Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
31
datasets available to search
ShareScore release 0.9.0
Dataset results
31 results for “FAO”
FAO-SEPAL_Peatland restoration activities 8
Observing and verifying canal blocking and backfilling
Figure 1. – FAO Statistical Area 21, ranging approximately from 35 in The fisheries of the sea around Saint-Pierre and Miquelon: from cod to sea cucumber
Figure 1. – FAO Statistical Area 21, ranging approximately from 35 to 77.5 0N, and from 42 to 77.5 0E (6,293,000 km2), and encompassing the Newfoundland-Labrador Shelf Large Marine Ecosystem (LME; dark grey; 664,700 km2) and the Southern Grand Banks- South Newfoundland Marine Ecoregion (light grey; 55,400 km2), slightly modified to fit inside the LME. The insert in the lower right corner shows the Exclusive Economic Zone of St Pierre and Miquelon (12,400 km2).
10nM insulin stimulation of Fao Cells
GEO Series GSE58302. Rattus norvegicus. 5 samples. Type: Expression profiling by array.
UrbanOccupationsOETR_temettuat_animals_FAO_6_region_rural_geosample
<p><span>With the UrbanOccupationsOETR, a European Research Councill, Starting Grant funded (Grant Number 679097, Industrialisation and Urban Growth from the mid-nineteenth century Ottoman Empire to Contemporary Turkey in a Comparative Perspective, 1850-2000, </span><span><a href="https://urbanoccupations.ku.edu.tr/"><span><span>UrbanOccupationsOETR</span></span></a></span><span>) project hosted at Koç University 2016-2022, we wanted to highlight the importance of rural economic dynamics to explain differences in long-term regional economic development in the late Ottoman Empire. We provide an Excel dataset on the crop-specific agricultural mix and land area of an Ottoman region, Bursa, in the 1840s. This dataset is the result of a new geosampling methodology we devised, representing a novel development in the agricultural and overall economic history of Southeast Europe and the Middle East.</span><span> </span></p> <p><span>The 1840s serve as a good period to choose for base years mainly due to three main factors to sample economic data on a regional scale. First, due to <em>Tanzimat</em> reforms (planned and only partially accomplished transformation of the Ottoman central administration in the mid-nineteenth century), the 1840s marked a watershed of bureaucratical information gathering. Especially, the <em>temettuat</em> registers were created as a by-product to realize a drastic change in tax collection. With at least in its first iteration, the unsuccessful abolishment of tax-farming by the Tanzimat decree in 1839, the Ottoman central administration aimed to transform the existing indirect and communal taxation with direct and individual modalities. To accomplish this goal, the administration had to survey the tax base, which was in disguise due to centuries-long tax farming practices. The <em>temettuat </em>registers were conducted in the core regions of the empire with the main exception of the imperial capital, Istanbul. Second, the 1840s correspond to the last period before the beginning of drastic territorial losses, primarily in Southeast Europe, which triggered in size and frequency unprecedented waves of emigration and immigration between the core territories of the empire both in Southeast Europe as well as in Anatolia, which continued until the official demise or the implosion of the empire. Third and lastly, the 1840s serves as a very suitable point to assess the dynamics of pre-industrial and <em>ancienne</em> regime agricultural dynamics due to the lack of modern means of mechanization, irrigation, and fertilization combined with extremely rudimentary transport facilities.</span></p> <p><span>The temettuat surveys are invaluable resources for they provide agricultural asset- / crop-type specific agricultural mix information with cultivation area per household. However, extracting their detailed information requires a team and years. To overcome this, we developed a sampling strategy that selected five locations per subdistrict using the Analytical Hierarchy Process (AHP), considering factors of agricultural suitability (85% weight), connectivity to historical roads (within a 500-meter to the closest road or to the Danube, 15% weight, justified by its impact on suitability), and subdistrict population size (chosen villages must represent at least 5% of the subdistrict's total population).</span><span> </span></p> <p><span>Our geosampling methodology of the 1840s tax registers (<em>temettuat</em>) is based on contemporary Ottoman population registers. With this geosampling method, we aim to estimate the regional (district (<em>sancak</em>) and subdistrict (<em>kaza</em>)) level total area of cultivation and shares of the agricultural mix for key products. We are using two mid-nineteenth-century datasets: Ottoman tax (TMT) (<em>temettuat</em>) surveys for crop type and cultivation area and the population (<em>nüfus</em>) (NFS) registers for population-based sampling. Connectivity is based on a detailed and provenly accurate 1940s German military map of Turkey, <em>Deutsche Heereskarte </em>(DHK). The agricultural suitability raster is an amalgamation of the Land Capability Classification (LCC) encapsulating the variables of soil quality and quantity and the Digital Elevation Model (DEM) based on Shuttle Radar Topography Mission with 30-meter-resolution and comprising elevation, slope, and ruggedness data.</span></p> <p><span>In the end, a geosampling initiative was undertaken across six regions in Southeast Europe and Anatolia, namely Ankara, Bursa, Plovdiv, Ruse, Manisa, and Edirne, covering a total of 277 locations with 17,675 households. Our project team entered the economic data from those records into a Microsoft Access database. We employed a specially crafted data entry template to organize the tax survey data into multiple categories systematically.</span></p> <p><span>After geosampling locations, our objective extended to deriving estimates for the total number of animals within each subdistrict and region. To achieve this goal, it was imperative that the data undergo coding of the types of animals into a standardized and comparable scheme. We adopted the “Classification of Livestock for the Agricultural Census” from the “World Programme for the Census of Agriculture 2010” of the Food and Agriculture Organization of the United Nations (FAO) published in 2005. </span></p> <p><span>Every animal entry was coded into the “Classification of Livestock for the Agricultural Census” scheme, encompassing the categories of “11” – Cattle; “12” – Buffaloes; “20” – Small ruminants; “21” – Sheep; “22” – Goats; “30” - Pigs or swine; “40” – Equines; “41” - Horses; “42” – Asses; “43” – Mules and hinnies; “52” - Camels. </span></p> <p><span>To accurately reflect the total number of animals, we created a separate column where we doubled the quantities recorded as pairs (<em>çift</em> in Turkish) and then added this to the count of single animals.</span></p> <p><span>This Zenodo dataset offers animal data for the entire geosampling area per household. It includes 50,569 agricultural entries coded into to the “Classification of Livestock for the Agricultural Census”, detailing the quantity and the cultivated area, corresponding to 12,514 individuals across 12,147 households. Please note that this is a rural geosample. Although the tax surveys of the primary (urban) and secondary (subdistrict centers) locations of all regions were read and entered, they are not included in this dataset.</span></p> <p><span>The categories and descriptions of the variables of the geosample dataset are as follows:</span></p> <table> <tbody> <tr> <td> <p><strong><span>Variable</span></strong></p> </td> <td> <p><strong><span>Description</span></strong></p> </td> </tr> <tr> <td> <p><span> </span></p> <p><span>“GeoCode”</span></p> </td> <td> <p><span> </span></p> <p><span>UniqueID belonging to a specific geosampled location</span></p> </td> </tr> <tr> <td> <p><span> “Longitude” & “Latitude”</span></p> </td> <td> <p><span>Geographical coordinates used to specify the precise location of a geosampled location on the Earth's surface</span></p> </td> </tr> <tr> <td> <p><span>“Region” & “SubDistrict” & “Location”</span></p> </td> <td> <p><span>Geographic unit of entry, including region (district/<em>sancak</em>); subdistrict (<em>kaza</em>); and geosampled location as they appear in the population registers</span></p> </td> </tr> <tr> <td> <p><span>“RegisterNo”</span></p> </td> <td> <p><span>Archival code of the population register whose data is being entered</span></p> </td> </tr> <tr> <td> <p><span>“HaneNo”</span></p> </td> <td> <p><span>Number of the household (specified by the registers as <em>Hane</em>), as appears in the register</span></p> </td> </tr> <tr> <td> <p><span>"HouseID"</span></p> </td> <td> <p><span>Unique ID belonging to a specific household, automatically generated by Microsoft Access</span></p> </td> </tr> <tr> <td> <p><span>"IndividualID"</span></p> </td> <td> <p><span>Unique ID belonging to a specific individual, automatically generated by Microsoft Access</span></p> </td> </tr> <tr> <td> <p><span>"AnimalID"</span></p> </td> <td> <p><span>UniqueID belonging to a specific animal of an individual</span></p> </td> </tr> <tr> <td> <p><span>"Animals"</span></p> </td> <td> <p><span>Type of the animal</span></p> </td> </tr> <tr> <td> <p><span>"AnimalClass"</span></p> </td> <td> <p><span>FAO code of the animal</span></p> </td> </tr> <tr> <td> <p><span>"CtgUnit"</span></p> </td> <td> <p><span>Specific terms describing quantity of an animal (“adet”, “res”, “tek” for a single animal, “çift” for pair of animals)</span></p> </td> </tr> <tr> <td> <p><span>"Unit"</span></p> </td> <td> <p><span>Quantity of the "CategoryUnit"</span></p> </td> </tr> <tr> <td> <p><span>“AnimalCount”</span></p> </td> <td> <p><span>"Unit" converted into precise numbers. In other words, the quantities expressed in pairs (<em>çift</em>) have been doubled, while the counts of single animals have been left unchanged.</span></p> </td> </tr> </tbody> </table> <p> </p>
The Therapeutic Effect of Curcumin in Nanogels Compared to 0.1% FAO in the Management of Oral Lichen Planus
ClinicalTrials.gov study NCT06932848. IPD Sharing: NO. Countries: 0. Publications: 0.
FAO-fueled OXPHOS and NRF2-mediated stress resilience in MICs drive lymph node metastasis
GEO Series GSE220108. Homo sapiens. 5 samples. Type: Expression profiling by high throughput sequencing.
Minister Mansholt opent de vergadering van het nationaal Fao Comite
<u>Source</u>: Europeana <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1652523900.6547.jpg">https://4dcity.org/imgupload/1652523900.6547.jpg</a> <br><u>Original Image URL</u>: <a href="https://api.europeana.eu/thumbnail/v2/url.json?uri=https%3A%2F%2Fwww.openbeelden.nl%2Fimages%2F1220286%2FMinister_Mansholt_opent_de_vergadering_van_het_nationaal_Fao_Comite_%25280_55%2529.png&type=VIDEO">https://api.europeana.eu/thumbnail/v2/url.json?uri=https%3A%2F%2Fwww.openbeelden.nl%2Fimages%2F1220286%2FMinister_Mansholt_opent_de_vergadering_van_het_nationaal_Fao_Comite_%25280_55%2529.png&type=VIDEO</a> <br><br><u>Image-Metadata:</u><br>Filename: 1652523900.6547.jpg<br>Image Dimensions: 360x288<br>Megapixels: 0.10 MP<br>Filesize: 123.61 KB<br>
Fluorescence detection of FAO activity with a chemical probe in cultured cells and mouse liver tissue
GEO Series GSE224649. Mus musculus. 5 samples. Type: Expression profiling by high throughput sequencing.
HFD activated FAO mediates intestinal stemness and tumorgenicity I
GEO Series GSE151045. Mus musculus. 32 samples. Type: Expression profiling by high throughput sequencing.
HFD activated FAO mediates intestinal stemness and tumorgenicity II
GEO Series GSE151046. Mus musculus. 38 samples. Type: Expression profiling by high throughput sequencing.
HFD activated FAO mediates intestinal stemness and tumorgenicity
GEO Series GSE151047. Mus musculus. 102 samples. Type: Expression profiling by high throughput sequencing.
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.