere permafrost areas. The potential environmental spaces, i.e. conditions where climate, topography and soil properties are suitable for landform presence, were predicted with statistical ensemble modelling employing geospatial data on environmental conditions at 30 arc-second resolution (~1 km). In addition to the baseline period (1950-2000), the predictions are provided for 2041-2060 and 2061-2080 using climate-forcing scenarios (Representative Concentration Pathways 4.5 and 8.5). The resulting dataset consists of five spatial predictions for each landform in GeoTIFF format.The data provide new information on 1) the fine-scale spatial distribution of permafrost landforms in the Northern Hemisphere, 2) the potential future alterations in the environmental suitability for permafrost landforms due to climate change, and 3) the circumpolar distribution of various ground ice types, and can 4) facilitate efforts to inventory permafrost landforms in incompletely mapped areas.

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    B2FIND
    Dataset . 2020
    Data sources: B2FIND
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    PANGAEA
    Dataset . 2020
    License: CC BY
    Data sources: PANGAEA
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      B2FIND
      Dataset . 2020
      Data sources: B2FIND
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      PANGAEA
      Dataset . 2020
      License: CC BY
      Data sources: PANGAEA
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    Misurazioni orarie SMPS per Malin Head per l'anno 2019 Clima Chimica e Climate Change Network (AC3) Stazione: Testa di Malin (MLH) Le coordinate: 55º 22′ N, 7º 20′ O ALTITUDINE: 22 m s.l.m. Altezza di misura: 5 m Tipo di sito: fondo rurale Strumentazione: PALAS U200 SMPS Gamma di dati: 8-1200 nm (l'intestazione determina l'intervallo inferiore) Unità:[1/cm³] Tempo di consegna: TERMINI E CONDIZIONI Informazioni di base: Malin Head è l'attuale stazione meteorologica sinottica Met Eireann. Numero di serie dello strumento: Informazioni di calibrazione: Calibrato ogni 2 anni Погодинний вимірювання SMPS для Малін Голова на 2019 рік Хімія атмосфери та мережа змін клімату (AC3) Станція метро: Малін Голова (MLH) Географічні координати міста: 55° 22′ N, 7° 20′ ВТ ВИСОТА ПІДЙОМУ: 22 м с. Висота вимірювання: 5 м Тип сайту: сільський фон Вимірювальні прилади: ПАЛАС U200 SMPS Діапазон даних: 8-1200 нм (заголовок визначає нижній діапазон) Одиниці:[1/см³] Час роботи: СТВОРЕННЯ UTC Довідкова інформація: Малін Хед - існуюча синоптична метеорологічна станція Met Eireann. Серійний номер приладу: Інформація про калібрування: Калібрується кожні 2 роки Stundas SMPS mērījumi Malin Head 2019. gadam Atmosfēras ķīmijas un klimata pārmaiņu tīkls (AC3) Atrašanās vieta: Malin Head (MLH) Koordinātas: 55° 22′ ZIEMEĻU PLATUMA, 7° 20′ RIETUMU GARUMA, AUGSTUMS VIRS JŪRAS LĪMEŅA: 22 m asl Mērīšanas augstums: 5 m Vietnes tips: lauku vide Instrumenti: PALAS U200 SMPS Datu diapazons: 8–1200 nm (virsma nosaka zemāko diapazonu) Vienības:[1/cm³] Laiks: UTC UTC Vispārīga informācija: Malin Head ir esošā Met Eireann sinoptiskās meteoroloģijas stacija. Instrumenta sērijas numurs: Kalibrēšanas informācija: Kalibrēts ik pēc 2 gadiem Kejl fis-siegħa tal-SMPS għal Malin Head għas-sena 2019 Netwerk dwar il-Kimika u t-Tibdil fil-Klima (AC3) Stazzjon: Ras ta’ Malin (MLH) Koordinati: 55° 22′ N, 7° 20′ W ALTITUDNI: 22 m asl L-għoli tal-kejl: 5 m Tip ta’ sit: sfond rurali Strumentazzjoni: PALAS U200 SMPS Firxa ta’ dejta: 8–1200 nm (l-intestatura tiddetermina l-firxa aktar baxxa) Unitajiet:[1/cm³] Ħin: UTC Informazzjoni ta’ sfond: Malin Head huwa l-istazzjon eżistenti tal-meteoroloġija sinottika Met Eireann. Numru tas-serje tal-istrument: Informazzjoni ta’ kalibrazzjoni: Kalibrat kull sentejn Valandiniai SMPS matavimai Malin Head 2019 metams Atmosferos chemijos ir klimato kaitos tinklas (AC3) Stotelės: Malin Head (MLH) Koordinatės: 55° 22′ ŠIAURĖS PLATUMOS, 7° 20′ VAKARŲ ILGUMOS AUKŠTIS: 22 m asl Matavimo aukštis: 5 m Svetainės tipas: kaimo fonas Instrumentai: PALAS U200 SMPS Duomenų diapazonas: 8–1200 nm (antraštė nustato žemesnį diapazoną) Vienetų skaičius:[1/cm³] Laikas: UTC Pagrindinė informacija: Malin Head yra esama Met Eireann sinoptinės meteorologijos stotis. Priemonės serijos numeris: Kalibravimo informacija: Kalibruojama kas 2 metus Mediciones por hora de SMPS para Malin Head para el año 2019 Red de Química y Cambio Climático de la Atmósfera (AC3) De la estación: Malin Head (MLH) Coordenadas: 55.º 22′ N, 7.º 20′ O ALTURA: 22 m asl Altura de medición: 5 m Tipo de sitio: fondo rural Instrumentación: PALAS U200 SMPS Rango de datos: 8-1200 nm (la cabecera determina el rango inferior) Unidades:[1/cm³] Hora: UTC Información de antecedentes: Malin Head es la estación de meteorología sinóptica Met Eireann existente. Número de serie del instrumento: Información de calibración: Calibrado cada 2 años Medições horárias SMPS para Malin Head para o ano 2019 Rede de Química da Atmosfera e Alterações Climáticas (AC3) Estação: Cabeça de Malin (MLH) Coordenadas: 55.º 22′ N, 7.º 20′ W ALTITUDE: 22 m/sl Altura da medição: 5 m Tipo do site: contexto rural Instrumentação: PALAS U200 SMPS Gama de dados: 8-1200 nm (o cabeçalho determina o intervalo mais baixo) Unidades:[1/cm³] Hora: UTC Informações gerais: Malin Head é a estação de meteorologia sinóptica Met Eireann existente. Número de série do instrumento: Informações de calibração: Calibrado a cada 2 anos SMPS-metingen per uur voor Malin Head voor het jaar 2019 Netwerk van atmosfeerchemie en klimaatverandering (AC3) Locatie: Malin Hoofd (MLH) Coördinaten: 55° 22′ NOORDERBREEDTE, 7° 20′ WL HOOGTE: 22 m asl De hoogte van de meting: 5 m Soort site: landelijke achtergrond Instrumentatie: PALAS U200 SMPS Gegevensbereik: 8-1200 nm (header bepaalt een lager bereik) Eenheden:[1/cm³] Tijd: UTC Achtergrondinformatie: Malin Head is het bestaande synoptische meteorologisch station Met Eireann. Instrument serienummer: Kalibratie info: Om de 2 jaar gekalibreerd Почасови SMPS измервания за Malin Head за 2019 г. Мрежа за химия на атмосферата и изменение на климата (AC3) Станция: Malin Head (MLH) Координати: 55° 22′ С.Ш., 7° 20′ З.Д. ВИСОЧИНА:22 m asl Височина на измерване:5 м Тип на сайта:произход на селските райони Инструментална апаратура:PALAS U200 SMPS Обхват на данните:8—1200 nm (главата определя долен обхват) Единици:[1/cm³] Време:UTC Основна информация: Malin Head е съществуващата метеорологична станция Met Eireann. Сериен номер на инструмента: Информация за калибриране: Калибрирани на всеки 2 години Mesures horaires SMPS pour Malin Head pour l’année 2019 Réseau de chimie de l’atmosphère et du changement climatique (AC3) Station: Tête de Malin (MLH) Coordonnées: 55° 22′ N, 7° 20′ O ALTITUDE:22 m asl Hauteur de mesure:5 m Type de site:milieu rural Instrumentation:PALAS U200 SMPS Plage de données:8-1200 nm (l’en-tête détermine la plage inférieure) Unités:[1/cm³] Temps:UTC Informations générales: Malin Head est la station météorologique synoptique de Met Eireann. Numéro de série de l’instrument: Informations d’étalonnage: Calibré tous les 2 ans

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    Authors: Domagalski, Piotr; Sætran, Lars Roar;

    Herewith we present the extended 1Hz dataset of wind measurements from a Skipheia meteorological station on the island of Frøya on the western coast of Norway, Trondelag. The data binned in 10 min averages can be find at: https://doi.org/10.5281/zenodo.2557500 The site represents an exposed coastal wind climate with open sea, land and mixed fetch from various directions. UTM-coordinates of the Met-mast: 8.34251 E and 63.66638 N. See the map for details (NorwegianMapping Authority): https://www.norgeskart.no/#!?project=norgeskart&layers=1003&zoom=3&lat=7035885.49&lon=539601.41&markerLat=7077031.483032227&markerLon=170902.83203125&panel=searchOptionsPanel&sok=Titranveien Presented data were gathered between years 2009-2016. Data&hardware summary: Years 2009-2016: Mast2 equipped with 6 pairs of 2D sonic anemometers at 10, 16, 25, 40, 70, 100 m above the ground, independent temperature measurements at the same heights and near the ground; pressure and relative humidity from local meteostation (Sula, 20 km away). Years 2014-2016: Mast4 equipped with 2 pairs of 2D sonic anemometers at 40 and 100 m above the ground. The distance between the masts is 79 m. Data is binned in years and months and stored in a ‘*.txt’ tab-separated values file. Data column order is described in SkipheiaMast2_header.txt and SkipheiaMast4_header.txt, where WSx is the wind speed (m/s), WDx is the wind direction (360 deg), ATx is the air temperature (deg C) and x designates the instrument number. The instruments are numbered starting from the ground. Example: For Mast2 (6 pairs of anemometers, ground temperature + 6 temperature sensors on the mast) that means that AT0 is the ground temperature. WS1 and WS2 are wind speed records at 10 m level. WS3 and WS4 are wind speed records at 16 m. For Mast4 (2 pairs of anemometers) that means that WS1 and WS2 are wind speed records at 40 m level. WS3 and WS4 are wind speed records at 100 m. Detailed site description with wind climate description can be found in attached analysis: Site analysys.pdf. Additional information and analysis can be found in listed below works, using data from Frøya site: Bardal, L. M., & Sætran, L. R. (2016, September). Spatial correlation of atmospheric wind at scales relevant for large scale wind turbines. In Journal of Physics: Conference Series (Vol. 753, No. 3, p. 032033). IOP Publishing, doi:10.1088/1742-6596/753/3/032033, https://iopscience.iop.org/article/10.1088/1742-6596/753/3/032033/pdf Bardal, L. M., & Sætran, L. R. (2016). Wind gust factors in a coastal wind climate. Energy Procedia, 94, 417-424, https://doi.org/10.1016/j.egypro.2016.09.207 IEA Wind TCP Task 27 Compendium of IEA Wind TCP Task 27 Case Studies, Technical Report, Prepared by Ignacio Cruz Cruz, CIEMAT, Spain Trudy Forsyth, WAT, United States, October 2018; Chapter 1.8. https://community.ieawind.org/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=8afc06ec-bb68-0be8-8481-6622e9e95ae7&forceDialog=0 Domagalski, P., Bardal, L. M., & Sætran, L. Vertical Wind Profiles in Non-neutral Conditions-Comparison of Models and Measurements from Froya. Journal of Offshore Mechanics and Arctic Engineering, doi: 10.1115/1.4041816, http://offshoremechanics.asmedigitalcollection.asme.org/article.aspx?articleid=2711333&resultClick=3 Møller, M., Domagalski, P., & Sætran, L. R. (2019, October). Characteristics of abnormal vertical wind profiles at a coastal site. In Journal of Physics: Conference Series (Vol. 1356, No. 1, p. 012030). IOP Publishing. https://iopscience.iop.org/article/10.1088/1742-6596/1356/1/012030 Møller, M., Domagalski, P., and Sætran, L. R.: Comparing Abnormalities in Onshore and Offshore Vertical Wind Profiles, Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-40 , in review, 2019.

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    ZENODO
    Dataset . 2019
    Data sources: Datacite
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    ZENODO
    Dataset . 2019
    Data sources: Datacite
    ZENODO
    Dataset . 2019
    Data sources: ZENODO
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      ZENODO
      Dataset . 2019
      Data sources: Datacite
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      ZENODO
      Dataset . 2019
      Data sources: Datacite
      ZENODO
      Dataset . 2019
      Data sources: ZENODO
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    Authors: Bussmann, Ingeborg; Anselm, Norbert; Fischer, Philipp; von der Esch, Elisabeth;

    The main objective of this Sternfahrt-8, from 10th to 16th September 2021, was to assess the temporal variance of oceanographic real time data in the Elbe influence area of the German Bight (North Sea). Therefore, the participating Ships should repeat the same tracks for four days (see map). One ship (RV Uthörn) covered the western part between Cuxhaven and Heligoland, the second ship (RV Littorina) went to the northern part between Heligoland and Büsum and the third vessel (RV Ludwig Prandtl) should have covered the middle part of the study area, but due to vandalism damage it could not participate on the cruise. During the whole cruise chemical and physical data were recorded continuously along the tracks. Additionally, discrete water samples were taken on six stations along the way for further analysis in the laboratory. The latter data is not included in the present dataset, and can be accessed via https://doi.pangaea.de/10.1594/PANGAEA.963455. For more information about the MOSES campaign and the "Sternfahrten" cruises see article cited in references.

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    Authors: Schild, Laura; Kruse, Stefan; Heim, Birgit; Stieg, Amelie; +7 Authors

    Vegetation surveys were carried out in four different study areas in the Sakha Republic, Russia: in the mountainous region of the Verkhoyansk Range within the Oymyakonsky and Tomponsky District (Event EN21-201 - EN21-219), and in three lowland regions of Central Yakutia within the Churapchinsky, Tattinsky and the Megino-Kangalassky District (Event EN21220 - EN21264). The study area is located within the boreal forest biome that is underlain by permafrost soils. The aim was to record the projective ground vegetation in different boreal forest types studied during the RU-Land_2021_Yakutia summer field campaign in August and September 2021.Ground vegetation was surveyed for different vegetation types within a circular forest plot of 15m radius. Depending on the heterogeneity of the forest plot, multiple vegetation types (VA, VB, or VC) were chosen for the survey. The assignment of a vegetation type is always unique to a site. Their cover on the circular forest plot was recorded in percent.In total, 84 vegetation types at 58 forest plots were assessed. All data were collected by scientists form the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI) Germany, the University of Potsdam Germany, and the North-Easter Federal University of Yakutsk (NEFU) Russia.

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    PANGAEA
    Dataset . 2023
    Data sources: PANGAEA
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      PANGAEA
      Dataset . 2023
      Data sources: PANGAEA
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    Authors: Braun, Camrin; Arostegui, Martin; Farchadi, Nima; Alexander, Michael; +20 Authors

    Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery-dependent (conventional mark-recapture tags, fisheries observer records) and two fishery-independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage strengths of individual data types while statistically accounting for limitations, such as sampling biases.  Please see the README document ("README.md") and the accompanying published article: Braun, C. D., M. C. Arostegui, N. Farchadi, M. Alexander, P. Afonso, A. Allyn, S. J. Bograd, S. Brodie, D. P. Crear, E. F. Culhane, T. H. Curtis, E. L. Hazen, A. Kerney, N. Lezama-Ochoa, K. E. Mills, D. Pugh, N. Queiroz, J. D. Scott, G. B. Skomal, D. W. Sims, S. R. Thorrold, H. Welch, R. Young-Morse, R. Lewison. In press. Building use-inspired species distribution models: using multiple data types to examine and improve model performance. Ecological Applications. Accepted. DOI: < article DOI will be added when it is assigned >

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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    Authors: Sánchez, Nicolás; Brüggemann, Daniel; Goldenberg, Silvan Urs;

    This data was collected as a part of a mesocosm study to investigate the ecosystem impacts of ocean alkalinity enhancement, within the EU H2020 OceanNETs project. Nine mesocosms were deployed in Taliarte Harbour (Gran Canaria, Spain) and were regularly sampled using integrated water samplers between 10th September-25th October 2021. A gradient design was used in this experiment with a total of nine different alkalinity concentrations. Seawater alkalinity ranged between ambient (0 µeq kg-1 added alkalinity, OAE0) and 2400 µeq kg-1 additional alkalinity (OAE2400). The alkalinity levels increased in equal intervals of 300 µeq kg-1 across nine mesocosms (OAE0, OAE300, OAE600, OAE900, OAE1200, OAE1500, OAE1800, OAE2100, OAE2400). This data set contains metazoan zooplankton biomass (µgC per L) from these nine mesocosms. Biomass was calculated based on zooplankton abundances transformed using carbon mass conversion factors. Metazoan zooplankton were sampled with apstein net (ø17cm, mesh size 55µm, 64.06285L) hauls taken every two days (except for days 5 and 9). Zooplankton were size fractioned and assessed in the correspondent size class (small: 55-200µm; medium: 200-500µm; large: 500µm-3mm). Within each size class, all organisms were counted and identified to the lowest possible taxonomic level, and developmental stages were differentiated where possible. Zooplankton abundances (individuals per L) converted to carbon biomass (µgC per L) using biomass conversion factors. Conversion factors are obtained from different sources (Sanchez et al. (in prep)). Briefly: i) metazoan zooplankton functional groups were sampled and measured for carbon biomass using an elemental analyser at specific points throughout the experiment, ii) individual zooplankton were photographed, measured, and their biovolumes and carbon masses derived using standard conversions cited in the literature, iii) zooplankton conversion factors from KOSMOS Gran Canaria 2019 (https://doi.pangaea.de/10.1594/PANGAEA.971765). The experiment, which lasted 33 days, was divided into four response phases (see Sánchez et al. (in prep)): i) pretreatment (days 1 to 4, treatment was implemented on day 4), ii) immediate (days 5-10), iii) shorter term (days 11-22), iv) longer term (days 23 to 33). This data set is associated to the submission by Paul et al. (in review) (https://doi.pangaea.de/10.1594/PANGAEA.966941), so we refer to this data set for basic parameters like water temperature, salinity, pH and carbonate chemistry, to avoid repetition.

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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ PANGAEA - Data Publi...arrow_drop_down
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