Find research datasets worth reusing

opencc-by-4.0Aug 2022View details →

◂Fig. 6 Gynoecial development, fruit and seedling of C. crenata %yellow frames), C. cf. grandicalyx %blue frames) and C. sinensis %pink frames; A–F light microscopy, G–K stereo microscopy of endocarp, mesocarp removed; L–O field images; TS in horizontal orientation). A, B TS of anthetic flower %note two to three abortive ovules and strongly stained, peripheral tissue). C, D TS of anthetic flower %note two to three abortive ovules and lignifying portions of prospective mesocarp). E Young fruit %note developing endocarp and flashily pink portions of the mesocarp). F TS of postanthetic flower %note three abortive ovules and lignifying portions of prospective mesocarp). G TS of endocarp, with three developed embryos removed %note scanty endosperm). H Endocarp. J TS of endocarp. K Endocarp. L Immature fruits. M Mature fruits. N Seedlings %note short hypocotyl and long petioles of cotyledons). O Seedlings %note long hypocotyl and short petioles of cotyledons; image taken from cultivated plant, accession number 2012–0005, in the Botanical Garden Munich) %LS, longisection; TS, transverse section; ao, abortive ovule; cot, cotyledon; db, dorsal bundle; c, calyx; ec, endocarp; ens, endosperm; ex, exocarp; fr, fruit; h, hypocotyl; int, integument; lb, lateral bundle; mc, mesocarp; o, ovule; pet, petiolus; sty, style; ut, peripheral tissue; vs, ventral slit) in Observations on flower and fruit anatomy in dioecious species of Cordia (Cordiaceae, Boraginales) with evolutionary interpretations

◂Fig. 6 Gynoecial development, fruit and seedling of C. crenata %yellow frames), C. cf. grandicalyx %blue frames) and C. sinensis %pink frames; A–F light microscopy, G–K stereo microscopy of endocarp, mesocarp removed; L–O field images; TS in horizontal orientation). A, B TS of anthetic flower %note two to three abortive ovules and strongly stained, peripheral tissue). C, D TS of anthetic flower %note two to three abortive ovules and lignifying portions of prospective mesocarp). E Young fruit %note developing endocarp and flashily pink portions of the mesocarp). F TS of postanthetic flower %note three abortive ovules and lignifying portions of prospective mesocarp). G TS of endocarp, with three developed embryos removed %note scanty endosperm). H Endocarp. J TS of endocarp. K Endocarp. L Immature fruits. M Mature fruits. N Seedlings %note short hypocotyl and long petioles of cotyledons). O Seedlings %note long hypocotyl and short petioles of cotyledons; image taken from cultivated plant, accession number 2012–0005, in the Botanical Garden Munich) %LS, longisection; TS, transverse section; ao, abortive ovule; cot, cotyledon; db, dorsal bundle; c, calyx; ec, endocarp; ens, endosperm; ex, exocarp; fr, fruit; h, hypocotyl; int, integument; lb, lateral bundle; mc, mesocarp; o, ovule; pet, petiolus; sty, style; ut, peripheral tissue; vs, ventral slit)

opencc-by-4.0Oct 2020View details →

Figure 2 in New record of Microtechnites bractatus (Say) (Hemiptera: Miridae) infesting Crotalaria spp. and injuries of Miridae in cultivated plants in the State of Paraná, Brazil

Figure 2 Damage of (A) Microtechnites bractatus and (B) Collaria scenica in black oats (Avena strigosa), ryegrass (Lolium multiflorum), beans (Phaseolus vulgaris), white clover (Trifolium repens), tifton 85 (Cynodon spp.), fescue (Festuca sp.), corn (Zea mays) and (viii) crotalaria (Crotalaria juncea).

Fig. 8 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?

Fig. 8. Estimated richness of insects in the whorl, ear and tassel of conventional (Conv.) and transgenic maize (Cry1Ab, Cry1F and combined Cry2Ab2 and Cry1A105 proteins) from Iguatama county, MG. Bars represent 95% confidence interval.

Fig. 15 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?

Fig. 15. Estimated diversity of secondary pests (S.P.) and natural enemies (N.E.) in tassels of conventional and transgenic maize for Cry1Ab, Cry1F and combined Cry2Ab2 and Cry1A105 proteins (A) and estimated richness in conventional maize and Bt maize (B), in different counties in Minas Gerais.Bars represent 95% confidence interval.

Fig. 4 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?

Fig. 4. Estimated richness of insects in the whorl, ear and tassel of conventional (Conv.) and transgenic maize (Cry1Ab and Cry1F proteins) from Varjão de Minas county. Bars represent 95% confidence interval.

Fig. 16 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?

Fig. 16. Relationship between estimated richness of secondary pests and estimated richness of natural enemies in the studied cornfields.

Fig. 11 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?

Fig. 11. Estimated diversity secondary pests (S.P.) and natural enemies (N.E.) in conventional and transgenic maize whorls for Cry1Ab, Cry1F and combined Cry2Ab2 and Cry1A105 proteins (A) and estimated diversity in conventional maize and Bt maize (B) in different counties in Minas Gerais. Bars represent 95% confidence interval.

Fig. 9 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?

Fig. 9. Estimated richness of insects in the whorl and tassel of conventional (Conv) and transgenic maize (Cry1Ab and Cry1F proteins) from Matozinhos county, MG. Bars represent 95% confidence interval.

Fig. 6 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?

Fig. 6. Estimated richness of insects in the whorl, ear and tassel of conventional (Conv.) and transgenic maize (Cry1Ab, Cry1F and combined Cry2Ab2 and Cry1A105 proteins) from Três Corações county, MG. Bars represent 95% confidence interval.

Fig. 3 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?

Fig. 3. Estimated richness of insects in the whorl, ear and tassel of conventional (Conv.) and transgenic maize (Cry1Ab, Cry1F and combined Cry2Ab2 and Cry1A105 proteins) from Nazareno county, MG. Bars represent 95% confidence interval.

Fig. 1 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?

Fig. 1. Abundance of different sizes of larvae of Spodoptera frugiperda in whorls of conventional and transgenic maize (Cry1Ab, Cry1F and combined Cry2Ab2 and Cry1A105 proteins) from cornfields of different counties in Minas Gerais. Bars represent a 95% confidence interval.

Fig. 5 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?

Fig. 5. Estimated richness of insects in the whorl, ear and tassel of conventional (Conv.) and transgenic maize (Cry1Ab and Cry1F proteins) from Iraí de Minas Gerais county. Bars represent 95% confidence interval.

Fig. 14 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?

Fig. 14. Estimated richness of secondary pests (S.P.) and natural enemies (N.E.) in tassels of conventional and transgenic maize for Cry1Ab, Cry1F and combined Cry2Ab2 and Cry1A105 proteins (A) and estimated richness in conventional maize and Bt maize (B), in different counties in Minas Gerais.Bars represent 95% confidence interval.

Fig. 13 in Does Bt maize cultivation affect the non-target insect community in the agro ecosystem?

Fig. 13. Estimated diversity of secondary pests (S.P.) and natural enemies (N.E.) in ears of conventional and transgenic maize for Cry1Ab, Cry1F and combined Cry2Ab2 and Cry1A105 proteins (A) and estimated diversity in conventional maize and Bt maize (B), in different counties in Minas Gerais. Bars represent 95% confidence interval.

opencc-by-4.0Aug 2024View details →

UrbanOccupationsOETR_temettuat_cultivation_CLC_6_region_rural_geosample

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, <a href="https://urbanoccupations.ku.edu.tr/">UrbanOccupationsOETR</a>) 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. 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 Tanzimat 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 temettuat 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 temettuat 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 ancienne regime agricultural dynamics due to the lack of modern means of mechanization, irrigation, and fertilization combined with extremely rudimentary transport facilities. 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). Our geosampling methodology of the 1840s tax registers (temettuat) is based on contemporary Ottoman population registers. With this geosampling method, we aim to estimate the regional (district (sancak) and subdistrict (kaza)) 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) (temettuat) surveys for crop type and cultivation area and the population (nüfus) (NFS) registers for population-based sampling. Connectivity is based on a detailed and provenly accurate 1940s German military map of Turkey, Deutsche Heereskarte (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. 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. After geosampling locations, our objective extended to deriving estimates for the total cultivated area within each subdistrict and region. To achieve this goal, it was imperative that the data undergoes coding the cultivation areas into a standardized and comparable land-use scheme. We adopted the Corine Land Cover (CLC) nomenclature from the European Union's Earth Observation Programme (Copernicus), established in 1985 and regularly updated. Our study followed the revised guidelines issued by the European Environment Agency on 10.05.2019. Despite its primary design for contemporary land cover analysis, CLC nomenclature proved well-suited for accurately representing the agricultural tax data and the historical context of the 1845 Ottoman tax surveys. In our analysis, we coded micro-level cultivated land entries associated with individual households by using CLC's highest detail level. Successfully, every cultivated land entry was coded into the third level of detail in CLC, encompassing sub-categories such as 2.1 – “Arable land”, 2.2 – “Permanent crops”, 2.3 – “Pastures”, and 2.4 – “Heterogeneous agricultural areas”—all falling under the overarching category of 2 - Agricultural areas. Additionally, we coded entries related to 3.1 - “Forest” and 3.2 – “Shrub and/or herbaceous vegetation associations”, falling under the primary category of 3 – “Forest and seminatural areas.” Finally, cultivation areas expressed in Ottoman measurement units like dönüm (1/9,2 of a hectare) were converted into hectares to ensure consistency and ease of spatiotemporal comparison. This Zenodo dataset offers agricultural data for the entire geosampling area per household. It includes 39,002 agricultural entries coded according to CLC, detailing both the quantity and the cultivated area, corresponding to 14,997 individuals across 13,564 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. The categories and descriptions of the variables of the geosample dataset are as follows:   <table> <tbody> <tr> <td> Variable </td> <td> Description </td> </tr> <tr> <td>   “GeoCode” </td> <td>   UniqueID belonging to a specific geosampled location </td> </tr> <tr> <td>  “Longitude” & “Latitude” </td> <td> Geographical coordinates used to specify the precise location of a geosampled location on the Earth's surface </td> </tr> <tr> <td> “Region” & “SubDistrict” & “Location” </td> <td> Geographic unit of entry, including region (district/sancak); subdistrict (kaza); and geosampled location as they appear in the population registers </td> </tr> <tr> <td> “RegisterNo” </td> <td> Archival code of the population register whose data is being entered </td> </tr> <tr> <td> “HaneNo” </td> <td> Number of the household (specified by the registers as Hane), as appears in the register </td> </tr> <tr> <td> "HouseID" </td> <td> Unique ID belonging to a specific household, automatically generated by Microsoft Access </td> </tr> <tr> <td> "IndividualID" </td> <td> Unique ID belonging to a specific individual, automatically generated by Microsoft Access </td> </tr> <tr> <td> "AgrID" </td> <td> UniqueID belonging to a specific agricultural asset / crop belonging to an individual </td> </tr> <tr> <td> "Cultivation" </td> <td> Type of the agricultural asset / crop </td> </tr> <tr> <td> "CLC_Cultivation_Code" </td> <td> CLC-code of the agricultural asset / crop </td> </tr> <tr> <td> "CtgUnit" </td> <td> Is applicable when the quantity of an agricultural asset or crop is specified using specific terms ("aded", "res", "eşcar", "sak" [usually for individual trees]), and when the area of an agricultural asset or crop is described in vague terms ("bab", "kıta" [usually for  fields, gardens, and vineyards]) </td> </tr> <tr> <td> "Unit" </td> <td> Quantity of the "CategoryUnit" </td> </tr> <tr> <td> "CtgArea" </td> <td> The land area type of the agricultural asset / crop in Ottoman measurement units, "Dönüm" and “Evlek” (1/4 of a “Dönüm”) </td> </tr> <tr> <td> "Area" </td> <td> Quantity of the "CategoryArea" </td> </tr> <tr> <td> “Area_Ottoman_dönüm” </td> <td> "Area" converted into Ottoman “Dönüm” </td> </tr> <tr> <td> “Area_hectare” </td> <td> "Area" converted into hectares </td> </tr> </tbody> </table>

Figure 5 in A reliable method for quick comparisons of enchytraeid (Oligochaeta) densities in soil and their seasonal changes under cultivated and natural fields in central Greece

Figure 5. Principal component analysis of the samples collected from two sites, alfalfa field and boundary zone based on nine soil properties.

Figure 4 in A reliable method for quick comparisons of enchytraeid (Oligochaeta) densities in soil and their seasonal changes under cultivated and natural fields in central Greece

Figure 4. Seasonal changes in total precipitation and mean monthly temperature in Kopaida valley during the period April 2021 – March 2022.

Figure 3 in A reliable method for quick comparisons of enchytraeid (Oligochaeta) densities in soil and their seasonal changes under cultivated and natural fields in central Greece

Figure 3 depicts the monthly changes of the mean soil moisture of all three soil depths and the instant soil temperature at the sampling time at 10 cm depth. It is obvious that these two parameters altered identically in the two fields and only small differences can be detected, e.g. the rise in soil moisture in July in the alfalfa field due to the application of irrigation water.

Figure 3 in A reliable method for quick comparisons of enchytraeid (Oligochaeta) densities in soil and their seasonal changes under cultivated and natural fields in central Greece

Figure 3. Seasonal fluctuations of the mean soil moisture up to 15 cm depth and of the soil temperature at 10 cm depth in the alfalfa plantation and its boundary zone in Kopaida valley.

Figure 1 in A reliable method for quick comparisons of enchytraeid (Oligochaeta) densities in soil and their seasonal changes under cultivated and natural fields in central Greece

Figure 1. Left: Extraction efficiency (enchytraeid individuals m-2) of sucrose centrifugation, wet extraction with filter and wet extraction without filter. Bars and error bars denote means and 95% confidence intervals respectively. Means that are significantly different in multiple comparisons using Wilcoxon test are represented by different letters above bars (P <0.05). Right: Relationship between Enchytraeidae populations extracted with wet extraction without filter and a) sucrose centrifugation, and b) wet extraction with filter.