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This dataset entails various structural material data that was used to provide additional evidence for arguments presented in publication "Deposition of Sn-Zr-Se precursor by thermal evaporation and PLD for the synthesis of SnZrSe3 thin films". Mainly data consists of: SEM, XRD, Raman, Auger and TGA raw data. Summary of results is provided in Extended_data.pdf file

NestWatch is a citizen-science project operated by the Cornell Lab of Ornithology. Since 1965, members of the public have been following a standardized protocol for observing and reporting birds' nests in the United States and Canada (and more recently, other countries). This dataset contains raw nest records submitted to NestWatch. The metadata paper associated with this dataset is critical for understanding fields and their contents. The dataset contains millions of nest check observations from > 574,000 nest attempts (as of 2023). Details about NestWatch can be found on the project website: www.nestwatch.org. The dataset is scheduled for updates annually on or about January 31. The most up-to-date data files are on the NestWatch website here: https://nestwatch.org/explore/nestwatch-open-dataset-downloads/. See documentation at "Related links" on this page for further details.

Integrierte Prioritätenkarte der „Klimaregulierung“ zur Verbesserung der „Klimaverordnung“, wie im Bericht beschrieben. Die Rasterkarte ergibt einen Wert, der sich aus der Summe der Werte der zugrunde liegenden Kriterienkarten ergibt, d. h. Wärmeinseleffekt und Kohlenstoffspeicherung. Dieser Betrag wurde erneut auf eine Punktzahl von 1 bis 5 reduziert. Die Bereiche mit einer Höchstpunktzahl von 5 zeigen die größten Dekorationsgewinne im Rahmen des Themas Klimaregulierung. Die städtischen Gebiete sind deutlich sichtbar. Für die Klimaregulierung gibt es klare Prioritäten. Die Bodenhärterung hat erhebliche Auswirkungen auf das Klima, sowohl auf lokaler als auch auf globaler Ebene. Durch die Entfärbung von Böden können wieder Kohlenstoffsenken entstehen. Diese integrierte Klimaregulierungskarte trägt zum Layout der letzten Eintrittskarte für die Haarentfernung bei (https://www.vlaanderen.be/datavindplaats/catalogus/kansenkaart-onthardingswinst) bei. Um den Härtegrad effizient und wirksam zu verringern, ist es wichtig, einen Überblick über die Standorte zu haben, die am wahrscheinlichsten zur Bodenräumung führen. Um zu ermitteln, wo die Enthaarung höchstwahrscheinlich am vielversprechendsten ist, wurde die Karte entwickelt, um die Verwitterungschancen (und den Ausgleichsrahmen) zu verbessern. Aus den „Integrated CleaningPriority Maps“ – einschließlich dieser Klima-Regulierungskarte – geht nach Parametern hervor, ob ein gepflasteter Standort erhebliche oder geringe Auswirkungen auf die Umwelt hat und somit eine hohe oder geringe Priorität bei der Freigabe hat. Weitere Informationen über die Erstellung aller Prioritäts- und Opportunitätskarten und die endgültige Kombination bis zur endgültigen Änderungskarte finden Sie im Abschlussbericht der vom Umweltministerium in Auftrag gegebenen Studie: https://researchportal.be/nl/publicatie/onthardingswinst-afwegingskader-en-kansenkaart-deel-1 Deze dataset bevat de digitale vectoriële versie van alle planelementen - van het type grondvlakken - van de algemene plannen van aanleg. Een planelement is een object waarmee de betrokken overheid het toepassingsgebied van (een deel van) de ruimtelijke optie die zij heeft genomen aan de hand van bepaalde stedenbouwkundige voorschriften, weergeeft op het grafische plan, als zij dat nodig acht. Dit object is conceptueel altijd te herleiden tot een vlak, lijn of punt, waaraan een zeker legendesymbool is toegekend. Om de koppeling te garanderen tussen een “locatie” en de “volledige set van voorschriften die gelden op die locatie”, is het nodig een plan te ontrafelen in enkele complementaire geodatalagen in vectorformaat. Zo bekomen we een eenduidige koppeling tussen voorschriftteksten en hun bijhorende planelementen. Grondvlakvoorschriften zullen in de praktijk altijd aan een vlakvormig planelement gekoppeld worden dat op zijn beurt een vlakvullend legendesymbool ontvangt, m.a.w. een dekkende symboliek. Dit soort voorschriften kan zowel een vervangend als een aanvullend karakter vertonen ten opzicht van andere voorschriften, afhankelijk van de geldende decretale marges betreffende subsidiariteit, hiërarchie van de plannen, en de overgang van de plannen van de oude wetgeving naar de nieuwe. Bovenop de grondvlakken van het plan kunnen andersoortige planelementen gelegd worden. Deze worden ingedeeld volgens geometrie: vlakken, lijnen, punten en volgens de geometrische nauwkeurigheid van de planelementen (geometrisch nauwkeurig te bepalen of bij wijze van aanduiding). De planelementen zijn in deze dataset beschikbaar voor zover ze al zijn opgeladen door het betreffende gemeentebestuur, dat eigenaar is van de data en verantwoordelijk voor de inhoud. Carte couvrant le territoire de la «Health Effecten Screening score» (GES) de la qualité de l’air local en Flandre. L’indicateur est basé sur la moyenne annuelle des concentrations de NO2 (dioxyde d’azote) (Vlaamse Milieumaatschappij 2017), modélisée avec la chaîne de modélisation RIO-IFDM-OSPM. Ce polluant a été pris comme base en raison de sa grande diversité spatiale, de son lien étroit avec les émissions locales et de ses effets sur la santé bien documentés. Les concentrations de NO2 ont ensuite été réparties en classes permettant de comprendre les effets potentiels de la pollution atmosphérique due au NO2 sur la santé, à titre de première indication de la qualité de l’air au niveau local, sur la base de la méthodologie néerlandaise «Health Effect Screening» (GES). Il va de soi que d’autres polluants (par exemple, les particules fines ou l’ozone) sont également importants dans la poursuite de l’analyse et de l’évaluation de la qualité de l’air au niveau local. Les classes de GES ont été définies de cette manière: GES 1 = 0-10 µg/m³ moyenne annuelle de NO2 = «bon»; GES 4 = 10-20 µg/m³ moyenne annuelle de NO2 = «modéré»; GES 6 = 20-30 µg/m³ moyenne annuelle de NO2 = «insuffisant», GES 7 = 30-40 µg/m³ moyenne annuelle NO2 = «nettement insuffisant», GES 8 > 40 µg/m³ moyenne annuelle NO2 = «très insuffisant». À partir de GES 6, la valeur des conseils en matière de santé recommandée par l’Agence des soins de santé est dépassée (jusqu’à 20 µg/m³ en moyenne annuelle de NO2), mais des concentrations plus faibles ont également des effets néfastes sur la santé. This dataset contains the digital vector version of all the plan elements – of the line type – of the provincial spatial implementation plans, both the final approved and those in the format. It also includes the suspensions and destruction of the Council of State. A plan element is an object by which the public authority concerned represents the scope of (part of) the spatial option it has taken on the basis of certain urban planning regulations on the graphic plan, if it considers it necessary. This object is conceptually always traceable to a plane, line or point assigned a certain legende symbol. In order to ensure the link between a ‘site’ and the ‘full set of requirements applicable at that location’, it is necessary to unravel a plan in some complementary geodata layers in vector format. This gives us a clear link between the texts of rules and their associated planning elements. Requirements linked to linear or point-shaped plan elements, unlike those linked to planes, can never cover underlying plant elements in the sense that they replace them. The plan elements are available in this dataset to the extent that they have already been loaded by the relevant provincial authority, which owns the data and is responsible for the content.

Πρόκειται για τη συλλογή των γεωμετριών των δασικών περιοχών που βρίσκονται στο έδαφος της περιφέρειας Friuli Venezia Giulia. Οι περισσότερες από αυτές τις περιοχές καταλαμβάνονται από δάση, όπως ορίζονται στον περιφερειακό νόμο για τα δάση (αριθ. 9/2007). Οι γεωμετρίες προέρχονται από δύο διαφορετικές βάσεις δεδομένων: «Τύποι Forestali 1998», που επικαιροποιήθηκαν το 2010 και «Ολοκλήρωση του ΣΓΠ των τύπων δασών Friuli Venezia Giulia» που πραγματοποιήθηκαν το 2011 και επικυρώθηκαν οριστικά το 2013. Is é an bailiúchán de geometries na limistéar foraoise atá suite i gcríoch Réigiún Friuli Venezia Giulia. Tá an chuid is mó de na limistéir sin á n-áitiú ag foraoisí mar a shainítear leis an Dlí Réigiúnach Foraoise (Uimh.9/2007). Tagann na geoiméadrachtaí ó dhá bhunachar sonraí éagsúla: “Cineálacha Forestali 1998”, a tugadh cothrom le dáta in 2010 agus “Comhlánú GIS de Chineálacha Foraoise Friuli Venezia Giulia” a rinneadh in 2011 agus a bailíochtaíodh go cinntitheach in 2013. Es la colección de las geometrías de las zonas forestales ubicadas en el territorio de la región de Friuli Venezia Giulia. La mayor parte de estas zonas están ocupadas por bosques tal como se definen en la Ley Forestal Regional (N.º9/2007). Las geometrías provienen de dos geodatabases diferentes: «Tipos Forestali 1998», actualizado en 2010 y «Completación del SIG de los tipos forestales de Friuli Venezia Giulia», llevado a cabo en 2011 y validado definitivamente en 2013. Il s’agit de la collection des géométries des zones forestières situées sur le territoire de la région du Frioul-Vénétie Giulia. La plupart de ces zones sont occupées par des forêts telles que définies par la loi forestière régionale (No.9/2007). Les géométries proviennent de deux bases de données différentes: «Types Forestali 1998», mis à jour en 2010 et «Achevée du SIG des types forestiers du Frioul Venezia Giulia» réalisée en 2011 et validée définitivement en 2013. Costituisce la raccolta delle geometrie delle aree forestali situate nel territorio della Regione Friuli Venezia Giulia. In buona parte si tratta di superfici occupate da boschi così come definiti dalla vigente legge forestale regionale (n.9/2007). Le geometrie provengono da due diversi geodatabase: "Tipi Forestali 1998", aggiornato nel 2010 e "Completamento del GIS dei Tipi forestali del Friuli Venezia Giulia" realizzato nel 2011 e validato definitivamente nel 2013. Het is de verzameling van de geometrieën van de bosgebieden gelegen op het grondgebied van de regio Friuli Venezia Giulia. De meeste van deze gebieden worden bewoond door bossen zoals gedefinieerd in de Regional Forest Law (nr.9/2007). De geometrieën zijn afkomstig van twee verschillende geodatabases: „Types Forestali 1998”, bijgewerkt in 2010 en „Voltooiing van het GIS van de bossoorten Friuli Venezia Giulia”, uitgevoerd in 2011 en definitief gevalideerd in 2013. Huwa l-ġbir tal-ġeometriji taż-żoni forestali li jinsabu fit-territorju tar-Reġjun ta’ Friuli Venezia Giulia. Il-biċċa l-kbira ta’ dawn iż-żoni huma okkupati minn foresti kif definit mil-Liġi Reġjonali dwar il-Foresti (Nru.9/2007). Il-ġeometriji ġejjin minn żewġ ġeobażijiet tad-data differenti: “Types Forestali 1998”, aġġornata fl-2010 u “Tlestija tal-GIS tat-Tipi ta’ Foresti ta’ Friuli Venezia Giulia” imwettqa fl-2011 u vvalidata b’mod definittiv fl-2013. Este colecția geometriilor zonelor forestiere situate pe teritoriul regiunii Friuli Venezia Giulia. Cele mai multe dintre aceste zone sunt ocupate de păduri, astfel cum sunt definite în Legea regională privind pădurile (nr.9/2007). Geometriile provin din două baze de date geografice diferite: „Tipuri Forestali 1998”, actualizat în 2010 și „Finalizarea GIS a tipurilor de păduri de Friuli Venezia Giulia”, efectuată în 2011 și validată definitiv în 2013. É a coleção das geometrias das áreas florestais localizadas no território da Região Friuli Venezia Giulia. A maior parte destas áreas é ocupada por florestas, tal como definidas na Lei Regional das Florestas (n.º 9/2007). As geometrias vêm de duas bases de dados geométricas diferentes: «Tipos Forestali 1998», atualizado em 2010 e «Conclusão do SIG dos Tipos Florestais de Friuli Venezia Giulia», realizado em 2011 e validado definitivamente em 2013. It is the collection of the geometries of the forest areas located in the territory of the Friuli Venezia Giulia Region. Most of these areas are occupied by forests as defined by the Regional Forest Law (No.9/2007). The geometries come from two different geodatabases: “Types Forestali 1998”, updated in 2010 and “Completion of the GIS of the Forest Types of Friuli Venezia Giulia” carried out in 2011 and validated definitively in 2013.

The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown herein as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary.

The data consists of annual measurements of standing aboveground plant biomass, annual aboveground net primary productivity and annual soil respiration between 1998 and 2012. Data were collected from seven European shrublands that were subject to the climate manipulations drought and warming. Sites were located in the United Kingdom (UK), the Netherlands (NL), Denmark ( two sites, DK-B and DK-M), Hungary (HU), Spain (SP) and Italy (IT). All field sites consisted of untreated control plots, plots where the plant canopy air is artificially warmed during night time hours, and plots where rainfall is excluded from the plots at least during the plants growing season. Standing aboveground plant biomass (grams biomass per square metre) was measured in two undisturbed areas within the plots using the pin-point method (UK, DK-M, DK-B), or along a transect (IT, SP, HU, NL). Aboveground net primary productivity was calculated from measurements of standing aboveground plant biomass estimates and litterfall measurements. Soil respiration was measured in pre-installed opaque soil collars bi-weekly, monthly, or in measurement campaigns (SP only). The datasets provided are the basis for the data analysis presented in Reinsch et al. (2017) Shrubland primary production and soil respiration diverge along European climate gradient. Scientific Reports 7:43952 https://doi.org/10.1038/srep43952 Standing biomass was measured using the non-destructive pin-point method to assess aboveground biomass. Measurements were conducted at the state of peak biomass specific for each site. Litterfall was measured annually using litterfall traps. Litter collected in the traps was dried and the weight was measured. Aboveground biomass productivity was estimated as the difference between the measured standing biomass in year x minus the standing biomass measured the previous year. Soil respiration was measured bi-weekly or monthly, or in campaigns (Spain only). It was measured on permanently installed soil collars in treatment plots. The Gaussen Index of Aridity (an index that combines information on rainfall and temperature) was calculated using mean annual precipitation, mean annual temperature. The reduction in precipitation and increase in temperature for each site was used to calculate the Gaussen Index for the climate treatments for each site. Data of standing biomass and soil respiration was provided by the site responsible. Data from all sites were collated into one data file for data analysis. A summary data set was combined with information on the Gaussen Index of Aridity Data were then exported from these Excel spreadsheet to .csv files for ingestion into the EIDC.

Although GPS coordinates for current populations are not included due to the potential threat of poaching, the climate variables for each species are provided. The records for extant gecko and skinks mainly came from the New Zealand's Department of Conervation Herpetofauna Database. After updating the taxonomy and cleaning the data to reflect the taxonomy as at 2019 of 43 geckos speceis recognised across seven genera and 61 species in genus, we then thinned the occurrence records at a 1 km resolution for all species then predicted distributions for those with > 15 records using species distribution models. The climate variables for each species were selected among annual mean temperature (bio1), maximum temperature of the warmest month (bio5), minimum temperature of the coldest month (bio6), mean temperature of driest quarter (bio9), mean temperature of wettest quarter (bio10), and precipitation of the driest quarter (bio17). To reduce multicollinearity in species distribution models for each species, we only retained climate variables with a variable inflation factor < 10. The climate variables were from the CHELSA database (https://chelsa-climate.org/), which can be freely downloaded for current and future scenarios. We also provide MCC tree files for the geckos and skinks. The phylogenetic trees have been constructed for NZ geckos by (Nielsen et al., 2011) and for NZ skinks by (Chapple et al., 2009). For geckos we used a subset of the sequences used by Nielsen et al. (2011) for four genes, two nuclear (RAG 1, PDC) and two mitochondrial (16S, ND2 along with flanking tRNA sequences). For skinks, we used sequences from Chapple et al. (2009) for one nuclear (RAG 1) and five mitochondrial (ND2, ND4, Cyt b, 12S and 16S) genes, and additional ND2 sequences for taxa not included in the original phylogeny (Chapple et al., 2011, p. 201). In total we used sequences for all recognised extant taxa (Hitchmough et al., 2016) as at 2019 except for three species of skink (O. aff. inconspicuum “Okuru”, O. robinsoni, and O. aff. inconspicuum “North Otago”) and two species of gecko (M. “Cupola” and W. “Kaikouras”) for which genetic data were not available. Aim: The primary drivers of species and population extirpations have been habitat loss, overexploitation, and invasive species, but human-mediated climate change is expected to be a major driver in future. To minimise biodiversity loss, conservation managers should identify species vulnerable to climate change and prioritise their protection. Here, we estimate climatic suitability for two speciose taxonomic groups, then use phylogenetic analyses to assess vulnerability to climate change. Location: Aotearoa New Zealand (NZ) Taxa: NZ lizards: diplodactylid geckos and eugongylinae skinks Methods: We built correlative species distribution models (SDMs) for NZ geckos and skinks to estimate climatic suitability under current climate and 2070 future-climate scenarios. We then used Bayesian phylogenetic mixed models (BPMMs) to assess vulnerability for both groups with predictor variables for life history traits (body size and activity phase) and current distribution (elevation and latitude). We explored two scenarios: an unlimited dispersal scenario, where projections track climate, and a no-dispersal scenario, where projections are restricted to areas currently identified as suitable. Results: SDMs projected vulnerability to climate change for most modelled lizards. For species’ ranges projected to decline in climatically suitable areas, average decreases were between 42–45% for geckos and 33–91% for skinks, although area did increase or remain stable for a minority of species. For the no-dispersal scenario, the average decrease for geckos was 37–52% and for skinks was 33–52%. Our BPMMs showed phylogenetic signal in climate change vulnerability for both groups, with elevation increasing vulnerability for geckos, and body size reducing vulnerability for skinks. Main conclusions: NZ lizards showed variable vulnerability to climate change, with most species’ ranges predicted to decrease. For species whose suitable climatic space is projected to disappear from within their current range, managed relocation could be considered to establish populations in regions that will be suitable under future climates.

Journal of Economic Inequality, accepted

Ekvivalentinės juodosios anglies matavimai Isproje, Italijoje. Măsurători ale carbonului negru echivalent în Ispra, Italia. Вимірювання еквівалентного чорного вуглецю в Іспрі, Італія. Измервания на еквивалентен черен въглерод в Испра, Италия. Merania ekvivalentného čierneho uhlíka v Ispre, Taliansko. Tomhais de charbón dubh coibhéiseach in Ispra na hIodáile. Metingen van equivalente zwarte koolstof in Ispra, Italië. Mediciones de carbono negro equivalente en Ispra, Italia. Measurements of equivalent black carbon in Ispra, Italy. Pomiary równoważnego czarnego węgla w Ispra we Włoszech.