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17 results for “term map”
Long-term (1993-2019) dynamics of tree populations on a mapped 3-ha permanent plot in old-growth northern hardwood forest, Huron Mts., Marquette Co., MI, USA
This data-set includes multiple remeasurements, over 25 years, of all woody stems >2 cm diameter (total of 2125 stems) on a 2.72-ha stem-mapped plot in old-growth northern hardwood forest in the Huron Mountains region of northern Marquette County, MI. The plot and surrounding forest is dominated by sugar maple (Acer saccharum) and eastern hemlock (Tsuga canadensis). Among secondary species, yellow birch (Betula alleghaniensis) and basswood (Tilia americana) are most common. Soils (identified as Kalkaska series) are developed on deep sandy glacial outwash. The plot is within a much larger region of old-growth forest, protected since ca. 1880, with only minimal disturbance associated with access tracks and trails. Numerous other forest community and dendrochronological studies support the interpretation that the area around the study plot has not experienced stand-initiating disturbance for at least 400 years. Initial mapping and measurements (1993-1995 for 2.52 ha; an additional 0.2 ha added in 1999) used a 20x20 m grid established in a near-level area of uniform substrate. All stems were identified to species, mapped on polar coordinates from the center of each grid cell (including, at first measurement, identifiable dead trees, standing and down), and diameter at breast height (dbh) measured to nearest 0.1 cm. All stems were remeasured on a five-year cycle 1999-2019, and new mortality was recorded at each remeasurement. New recruits > 2 cm dbh were added at each remeasurement.
Long-term (1993-2019) tree population measurements from a mapped 2.9-ha permanent plot in old-growth northern hardwood forest, Dukes Research Natural Area, Marquette Co., MI, USA
The Dukes Research Natural Area (Hiawatha National Forest, Marquette Co., MI) amounts to ca. 100 ha of minimally disturbed original forests, including a mix of mesic 'hemlock-northern hardwood' types and peaty wetlands dominated by several species of swamp conifers and black ash (Fraxinus nigra). The RNA hosts a regular grid of 250 permanent monitoring plots (data to be provided in a separate package). In 1993-95, a macroplot of 2.91 ha was established in a mixed mesic upland forest area within the RNA, in which all woody stems >2 cm diameter at breast height (DBH) were identified, measured, and mapped. In 1999 and again every five years subsequently through 2019, the macroplot was recensused; all stems were remeasured, stems newly recruited (>2 cm DBH) were measured and mapped, and any mortality since previous census was noted and described. A severe storm in 2002 resulted in extensive mortality throughout the RNA, particularly in the area in and around the macroplot.
Change detection technique comparison in long-term wetland monitoring: datasets and maps of the Poitevin Marsh (France)
<h3>For a full description of the methodology and results, please see the following article:</h3> <div> <div>Demarquet, Q., Rapinel, S., Gore, O., Dufour, S., Hubert-Moy, L., 2024. Continuous change detection outperforms traditional post-classification change detection for long term monitoring of wetlands. <em>International Journal of Applied Earth Observation and Geoinformation </em>133, 104142. <a href="https://doi.org/10.1016/j.jag.2024.104142">https://doi.org/10.1016/j.jag.2024.104142</a></div> <div> </div> <div>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> </div> <h3># Datasets</h3> <p>Points datasets are projected in WGS84 (EPSG:4326), and are provided in the open source GeoPackage format.</p> <p>The first dataset (<strong>Dataset_1.gpkg</strong>) contains training and validation points for random forest classification of EUNIS habitats in the Poitevin Marsh. This dataset consists of 3360 training and 840 validation points (total: 4200).<br>Fields description:</p> <ul> <li>"<em>ID</em>": unique identifier</li> <li>"<em>CLASS</em>": EUNIS first level habitat type, classified as following:<br> <ul> <li>1: EUNIS habitat A</li> <li>2: EUNIS habitat B</li> <li>3: EUNIS habitat C1J5</li> <li>4: EUNIS habitat C3</li> <li>5: EUNIS habitat E</li> <li>6: EUNIS habitat G</li> <li>7: EUNIS habitat I</li> <li>8: EUNIS habitat J</li> </ul> </li> <li>"<em>DATE</em>": Date associated with EUNIS habitat sample</li> <li>"<em>LON</em>": Point longitude in decimal degrees</li> <li>"<em>LAT</em>": Point latitude in decimal degrees</li> <li>"<em>TYPE</em>": Either training ("<em>train</em>") or validation ("<em>test</em>") sample</li> </ul> <p>The second dataset (<strong>Dataset_2.gpkg</strong>) contains points for the Olofsson correction method. This dataset consists of 326 points where the change classes are classified as following: -10 (wetland loss), 10 (wetland gain), 100 (stable existing wetland), and 200 (stable damaged wetland).<br>Fields description:</p> <ul> <li>"<em>ID</em>": unique identifier</li> <li>"<em>LON</em>": Point longitude in decimal degrees</li> <li>"<em>LAT</em>": Point latitude in decimal degrees</li> <li>"<em>REFERENCE</em>": Change class reference</li> <li>"<em>CCDC</em>": Change class obtained from the Continuous Change Detection and Classification approach</li> <li>"<em>PCCD</em>": Change class obtained from the Post-Classification Change Detection approach</li> </ul> <p>Supplementary layout files (<strong>Dataset_1.qml</strong> and <strong>Dataset_2.qml</strong>) support formatting of the points in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># EUNIS habitat</h3> <p>Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. </p> <p>Habitat maps are given for the two approaches in years 1984 and 2022:</p> <ul> <li>CCDC: Continuous Change Detection and Classification (<strong>CCDC_HABITAT_1984.tif</strong> and <strong>CCDC_HABITAT_2022.tif</strong>)</li> <li>PCCD: Traditional post-classification approach (<strong>PCCD_HABITAT_1984.tif </strong>and <strong>PCCD_HABITAT_2022.tif</strong>)</li> </ul> <p>Supplementary layout files (<strong>CCDC_HABITAT_1984.qml, CCDC_HABITAT_2022.qml, PCCD_HABITAT_1984.qml, PCCD_HABITAT_2022.qml</strong>) support formatting of raster layers in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># Change detection during the 1984-2022 period</h3> <p>Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. Raster values follow the classification scheme used in Dataset_2.</p> <p>Change detection maps are given for the two approaches:</p> <ul> <li>CCDC: Continuous Change Detection and Classification (<strong>CCDC_CHANGE_1984_2022.tif</strong>)</li> <li>PCCD: Traditional post-classification approach (<strong>PCCD_CHANGE_1984_2022.tif</strong>)</li> </ul> <p>Supplementary layer files (<strong>CCDC_CHANGE_1984_2022.qml</strong> and<strong> PCCD_CHANGE_1984_2022.qml</strong>) support formatting of the raster layers in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># GEE repository</h3> <p>To get direct access to GEE scripts and assets, please follow those two links:</p> <p>https://code.earthengine.google.com/?accept_repo=users/demarquetquentin/CCDC_Poitevin</p> <p>https://code.earthengine.google.com/?asset=projects/ee-quen-dem/assets/CCDC_Poitevin</p>
A map of active cropland and short-term fallows across Northern Mozambique derived from PlanetScope data
<p><strong>Overview</strong></p> <p>A map of smallholder-dominated landscapes covering the provinces Niassa, Zambezia, Cabo Delgado, and Nampula in Northern Mozambique. The map includes active cropland and short-term fallows as separate classes, as well as five land cover classes (herbaceous vegetation, open woodlands, closed woodlands, non-vegetated land, water). The map is based on PlanetScope mosaics and consequently comes at 4.77m spatial resolution.</p> <p>The download contains the following files:</p> <ul> <li>ps_lc_nmoz.tif / .qml: land cover map and associated QGIS style file</li> <li>ps_lc_nmoz_probmargins.tif / .qml: probability margins and associated QGIS style file</li> <li>training.gpkg: training samples with class labels</li> <li>LICENSE.pdf: NICFI data program user license</li> </ul> <p><strong>Map accuracy</strong></p> <p>We conducted an area-adjusted accuracy assessment based on a stratified random sample, which yielded important insights regarding accuracies and error types. The area-adjusted overall accuracy of the map is 88.9%, but users should be aware of the most important error types:</p> <ul> <li>Active cropland were overestimated, whereas local topographical depressions with moist soils, and regions with exposed soils/rocks and sparse vegetation cover were found to be falsely classified.</li> <li>Short-term fallows were underestimated, particularly in regions with high growth rates and extensive land management, such as parts of the northern and north-eastern study region.</li> </ul> <p><strong>Further resources</strong></p> <p>The production of this map was made possible through the <a href="https://www.planet.com/nicfi/">NICFI data program</a>, providing the PlanetScope mosaics and the Google Earth Engine cloud computing platform for preprocessing of the satellite data and classification. As such, the use of the map falls under the <a href="https://assets.planet.com/docs/Planet_ParticipantLicenseAgreement_NICFI.pdf">NICFI data program license agreement</a> included in the download. The code for preprocessing the PlanetScope mosaics is based on the Google Earth Engine Python API and made available at <a href="https://github.com/philipperufin/eepypr/">https://github.com/philipperufin/eepypr/</a>.</p> <p>We advise map users to read the <a href="https://eartharxiv.org/repository/view/3174/">preprint</a> or the <a href="https://doi.org/10.1016/j.jag.2022.102937">open access paper</a> for detailed insights. In case of questions please consult these resources or contact the lead author of the work.</p>
Process map for casting a long-term experimental campaign on RC shrinkage cracking.
<p>This dataset presents the process map that was developed for the casting of a long-term experimental campaign on reinforced concrete (RC) slabs subjected to the combined effect of restrained shrinkage and vertical loads.<br> This experimental campaign was performed in the scope of the FCT project "IntegraCrete: A comprehensive multi-physics and multi-scale approach to the combined effects of applied loads and thermal/shrinkage deformations in reinforced concrete structures''.<br> The results from this experimental campaign are presented in Gomes et al (2020), while the conceptualization, planning and experimental procedures are described in detail in Gomes et al (2021). The latter is supported by this process map to describe the micromanagement plan that was devised for the casting day.<br> 14 people were involved in the casting of 3 slabs and 2 complementary specimens inside a highly instrumented climatic chamber, as well as 38 specimens for concrete characterization at different ages and 12 load blocks to use as vertical loads. This process map was developed with the standard Business Process Model and Notation (BPMN).</p>
ChinaWheat30L: Long-term winter wheat maps of China at 30-m resolution from 2000 to 2023
<p>This is the long-term winter wheat map product of China at 30-m resolution from 2000 to 2023 (ChinaWheat30L). The product was generated using a knowledge-guided machine learning approach and the integration of satellite remote sensing and environmental datasets.</p>
Fig. 1. The potential distribution map for B in Long-Term Bioclimatic Modelling The Distribution Of The Fire-Bellied Toad, Bombina Bombina (Anura, Bombinatoridae), Under The Influence Of Global Climate Change
Fig. 1. The potential distribution map for B. bombina under contemporary climatic conditions. The colour gradient represents high (red) to low (green) habitat suitability for the species.
Fig. 4. The potential distribution map for B. bombina under projected 2050 in Long-Term Bioclimatic Modelling The Distribution Of The Fire-Bellied Toad, Bombina Bombina (Anura, Bombinatoridae), Under The Influence Of Global Climate Change
Fig. 4. The potential distribution map for B. bombina under projected 2050 climatic conditions. The colour gradient represents high (red) to low (green) habitat suitability for the species.
Рис. 1. Регион иссΛеΑований: A — его поΛожение на карте Восточной Азии; B — общий виΑ Буреинско-Хинганской низменности; C — карта-схема ΑебеΑинского стационара Хинганского заповеΑника. УсΛовные обозначения: I — Хинганский заповеΑник (вкΛючает Αва кΛастера); II — заказник «Ганукан». 1 — Антоновское воΑохраниΛище; 2 — оз. ΔоΛгое; 3 — оз. Гусиное; 4 — оз. Третье ΑебеΑиное Fig. 1. Study region: A — study region on the map of the East Asia; B — Burea-Khingan (Arkhara) lowland; C — Lebedinsky Station. Notes: I — two clusters of Khingan Nature Reserve; II — Ganukan Sanctuary. 1 — Antonovskoye Reservoir; 2 — Dolgoye Lake; 3 — Gusinoye Lake; 4 — Lebedinoye Lake in The results of long-term observation of waterfowl spring migration in Khingan Nature Reserve, Eastern Russia
Рис. 1. Регион иссΛеΑований: A — его поΛожение на карте Восточной Азии; B — общий виΑ Буреинско-Хинганской низменности; C — карта-схема ΑебеΑинского стационара Хинганского заповеΑника. УсΛовные обозначения: I — Хинганский заповеΑник (вкΛючает Αва кΛастера); II — заказник «Ганукан». 1 — Антоновское воΑохраниΛище; 2 — оз. ΔоΛгое; 3 — оз. Гусиное; 4 — оз. Третье ΑебеΑиное Fig. 1. Study region: A — study region on the map of the East Asia; B — Burea-Khingan (Arkhara) lowland; C — Lebedinsky Station. Notes: I — two clusters of Khingan Nature Reserve; II — Ganukan Sanctuary. 1 — Antonovskoye Reservoir; 2 — Dolgoye Lake; 3 — Gusinoye Lake; 4 — Lebedinoye Lake
MeSDiCon subset for CodiEsp: MESH terms in MeSDiCon mapped to ICD10 CM and ICD10 PCS
<p>The MeSDiCon consists of a list or gazetteer of candidate names of diseases and symptoms mentioned in Spanish clinical texts. Thus MeSDiCon serves as a lexical resource or dictionary for automatic detection of disease/symptom mentions, as well as indexing or classification of medical texts with such concept types. Terms in MeSDiCon were mapped to MESH terminology.</p> <p>In this subset, we have mapped MESH codes to ICD10-CM and ICD10-PCS through UMLS Metathesaurus. Then, this resource contains diseases and symptoms terms from Spanish clinical texts mapped to MESH and ICD10.</p> <p> </p> <p><strong>Please cite if you use this dataset:</strong></p> <p>Antonio Miranda-Escalada, Aitor Gonzalez-Agirre, Jordi Armengol-Estapé and Martin Krallinger. Overview of automatic clinical coding: annotations, guidelines, and solutions for non-English clinical cases at CodiEsp track of CLEF eHealth 2020. In CLEF (Working Notes). 2020</p> <pre><code>@inproceedings{miranda2020overview, title={Overview of automatic clinical coding: annotations, guidelines, and solutions for non-english clinical cases at codiesp track of CLEF eHealth 2020}, author={Miranda-Escalada, Antonio and Gonzalez-Agirre, Aitor and Armengol-Estap{\'e}, Jordi and Krallinger, Martin}, booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings}, year={2020} }</code></pre> <p> </p> <p><strong>File structure</strong></p> <p>TSV. Data is separated by tabs (\t). Every row of the file has the following fields:</p> <pre><code>terminology identifier translatedTerm termCount documentCount ICD10CM-code ICD10PCS-code</code></pre> <p>In case one MESH term is mapped to more than one ICD10 code, they are separated by commas.</p>
Term map of European Social Survey publications
<p>This is a term-map which can be visualized using <a href="https://www.vosviewer.com/">VOSviewer</a>.</p> <p>We extracted terms from titles and abstracts from Europen Social Survey publication and visualized them using VOSviewer. Terms are located close to each other if they co-occur frequently. The axes themselves don’t have any special meaning, only the relative distances are relevant. The size of the terms reflect the number of publications.</p> <p>We provide so-called "overlay" views for various countries and institutions, showing where their activity.</p>
1000 Disease Ontology terms and their Wikidata mappings to 17 mostly Indian languages and English
<p>This dataset contains the result of a SPARQL query run on the <a href="https://query.wikidata.org/">Wikidata Query Service</a> on 13 February 2020 around 22:25 UTC. They are archived here as a means to determine progress with the coverage of disease-related terms in languages other than English, particularly in languages of India.</p> <p>The <a href="https://query.wikidata.org/#%23%20Wikidata%20items%20for%20concepts%20that%20have%20a%20Disease%20Ontology%20ID%20%28P699%29%0A%23%20sorted%20by%20number%20of%20sitelinks%0A%23%20optionally%20with%20their%20Wikidata%20label%20in%20English%0A%23%20optionally%20with%20their%20Wikidata%20label%20in%20Hindi%2C%20Bangla%20and%20Swahili%0A%23%20optionally%20with%20their%20Wikidata%20label%20in%20Marathi%2C%20Telugu%2C%20Eastern%20Punjabi%2C%20Western%20Punjabi%2C%20Gujarathi%2C%20Maithili%2C%20Kannada%2C%20Odia%2C%20Bhojpuri%2C%20Tamil%2C%20Nepali%2C%20Urdu%2C%20Malayalam%2C%20Esperanto%0A%0ASELECT%20DISTINCT%20%3Fitem%20%0A%23%20English%20Wikidata%20label%0A%20%3FLabelEN%0A%0A%23%20%20%20%20English%20Wikipedia%20article%20title%0A%3FPageTitleEN%20%0A%0A%23%20Hindi%2C%20%20%20Bangla%20and%20Swahili%20Wikidata%20labels%0A%3FLabelHI%20%3FLabelBN%20%20%20%20%3FLabelSW%20%0A%0A%23%20Marathi%2C%20%20Telugu%2C%20Eastern%20Punjabi%2C%20Western%20Punjabi%2C%20Gujarathi%2C%20Maithili%2C%20Kannada%2C%20%20%20%20Odia%2C%20Bhojpuri%2C%20%20%20Tamil%2C%20%20Nepali%2C%20%20%20%20Urdu%2C%20Malayalam%2C%20Esperanto%0A%20%3FLabelMR%20%3FLabelTE%20%20%20%20%20%20%20%20%20%3FLabelPA%20%20%20%20%20%20%20%20%3FLabelPNB%20%20%20%3FLabelGU%20%3FLabelMAI%20%3FLabelKN%20%3FLabelOR%20%20%3FLabelBH%20%3FLabelTA%20%3FLabelNE%20%3FLabelUR%20%20%20%3FLabelML%20%20%20%3FLabelEO%0A%0A%3Fsitelinks%20%0A%0AWHERE%20%7B%0A%20%20%3Fitem%20wdt%3AP699%20%5B%5D%20.%20%20%0A%20%20%3Fitem%20wikibase%3Asitelinks%20%3Fsitelinks%20.%0A%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3F%3FLabelEN%20filter%20%28lang%28%3FLabelEN%29%20%3D%20%22en%22%29%20.%20%7D%20%0A%20%20OPTIONAL%20%7B%20%3Farticle%20schema%3Aabout%20%3Fitem%20%3B%20schema%3AisPartOf%20%3Chttps%3A%2F%2Fen.wikipedia.org%2F%3E%20%3B%20%20schema%3Aname%20%3FPageTitleEN%20.%20%7D%0A%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelHI%20filter%20%28lang%28%3FLabelHI%29%20%3D%20%22hi%22%29%20.%20%7D%20%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelBN%20filter%20%28lang%28%3FLabelBN%29%20%3D%20%22bn%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelSW%20filter%20%28lang%28%3FLabelSW%29%20%3D%20%22sw%22%29%20.%20%7D%0A%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelMR%20filter%20%28lang%28%3FLabelMR%29%20%3D%20%22mr%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelTE%20filter%20%28lang%28%3FLabelTE%29%20%3D%20%22te%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelPA%20filter%20%28lang%28%3FLabelPA%29%20%3D%20%22pa%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelPNB%20filter%20%28lang%28%3FLabelPNB%29%20%3D%20%22pnb%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelGU%20filter%20%28lang%28%3FLabelGU%29%20%3D%20%22gu%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelMAI%20filter%20%28lang%28%3FLabelMAI%29%20%3D%20%22mai%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelKN%20filter%20%28lang%28%3FLabelKN%29%20%3D%20%22kn%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelOR%20filter%20%28lang%28%3FLabelOR%29%20%3D%20%22or%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelBH%20filter%20%28lang%28%3FLabelBH%29%20%3D%20%22bh%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelTA%20filter%20%28lang%28%3FLabelTA%29%20%3D%20%22ta%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelNE%20filter%20%28lang%28%3FLabelNE%29%20%3D%20%22ne%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelUR%20filter%20%28lang%28%3FLabelUR%29%20%3D%20%22ur%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelML%20filter%20%28lang%28%3FLabelML%29%20%3D%20%22ml%22%29%20.%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fitem%20rdfs%3Alabel%20%3FLabelEO%20filter%20%28lang%28%3FLabelEO%29%20%3D%20%22eo%22%29%20.%20%7D%0A%7D%0AORDER%20BY%20DESC%28%3Fsitelinks%29%0ALIMIT%201000">SPARQL query</a></p> <ul> <li>was for <ul> <li> <p>Wikidata items for concepts that have a <a href="https://www.wikidata.org/wiki/Property:P699">Disease Ontology ID (P699)</a></p> <ul> <li> <p>sorted by number of sitelinks</p> </li> <li> <p>optionally with their Wikidata label in English</p> </li> <li> <p>optionally with their Wikipedia article title in English</p> </li> <li> <p>optionally with their Wikidata label in Hindi, Bangla and Swahili</p> </li> <li> <p>optionally with their Wikidata label in Marathi, Telugu, Eastern Punjabi, Western Punjabi, Gujarathi, Maithili, Kannada, Odia, Bhojpuri, Tamil, Nepali, Urdu, Malayalam, Esperanto</p> </li> </ul> </li> </ul> </li> <li>is contained in the file SPARQL.txt,</li> </ul> <p>whereas the results are available in several formats, as provided by the Wikidata Query Service:</p> <ul> <li>query.csv</li> <li>query.tsv<br> query.html</li> <li>query.json.txt (Zenodo produced an error upon trying to upload the file as query.json, so I renamed it, which worked fine).</li> </ul> <p>A simplified version of the SPARQL query can also be fed into the TABernacle tool that <a href="https://tools.wmflabs.org/tabernacle/#/tab/sparql/SELECT%20%09%0A%09%3Fitem%0AWHERE%20%09%0A%7B%0A%20%20%3Fitem%20wdt%3AP699%20%5B%5D%20.%20%20%0A%20%20%3Fitem%20wikibase%3Asitelinks%20%3Fsitelinks%20.%0A%0A%7D%0AORDER%20BY%20DESC(%3Fsitelinks)/Len%2Chi%2Cbn%2Csw%2Cmr%2Cte%2Cpa%2Cpnb%2Cgu%2Cmai%2Ckn%2Cor%2Cbh%2Cta%2Cne%2Cur%2Cml%2Ceo">represents</a> the live data in a way that facilitates editing the missing pieces.</p>
A dataset of long-term consistency values of resting-state fMRI connectivity maps in a single individual derived at multiple sites and vendors using the Canadian Dementia Imaging Protocol
<p>This dataset contains preprocessed resting state fMRI data (.nii.gz) with accompanying confound files (.tsv) from the Single Individual volunteer for Multiple Observations across Networks (SIMON; http://fcon_1000.projects.nitrc.org/indi/retro/SIMON.html) dataset that has been minimally preprocessed using the NeuroImaging Analysis Kit (NIAK; http://niak.simexp-lab.org/build/html/PREPROCESSING.html). Preprocessing steps included: (1) Slice timing correction; (2) Estimation of rigid-body motion in fMRI runs, both within- and between sessions; (3) Linear or non-linear coregistration of the structural scan in stereotaxic space; (4) Individual coregistration between structural and functional scans; (5) Resampling of functional scans in stereotaxic space. Confound files (.tsv) have been included for purposes of scrubbing and regression of confounds using NIAK or other analysis software, allowing for further processing without conflicts.</p>
T2 Heart Mapping in AMI Population for the Prediction of Short Term Major Adverse Cardiovascular Events
ClinicalTrials.gov study NCT01796743. IPD Sharing: Not stated. Countries: 1. Publications: 0.
HDF4 Data Used to Assess Long-Term Access to Remote Sensing Data with Layout Maps, Version 1
Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.This data set consists of a sampling of each type of Hierarchical Data Format version 4 (HDF4) data that are archived at the eight National Aeronautic and Space Administration (NASA) Earth Science Data Centers (ESDCs). The data were sampled for a collaborative study between The HDF Group, the Goddard Earth Sciences Data and Information Services Center (GES-DISC), and the National Snow and Ice Data Center (NSIDC) in order to assess the complex internal byte layout of HDF files. Based on the results of this assessment, methods for producing a map of the layout of the HDF4 files held by NASA were prototyped using a markup-language-based HDF tool. The resulting maps allow a separate program to read the file without recourse to the HDF application programming interface (API). Data products selected for the study, and a table summarizing the results, are available via HTTPS.
Mapping Gene Expression in Excitatory Neurons During Hippocampal Late-Phase Long-term Potentiation
GEO Series GSE79790. Mus musculus. 36 samples. Type: Expression profiling by high throughput sequencing.
Supporting Information for "Short-term ionospheric TEC variations from Global Ionosphere Maps"
<p>The following files are included:</p> <ol> <li>resAm6.7z (compressed 7z file): (2004-2017) residuals in MATLAB matrix format from the Total Electron Content (TEC) model of "New modes and mechanisms of long-term ionospheric TEC variations from Global Ionosphere Maps" doi: 10.1029/2019JA027703. The geomagnetic contribution in the model is set to constant Am=6, so that short-term variations can be properly investigated.<br> Grid format: res(Time, Latitude, Longitude). Time is given as modified Julian Date in the MJD matrix. Latitude is [90N:90S]=[1:71]. Longitude is [180W:180E]=[1:73]</li> </ol>
ScienceDex guides
Understand access before you commit
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.