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13,117 results for “Maps”
Reference data set used to validate the hybrid cropland map at 500m (Fritz, S. 2024)
<p>This is a reference data set for validation of the hybrid cropland map at 500m resolution for the year 2019 (Fritz, 2024, map <a title="Hybrid cropland map (GLAD/WorldCereal)" href="../doi/10.5281/zenodo.10818823" target="_blank" rel="noopener">available here</a>)</p> <p>Sampling design: random whithin areas of improvement, where the WorldCereal map is performing better (less errors) than the GLAD cropland map 2019. </p> <p>Number of sample sites: 500</p> <p>Method of data collection: visual interpreation of various sources of information, including very high resolution images and photos. </p> <p><br>Tool for data collection: <a href="https://www.geo-wiki.org" target="_blank" rel="noopener">Geo-Wiki</a></p>
Full Tree QSMs: LIDAR Driven Stemflow Mapping
<p>Quantitative structural models (QSM) of two trees derived using terrestrial lidar scanning in the winter (leafless season) at Secrest Arboretum, Wooster OH, USA (40°46'41.9"N 81°55'06.2"W, 311 m a.s.l.). Trimmed point clouds were imported into CompuTree 5.0 (http://computree.onf.fr/) and analyzed using the SimpleForest plugin (https://gitlab.com/SimpleForest/computree) in order to produce the attached files.</p> <p>File 32-06 represents a specimen of Celtis occidentalis L. (common hackberry) and 27-05 a specimen of Ulmus americana L. (American elm). </p>
Testing Data: LIDAR Driven Stemflow Mapping
<p>Data in this upload was and continues to be used for the validation of the model discussed in the paper with the following doi: <a href="http://dx.doi.org/10.2139/ssrn.4600550" target="_blank" rel="noopener">10.2139/ssrn.4600550</a>. These files are sub sections of QSMs generated using SimpleTree and constitue use cases that 1. cover then necessary functionality of the model and 2. assist in the discovery and identification of errors when the model is changed.<br><br>The git repository at the below link leverages GitHub Actions CI/CD to run a suite of 65 tests whenever a pull request is made to the main branch. Model outputs (given these files as inputs) were hand validated and are used to ensure consistent/expected model results. These outputs are stored in the below repository under 'canopyHydrodynamics/test/expected_results'.</p> <p>Repository: https://github.com/wischmcj/canopyHydrodynamics</p>
Map 1 in Lachnaia (Lachnaia) variolosa (Linnaeus, 1767) (Coleoptera, Chrysomelidae), new species for Portugal.
Map 1. - Iberian distribution of Lachnaia (Lachnaia) variolosa (Linnaeus, 1767), with the previously known Spanish provinces (in grey), the new Portuguese district (in blue) and the UTM 10x10 km square of the collecting site (in orange).
Map 1 in Nemausus sordidatus (Stṏl, 1858) (Hemiptera: Alydidae) in Portugal
Map 1.- Iberian distribution of Nemausus sordidatus (Stṏl, 1858), with the previously known Spanish provinces and Portuguese districts in grey, the previously known Portuguese MGRS 100 km2 squares in blue, and the new Portuguese MGRS 100 km2 square in orange.
Figure 1. Heat map indicating the cumulative percent seed shatter across the participating states for a in Seed-shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 1: Broadleaf species
Figure 1. Heat map indicating the cumulative percent seed shatter across the participating states for a window starting from soybean physiological maturity to 4 wk past maturity in 2016 and 2017. States were included in these maps only if they conducted sampling during the week indicated (e.g., In 2017, Arkansas sampled on October 2, October 18, and November 3, none of which are within ±3 d of the October 10 maturity date or maturity þ2 wk on October 24 in the state that year. Hence only data from maturity þ3 wk are for Arkansas for 2017.)
Gene expression QTL mapping in stimulated iPSC-derived macrophages provides insights into common complex diseases.
<p>Many disease-associated variants are thought to be regulatory but are not present in existing catalogues of expression quantitative trait loci (eQTL). We hypothesise that these variants may regulate expression in specific biological contexts, such as stimulated immune cells. Here, we used human iPSC-derived macrophages to map eQTLs across 24 cellular conditions. We found that 76% of eQTLs detected in at least one stimulated condition were also found in naive cells. The percentage of response eQTLs (reQTLs) varied widely across conditions (3.7% - 28.4%), with reQTLs specific to a single condition being rare (1.11%). Despite their relative rarity, reQTLs were overrepresented (p=0.05, Fisher's exact test) among disease-colocalizing eQTLs. We nominated an additional 21.7% of disease effector genes at GWAS loci via colocalization of reQTLs, with 38.6% of these not found in the Genotype–Tissue Expression (GTEx) catalogue. Our study highlights the diversity of genetic effects on expression and demonstrates how condition-specific regulatory variation can enhance our understanding of common disease risk alleles.</p>
FCH and FS Datasets for the paper "Integrating Multi-Source Remote Sensing Data for Mapping Boreal Forest Canopy Height and Species in interior Alaska in Support of Radar Modeling"
<p>This dataset provides forest canopy height and forest species in Delta Junction, interior Alaska in 2017. This dataset was produced based on the multi-source remote sensing datasets (AirMOSS, UAVSAR, Sentinel-1, Sentinel-2, topography), using a XGBoost approach.</p>
DiffModeler: Large Macromolecular Structure Modeling in Low-Resolution Cryo-EM Maps Using Diffusion Model
<p>Here, we store the modeled structures generated by DiffModeler for its 4 benchmark datasets: CryoREAD dataset(0-5A resolution, protein-DNA/RNA complex), ModelAngelo dataset(0-5A resolution, most protein complexes, a few protein-RNA complex), intermediate resolution dataset (5-10A resolution, protein complex), low resolution dataset (10-20A resolution, protein complex). For all protein-DNA/RNA complex, the map will be modeled by CryoREAD+DiffModeler.</p> <p>For each dataset, we keep the modeled structures by DiffModeler, named as [EMD-ID]_DiffModeler.cif; and their corressponding native structures from RCSB are saved as [EMD-ID]_[PDB_ID]_native.cif.</p> <p>For CryoREAD dataset, it includes 61 targets. For ModelAngelo dataset, it includes 28 targets. For intermediate resolution dataset , it includes 71 targets. For low resolution dataset, it incldues 6 targets.</p> <p>For intermediate resolution dataset, many maps were run with inaccurate AF2 predicted single-chain structures. We also benchmarked DiffModeler's performance by using native single-chain structures as input. They are saved under "dataset_5_10A_nativechain" folder.</p> <p>Additionally, we have stored the traced backbone map of the intermediate resolution dataset in the "dataset_5_10A_diffusion_traced_backbone_map" folder. The traced maps are saved as [EMD-ID]_diffusion.mrc. The intermediate reverse diffusion maps of the intermediate resolution dataset are saved in the "dataset_5_10A_reverse_diffusion_maps" folder. Each sub-folder is named according to the corresponding map's [EMD-ID] and contains three intermediate reverse diffusion maps: 20percentile_reverse_diffusion.mrc, 50percentile_reverse_diffusion.mrc, and 80percentile_reverse_diffusion.mrc. A higher percentile indicates a map closer to the end of the reverse diffusion steps.</p> <p>If you used DiffModeler, please cite: "Wang, Xiao, Han Zhu, Genki Terashi, Manav Taluja, and Daisuke Kihara. "DiffModeler: Large Macromolecular Structure Modeling in Low-Resolution Cryo-EM Maps Using Diffusion Model." bioRxiv (2024): 2024-01.".</p> <p>If you used CryoREAD, please cite: "Xiao Wang, Genki Terashi & Daisuke Kihara. De novo structure modeling for nucleic acids in cryo-EM maps using deep learning. Nature Methods, 2023."</p>
Fig. 1. Geological map and the abandoned Ogrodzieniec quarry. A in Morphological variations and geographic distribution of the rare Middle Jurassic ammonite Oecoptychius refractus
Fig. 1. Geological map and the abandoned Ogrodzieniec quarry. A. Map showing the studied localities in Poland, western France, and Kachchh (western India). B. Map of France showing the position of St.-Laon and Montreuil-Bellay. C. Map of India showing the position of Kachchh. D. Map of Poland showing the the position of Ogrodzieniec. E. Detailed location of Ogrodzieniec quarry (modified after Jain and Mazur 2021). F. Geological map of the Polish Jura Chain showing the location of the studied Ogrodzieniec quarry. G. Aerial view of the Ogrodzieniec quarry; arrow marks the location of the studied section. H. Exposure of the studied section.
Fig. 1. Locality maps. A in Early-middle Cambrian stratigraphy and faunas from northern Siberia
Fig. 1. Locality maps. A. Map of the Siberian Platform. B. Khorbusuonka River. Localities referred to in the main text: 96-1, 96-2, 96-3, Malaya Kuonamka (MK) and 96-6, 96-7, 96-8, Bol'shaya Kuonamka (BK) rivers, eastern flank of the Anabar Uplift; 3, 11, 15, 19, 20, Khorbusuonka River, Olenyok Uplift; 21, 22, Lena River lower reaches, Ulakhan-Ald'yarkhay Brook; Le, Lena River middle reaches, Tiktirikteekh and Bydyangaya Brooks; Al, Aldan River, between sections "Dvortsy" and UlakhanSulugur. Margin of the Siberian Platform is shown by a dashed line in A.
Map 1 in Australian Dragonfly (Odonata) Larvae: Descriptive history and identification
Map 1. The regions of Australia referred to in text and table (from Watson et al. (1991). SWA = south-western Australia; SES = south-eastern South Australia; VIC = Victoria; TAS = Tasmania; SEN = south-eastern New South Wales; NEN = north-eastern New South Wales; SEQ = south-eastern Queensland; NEQ = north-eastern Queensland; CY = Cape York Peninsula; NNT = top end of Northern Territory; KIM = Kimberley region; NWA = north-western Australia; IN = inland New South Wales; SIQ = southern inland Queensland; NIQ = northern inland Queensland; IA = inland Australia.
Map 2 in Australian Dragonfly (Odonata) Larvae: Descriptive history and identification
Map 2. Map of eastern Australia showing relevant localities (from Watson & Theischinger (1984). NSW = New South Wales; NT = Northern Territory; QLD = Queensland; SA = South Australia; VIC = Victoria; 1 = Paluma Range; 2 = Eungella; 3 = Carnarvon Gorge; 4 = Barrington Tops; 5 = Blue Mountains; 6 = Canberra. The PalumaEungella gap (marked with +, ca. 19ºS) spans between 1 and 2.
Figure 1 in Character mapping and cladogram comparison versus the requirement of total evidence: does it matter for polychaete systematics?
Figure 1. Example of the error of cladogram comparisons. A, phylogenetic hypotheses inferred from separate sets of premises. Letters on cladogram 'nodes' indicate population-splitting events relevant to the various hypotheses of character origin/fixation within ancestral populations. The requirement of total evidence precludes such a comparison of cladogram topologies because explanations of characters 1(1)–5(1) by population-splitting events A–C (left cladogram) contradict explanations of 6(1)–8(1) by population-splitting events D–F. See text for further discussion. B, explaining observations in accordance with the requirement of total evidence, correcting the problem in 'A'.
Figure 2 in Character mapping and cladogram comparison versus the requirement of total evidence: does it matter for polychaete systematics?
Figure 2. Example of the error of character mapping. A, phylogenetic hypotheses are inferred for a set of characters. Numbers on cladogram 'nodes' indicate population-splitting events relevant to the various hypotheses of character origin/fixation within ancestral populations (not shown; cf. fig. 1). B, a different set of characters are 'mapped' onto the branches of the cladogram in 'A'. C, the 'mapped' characters in 'B' actually refer to phylogenetic hypotheses inferred separately from the hypotheses implied by the cladogram in 'A' and 'B'. D, explaining observations in accordance with the requirement of total evidence, correcting the problem in 'B' and 'C'. See text for further discussion.
Figure 4. Geographic location map for Pepsis cerberus Lucas, P in Morphometric analysis and taxonomic re-evaluation of Pepsis cerberus Lucas and P. elegans Lepeletier (Hymenoptera: Pompilidae: Pepsinae: Pepsini)
Figure 4. Geographic location map for Pepsis cerberus Lucas, P. elegans Lepeletier, and P. novitia Banks in the Nearctic Region (based on Brimley 1936; Hurd 1952; Krombein 1952; Johnston 2000; Bond and Opell 2002; Vardy 2005; Leavengood et al. 2011; Bond and Godwin 2013; Hamilton et al. 2016; Norden 2017; Godwin and Bond 2021; Durand, pers. comm.; BugGuide. net; flickr.com; iNaturalist.org; gbif.org; SCAN; and specimen records from 36 insect collections). Black lines represent range limits of potential host spider genera. Solid black line represents the geographic limit of Ummidia (Halonoproctidae) species (Godwin and Bond 2021). Dashed black line represents the geographic limit of Aphonopelma (Theraphosidae) species (Hamilton et al. 2016). Dotted black line represents geographic limit of Eucteniza (Euctenizidae) species (Bond and Godwin 2013). Dash-dotted black line represents the geographic limit of Entychides Simon (Euctenizidae) species (Bond and Opell 2002). Map is adapted from Kurczewski (2023a).
Figure 1. Heat map indicating the cumulative percent seed shatter across the participating states for a in Seed-shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 2: Grass species
Figure 1. Heat map indicating the cumulative percent seed shatter across the participating states for a window starting from soybean physiological maturity to 4 wk past physiological maturity in 2016 and 2017. States were included in these maps only if they conducted sampling during the week indicated. (e.g., In 2017, Arkansas sampled on October 2, October 18, and November 3, none of which are within ±3 d of the October 10 maturity date or maturity +2 wk on October 24 in the state that year. Hence only data from maturity +3 wk are for Arkansas for 2017.)
Fig. 6. Olonia spp., distribution maps. A in Revision of the Eurybrachidae XVIII. The Australian genus Olonia Stål, 1862: Four new species, new records and biological data (Hemiptera: Fulgoromorpha)
Fig. 6. Olonia spp., distribution maps. A. Olonia albomarginata sp. nov., O. guillaumei Constant, 2018, O. lindae sp. nov. and O. rubicunda (Walker, 1851). B. Olonia aschei sp. nov., O. jackiei sp. nov. and O. picea Kirkaldy, 1906.
Mapping the existing food systems, value chains and markets for agroecological products
<table> <tbody> <tr> <td>The food systems and markets of our focal farming systems are mapped as well as communities across the value chain, including operations from production over to food disposal after consumption, all along with the contribution of these operations to socio-economic and environmental outcomes. The datasets are the results of survey/interviews on the above topic in the CANALLS project ALLs.<span> </span></td> </tr> </tbody> </table>
Map of built-up expansion ("nedbygging") over Norway 2017-2022 version 2
<p>Version 2 of the dataset https://zenodo.org/records/10566644</p> <p>Changes from first version include:</p> <ul> <li>added crowdsourced verification labels to the dataset gathered from the interactive app (link below) explained here: https://www.nina.no/Om-NINA/Aktuelt/Nyheter/article/kartlegg-nedbygging-av-natur-selv</li> <li>added the year of change crowdsourced labels</li> <li>added the type of built-up expansion as labelled by the NRK team</li> </ul> <p>Data can be viewed interactively here: <a href="https://nina.earthengine.app/view/nedbygging">https://nina.earthengine.app/view/nedbygging</a></p> <h4>(see Norwegian description below)</h4> <h2> 1. Dataset Information</h2> <p> - Title: Map of built-up expansion over Norway 2017-2022</p> <p> - Author(s): Zander Venter (NINA), Mads Nyborg Støstad (NRK), Ruben Solvang (NRK), Anne Linn Kumano-Ensby (NRK), Su Thet Mon (NRK)</p> <p> - Contact Information: zander.venter@nina.no</p> <p> - Date of Data Generation: 06.01.2024</p> <p> - Version: 1</p> <p> - Description: This is the dataset used in the <a href="https://www.nrk.no/dokumentar/xl/nrk-avslorer_-44.000-inngrep-i-norsk-natur-pa-fem-ar-1.16573560">NRK article</a> published on 06.01.2024. The data contains polygons outlining potential “nedbygging” (hereafter translated to “built-up expansion” in English) events between 2017 and 2022 over Norway. The built-up expansion polygons were identified using a combination of Sentinel-2 satellite imagery, a fully convolutional neural network (a type of AI model) from Google called <a href="https://dynamicworld.app/">Dynamic World</a> and NINA’s time series analysis thereof. The method to create the map will be published by NINA at a later date. The original map was created by NINA, but NRK performed some post-processing which included joining some polygons which were part of the same built-up expansion event (e.g. a long road). It is important to note that the map is a result of AI and has errors in it. Therefore, users are encouraged to read the sections on data quality and usage information below. Users can refer to <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000056">Venter et al. (2024)</a> for details on the scientific best practice which the NRK journalists followed to ensure that their reported area estimates in the article were not biased. In summary, the map is wrong 18% of the time. Users should expect to find that on average 1 in 5 square meter is incorrectly identified as built-up expansion. There are also many instances of built-up expansion which will be missed in the map such as forestry road development, building of small cabins etc. </p> <h2> 2. File Details</h2> <p> - Format: Shapefile (.shp, .shx, .dbf, .prj)</p> <p> - Size: 13.27 MB</p> <h2> 3. Geospatial Information</h2> <p> - Coordinate System: EPSG:32632, UTM zone 32N</p> <p> - Spatial Resolution: 10m</p> <p> - Geographical Coverage: Norway mainland (excludes Svalbard)</p> <p> - Temporal Coverage: 2017 to 2022</p> <h2> 4. Data Content</h2> <p> - Attributes Included:</p> <p> - *id*: unique identity number for each polygon</p> <p> - *undersøkt*: whether the polygon has been investigated manually using visual interpretation of orthophotos. “ja” = “yes” and “nei” = “no”</p> <p> - *<span><span>undersøkt_source*: whether the data was collected by the NRK team or the crowdsourcing effort</span></span></p> <p><span><span> - *</span></span><span><span>kategori_1*: the type of built-up expansion labelled by the NRK team - see Google Translate for translations</span></span></p> <p><span><span> - *year*: the year in which the built-up expansion occurred as defined by the crowdsourcing volunteers</span></span></p> <p> - *ai_feil*: whether the AI model method correctly (“riktig”) or incorrectly (“feil”) identified natural habitat conversion to built-up surface. Values where *undersøkt* == “nei” are labelled as “ikke_verifisert” </p> <h2> 5. Data Quality</h2> <p> - Accuracy: As described above, the false positive rate of the map was 18% based on 500 locations used for map validation and accuracy assessment. We did not quantify a false negative rate and balanced accuracy estimates because this would have required a denser sample for manual verification. Therefore, it is likely that there are many instances of built-up expansion that our map does not capture. After the formal accuracy assessment using the 500 stratified random points, NRK verified additional polygons (total of 3875) in the dataset during their investigative journalism workflow. Although these were not collected in a systematic manner, then can still be useful for some downstream tasks such as exploring what causes the AI model to misidentify built-up expansion.</p> <p> - Validation Methods: A design-based approach was used to quantify map accuracy and estimate uncertainty around the resulting area estimate reported in the NRK article. The details of this method are reported in <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000056">Venter et al. (2024)</a>. This approach quantifies the error in the AI-derived map, and corrects for this using a stratified area estimator. Therefore, the total built-up expansion of 208 km<2> reported in the NRK article has been bias-corrected. We also quantified 95% confidence intervals around this are estimate of 9.8 km<2>. It is important to note that the validation approach was conducted on individual Sentinel-2 pixels of 10x10m and not at the polygon level. Therefore, we did not quantify the error in the precision of the polygon shape in terms of capturing the full extent of a given built-up expansion event.</p> <h2> 6. Usage Information</h2> <p> - Use Limitations: Considering the map error described above, users should proceed with caution when analysing the map to derive area statistics or overlays with other maps. As described in <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000056">Venter et al. (2024)</a>, simply adding the areas of the polygons (or “pixel counting” with maps formatted as images) without accounting for the error in the map will lead to incorrect area statistics. We recommend that users validate the map for their municipality or study area before proceeding with analysis. It is likely that the margin of error is highly variable between municipalities. For example, although we have not quantified it, we noticed many AI mistakes in mountainous regions due to snow and ice interference and therefore high-altitude municipalities might have more errors than low-altitude ones.</p> <p> </p> <h1> Norwegian description: </h1> <h2> 1. Datasettinformasjon</h2> <p> - Tittel: Kart over nedbygging over Norge 2017-2022</p> <p> - Forfatter(e): Zander Venter (NINA), Mads Nyborg Støstad (NRK), Ruben Solvang (NRK), Anne Linn Kumano-Ensby (NRK), Su Thet Mon (NRK)</p> <p> - Kontaktinformasjon: zander.venter@nina.no</p> <p> - Dato for datagenerering: 06.01.2024</p> <p> - Versjon: 1</p> <p> - Beskrivelse: Dette er datasettet som brukes i <a href="https://www.nrk.no/dokumentar/xl/nrk-avslorer_-44.000-inngrep-i-norsk-natur-pa-fem%20-ar-1.16573560">NRK-artikkelen</a> publisert 06.01.2024. Dataene inneholder polygoner som skisserer potensiell nedbygging mellom 2017 og 2022 over Norge. Nedbyggingsområdene ble identifisert ved hjelp av en kombinasjon av Sentinel-2 satellittbilder, et fullstendig konvolusjonelt nevralt nettverk (en type KI-modell) fra Google kalt <a href="https://dynamicworld.app/">Dynamic World</a> og NINAs tidsserie-analyse av dette. Metoden for å lage kartet vil bli publisert av NINA på et senere tidspunkt. Det originale kartet ble laget av NINA, men NRK utførte en del etterbehandling som inkluderte sammenføyning av noen polygoner som var en del av den samme oppbygde utvidelseshendelsen (f.eks. en lang vei). Det er viktig å merke seg at kartet er produsert ved hjelp av kunstig intelligens og inneholder feil. Derfor oppfordres brukere til å lese avsnittene om datakvalitet og bruksinformasjon nedenfor. Brukere kan referere til <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000056">Venter et al. (2024) </a>for detaljer om den vitenskapelige beste praksisen som NRK-journalistene fulgte for å sikre at deres rapporterte arealstatistikk i artikkelen er korrekt. Oppsummert er 18 % av arealet i kartet feil. Brukere bør forvente å finne at i gjennomsnitt 1 av 5 kvadratmeter er feilaktig identifisert som nedbygging. Det er også mange tilfeller av nedbygging som som ikke vil vises i kartet, som skogsveiutbygging, bygging av småhytter mm.</p> <h2> 2. Fildetaljer</h2> <p> - Format: Shapefil (.shp, .shx, .dbf, .prj)</p> <p> - Størrelse: 13,27 MB</p> <h2> 3. Geospatial informasjon</h2> <p> - Koordinatsystem: EPSG:32632, UTM-sone 32N</p> <p> - Rolig oppløsning: 10m</p> <p> - Geografisk dekning: Norges fastland (ekskluderer Svalbard)</p> <p> - Tidlig dekning: 2017 til 2022</p> <h2> 4. Datainnhold</h2> <p> - Attributter inkludert:</p> <p> - *id*: unikt identitetsnummer for hver polygon</p> <p> - *undersøkt*: om polygonet er undersøkt manuelt ved bruk av visuell tolkning av ortofoto.</p> <p> - *undersøkt_source*: om dataene er samlet inn av NRK-teamet eller crowdsourcing-innsatsen</p> <p> - *kategori_1*: typen nedbygging merket av NRK-teamet </p> <p> - *year*: året hvor nedbygging skjedde som definert av crowdsourcing</p> <p> - *ai_feil*: om AI-modellmetoden var “riktig” eller “feil”. Verdier der *undersøkt* == «nei» er merket som «ikke_verifisert»</p> <h2> 5. Datakvalitet</h2> <p> - Nøyaktighet: Som beskrevet ovenfor var andelen falske positive punkter i kartet 18 % basert på 500 steder (prøveflater) brukt for kartvalidering og nøyaktighetsvurdering. Vi kvantifiserte ikke andelen falske negative punkter og balanserte nøyaktighetsestimater, fordi dette ville ha krevd en tettere stikkprøvedensitet for manuell verifisering. Derfor er det sannsynlig at det er mange tilfeller av nedbygging som kartet vårt ikke fanger opp. Etter den formelle nøyaktighetsvurderingen ved bruk av 500 stratifiserte tilfeldige prøveflater, verifiserte NRK ytterligere polygoner (totalt 3875) i datasettet i løpet av deres journalistiske undersøkelser. Selv om disse ikke ble samlet inn på en systematisk måte, kan de fortsatt være nyttige for noen oppfølgingsanalyser som å utforske hva som får AI-modellen til å feilidentifisere nedbygging.</p> <p> - Valideringsmetoder: En designbasert tilnærming («design-based area estimation» på engelsk) ble brukt for å kvantifisere kartnøyaktighet og estimere usikkerhet rundt det resulterende arealestimatet rapportert i NRK-artikkelen. Detaljene ved denne metoden er forklart i <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000056">Venter et al. (2024)</a>. Denne tilnærmingen kvantifiserer feilen i det KI-avledede kartet, og korrigerer for dette ved å bruke en stratifisert arealestimator. Derfor er den totale bebygde utvidelsen på 208 km<2> som er rapportert i NRK-artikkelen, skjevhetskorrigert. Vi kvantifiserte også 95 % konfidensintervaller rundt dette er estimat på 9,8 km<2>. Det er viktig å merke seg at valideringstilnærmingen ble utført på individuelle Sentinel-2 piksler på 10x10m og ikke på polygonnivå. Derfor kvantifiserte vi ikke feilen i presisjonen til polygonformen når det gjelder å fange opp hele omfanget av en gitt oppbygget ekspansjonshendelse.</p> <h2> 6. Bruksinformasjon</h2> <p> - Bruksbegrensninger: Med tanke på kartfeilen beskrevet ovenfor, bør brukere fortsette med forsiktighet når de analyserer kartet for å beregne områdestatistikk eller overlegg med andre kart. Som beskrevet i <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000056">Venter et al. (2024)</a>, ved å legge til områdene til polygonene (eller «pikseltelling» med kart formatert som bilder) uten å ta hensyn for feilen i kartet vil føre til feil arealstatistikk. Vi anbefaler at brukere validerer kartet for sin kommune eller studieområde før de går videre med analyse. Det er sannsynlig at feilmarginen varierer mye mellom kommuner. For eksempel, selv om vi ikke har kvantifisert det, la vi merke til mange AI-feil i fjellområder på grunn av snø- og is, og derfor kan kommune med mye fjell ha flere feil enn de langs kysten (for eksempel).</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.