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Fig. 5. – Grimmia elongata Kaulf. A in A revision of Grimmia (Grimmiaceae) from South Africa and Lesotho
Fig. 5. – Grimmia elongata Kaulf. A. Transverse section of stem, with central strand; B. Transverse section of stem, without central strand; C, D. Leaves; E. Outlines of transverse section of leaf; F. Cells in leaf base and transitional part; G. Dorsal cells of costa in apical part; H. Transverse section of leaf. [A, B, E: Hedderson 15449, BOL; C, F: Hedderson 15448, BOL; D, H: Hedderson 15455, BOL; G: Matcham 1094a, herb. Matcham]
Fig. 4 in A revision of Grimmia (Grimmiaceae) from South Africa and Lesotho
Fig. 4. – Grimmia donniana Sm. A. Transverse section of stem; B. Leaf; C. Outlines of transverse section of leaf; D. Cells in leaf base and transitional part; E. Transverse section of leaf.
Fig. 11. – Grimmia orbicularis Wilson. A in A revision of Grimmia (Grimmiaceae) from South Africa and Lesotho
Fig. 11. – Grimmia orbicularis Wilson. A. Transverse section of stem; B, C. Leaves; D. Outlines of transverse section of leaf; E, G. Cells in leaf base; F. Cells in transitional part; H. Transverse section of leaf. [A: Hedderson 15285, BOL; B, E: MacOwen s.n., BM; C, F-H: Hedderson 15264, BOL; D, Hedderson 13047, BOL]
Fig. 9 in A revision of Grimmia (Grimmiaceae) from South Africa and Lesotho
Fig. 9. – Grimmia longirostris Hook.: A. Transverse section of stem; B. Leaf; C. Outlines of transverse section of leaf; D. Cells in leaf base; E. Cells in transitional part; F. Transverse section of leaf[A: Esterhuysen 21637, BOL; B-E: Schelpe 2116, BOL; F: Esterhuysen 35931, BOL]
Fig. 6. – Grimmia fuscolutea Hook. A in A revision of Grimmia (Grimmiaceae) from South Africa and Lesotho
Fig. 6. – Grimmia fuscolutea Hook. A. Transverse section of stem; B. Leaf; C. Outlines of transverse sections of leaf; D. Cells in leaf base; E. Cells in transitional part; F, G, H. Transverse sections of leaves. [A-E, H: Hilliard & Burtt 7115, BOL; F: Schelpe 7682, BOL; G: Schelpe 2115, PRE]
Fig. 14 in A revision of Grimmia (Grimmiaceae) from South Africa and Lesotho
Fig. 14. – Grimmia sessitana De Not. A. Transverse section of stem; B. Leaf; C. Outlines of transverse section of leaf; D. Cells in leaf base of upper stem leaf; E. Cells in leaf base of lower stem leaf; F. Transverse section of leaf. [A, C, F: Esterhuysen 20971, BOL; B, D, E: Schelpe 8037, BOL]
Fig. 8 in A revision of Grimmia (Grimmiaceae) from South Africa and Lesotho
Fig. 8. – Grimmia laevigata (Brid.) Brid. A. Transverse section of stem; B, C. Leaves; D. Outlines of transverse section of leaf; E. Cells in leaf base; F, G. Transverse sections of leaves. [A, C, D-F: Hedderson 13782, BOL; B: Hedderson 13219, BOL; C, G: Lübenau SA 62, STU]
Fig. 13 in A revision of Grimmia (Grimmiaceae) from South Africa and Lesotho
Fig. 13. – Grimmia pygmaea Müll. Hal. A. Transverse section of stem; B, C. Leaves; D, E. Outlines of transverse sections of leaves; F. Cells in leaf base; G. Cells in transitional part; H. Cells in leaf base; I. Cells in transitional part; J, K. Transverse sections of leaves. [A: Duckett & Matcham 1246a, Herb. Matcham; B, D, F, G, J: Esterhuysen 35934, BOL; C, E, H, I: Hilliard & Burtt, BM; K: Magill 4204, BOL]
Fig. 12 in A revision of Grimmia (Grimmiaceae) from South Africa and Lesotho
Fig. 12. – Grimmia pulvinata (Hedw.) Sm. A. Transverse section of stem; B, C. Leaves; D, E. Outlines of transverse sections of leaves; F, G. Cells in leaf base; H, I. Transverse sections of leaves. [A, D, H: Hedderson 13601, BOL; C, F: Hedderson 13148, BOL; B, E, I: Hedderson 13851, BOL; G: Hedderson 13677, BOL]
Fig. 10 in A revision of Grimmia (Grimmiaceae) from South Africa and Lesotho
Fig. 10. – Grimmia montana Bruch & Schimp. A. Transverse section of stem; B. Leaf; C. Hair-point; D, E. Outlines of transverse sections of leaves; F. Cells in leaf base; G. Cells at margin above shoulder; H, I. Transverse sections of leaves; J, K. Transverse sections of costae at insertion. [A, C: Hedderson 13706, BOL; B, F, G, I: Hedderson 14491, BOL; D, J: Hedderson 14482, BOL; E: Hedderson 13645, BOL; H: Hedderson 14990, BOL; K: Hedderson 14501, BOL]
Fig. 3 in A revision of Grimmia (Grimmiaceae) from South Africa and Lesotho
Fig. 3. – Grimmia consobrina Müll. Hal. A. Transverse section of stem; B. Leaves; C. Outlines of transverse section of leaf; D. Cells in leaf base; E. Cells in transitional part of leaf; F-G. Transverse sections of leaves. [A-B, G: Hedderson 13754, BOL; C: Hedderson 13726, BOL; D-E: Hedderson 13088, BOL; F: Hedderson 13678, BOL].
Fig. 6 in Neoclita pringlei (Scarabaeidae, Cetoniinae), a new relict genus and species from the Drakensberg Range of South Africa
Fig. 6. Matatiele Nature Reserve, showing the mountain summit with the typical habitat of Neoclita pringlei gen. et sp. nov. (photo: Lynette Clennell, Matatiele, 6 Dec. 2008).
Fig. 5 in Neoclita pringlei (Scarabaeidae, Cetoniinae), a new relict genus and species from the Drakensberg Range of South Africa
Fig. 5. Neoclita pringlei gen. et sp. nov., ♀, specimen in its natural habitat (photo: Lynette Clennell, Matatiele, 6 Dec. 2018).
Fig. 4 in Neoclita pringlei (Scarabaeidae, Cetoniinae), a new relict genus and species from the Drakensberg Range of South Africa
Fig. 4. Neoclita pringlei gen. et sp. nov., ♂, specimen in its natural habitat (photo: Lynette Clennell, Matatiele, 6 Dec. 2018).
Fig. 3 in Neoclita pringlei (Scarabaeidae, Cetoniinae), a new relict genus and species from the Drakensberg Range of South Africa
Fig. 3. Neoclita pringlei gen. et sp. nov., paratype, ♀, total length = 16.8 mm. A. Habitus, dorsal view. B. Habitus, ventral view. (South Africa, Eastern Cape Province, Matatiele Nature Reserve, 6 Dec. 2008, R. Perissinotto and L. Clennell leg.)
Fig. 2 in Neoclita pringlei (Scarabaeidae, Cetoniinae), a new relict genus and species from the Drakensberg Range of South Africa
Fig. 2. Neoclita pringlei gen. et sp. nov., holotype, ♂. A. Aedeagus, dorsal view. B. Aedeagus, lateral view. (South Africa, Eastern Cape Province, Matatiele Nature Reserve, 6 Dec. 2008, R. Perissinotto and L. Clennell leg.)
Fig. 7. Dordrecht Mountain, where a in Neoclita pringlei (Scarabaeidae, Cetoniinae), a new relict genus and species from the Drakensberg Range of South Africa
Fig. 7. Dordrecht Mountain, where a second population of Neoclita pringlei gen. et sp. nov. was frst recorded in Dec. 2013 (photo: Lynette Clennell, Dordrecht, 31 Dec. 2015).
Fig. 1 in Neoclita pringlei (Scarabaeidae, Cetoniinae), a new relict genus and species from the Drakensberg Range of South Africa
Fig. 1. Neoclita pringlei gen. et sp. nov., holotype, ♂, total length = 16.3 mm. A. Habitus, dorsal view. B. Habitus, ventral view. (South Africa, Eastern Cape Province, Matatiele Nature Reserve, 6 Dec. 2008, R. Perissinotto and L. Clennell leg.)
Open access in Africa: scopus citation data
<p>The following citation dataset was retrieved from Scopus in June 24, 2017 (3am, Western Indonesian time).</p> <p>It consists of 3 sets of data based on our searches. Each search was saved both in 'csv' and 'bib':</p> <ol> <li>OA_Africa_inTitle.xxx: "Open Access" AND Africa IN TITLE</li> <li>OA_Africa_inTitle_inAbstract_inKeywords.xxx: "Open Access" AND Africa IN TITLE, IN ABSTRACT, IN KEYWORDS</li> <li>OAmovement_Africa_inTitle_inAbstract_inKeywords.xxx: "Open Access movement" AND Africa IN TITLE, IN ABSTRACT, IN KEYWORDS</li> </ol> <p>The access to Scopus was provided by The Central Library of Institut Teknologi Bandung (Indonesia)</p>
Maize management and yield of smallholder farmers in Sub-Saharan Africa between 2016 and 2022
<p>Yield and management practices data were collected from smallholders’ maize fields from 2016 to 2022. All fields corresponded to maize grown in pure stands (no intercropping). Data were collected from five maize producing regions in Sub-Saharan Africa: (i) north-central Nigeria (<em>n</em> = 115), (ii) Rwanda and Burundi (<em>n</em> = 2720), (iii) central Zambia (<em>n</em> = 861)<strong>,</strong> (iv) southwest Tanzania (<em>n</em> = 3710), and (v) eastern Uganda and western Kenya (<em>n</em> = 7367). Data were collected by One Acre Fund (https://oneacrefund.org/), an NGO that provides smallholder farmers access to agricultural training, credit, crop insurance services, and farming supplies. About half of the fields in the database comprised farmers who subscribed to the One Acre Fund program and the other half farmers who did not. </p> <p>Maize grain yield, plant density, and row spacing were measured in two randomly placed boxes of 36 square meters at harvest, avoiding field edges. Field geolocation was recorded in 70% of the observations. When missing, the field geolocation was defined based on the nearby town (21%) or associated district (9%) location for the purpose of retrieving climate data. Management practices associated with each field were reported by farmers, including sowing and harvest dates, cultivar name, fertilizer inputs (types and total quantities for both organic and inorganic), fertilization method, liming, weeding, and pesticides (mainly insecticides to control fall armyworms). Farmers also reported the incidence of adversities (such as pests, diseases, Striga witchweed, hail, and excess water). Field size was reported by farmers and, in those cases in which farmers could not provide an accurate measure of their field size, or there was a strong indication of mistakes (e.g., nutrient fertilizer rates out of range), One Acre Fund personnel took in-situ measurements to determine field size. Input rates per hectare were calculated as the ratio of the farmer-reported input amount and field size. Data were subjected to quality control to remove unlikely values. Maize yield outliers were detected with a Bonferroni Outlier Test. Observations with plant densities and fertilizer rates higher than four standard deviations from the mean were excluded as well as those without geolocation, no N or P data, and atypical sowing dates. After quality control, the database contains a total of 14,773 field observations.</p> <p>Inorganic fertilizer rates were converted to nutrient rates (in elemental nutrients) following typical fertilizer nutrient contents. Organic fertilizers were encoded separately in two binary variables and one continuous variable, indicating whether compost was used, if that compost contained manure, and compost application rate. Likewise, cultivars were classified into hybrids or open pollination varieties (OPVs), which included local varieties, retained seed, and improved OPVs. For hybrids, we retrieved the associated crop cycle maturity (short, medium, and long), disease tolerance traits, and year of release from companies’ seed catalogs. Reported incidence of diseases and insect pests (e.g., anthracnose, aphids, blight, cutworms, drought, fall armyworm, stemborer, termites, and stalk or kernel rot) were simplified to two binary variables indicating whether the crop was affected by pests and/or diseases. Infestation by parasitic witchweeds (Striga hermonthica and S. asiatica) was considered as a separate variable. Fertilization methods were also simplified to whether the fertilizer was applied inside a hole or broadcasted in the surface. Number of weeding operations was simplified to zero, one or two or more weeding per season. Sowing dates were expressed as a deviation from the estimated average sowing date for each climate zone-season combination. Fields were grouped based on their location using the climate zone scheme developed by the Global Yield Gap Atlas Project (www.yieldgap.org). Isolated observations (more than three standard deviations from the median distance across sites within the climate zone) were excluded from their group. In the case of climate zones with two maize seasons, each crop season was considered as a separate group. Field elevation was retrieved from the Amazon Web Services Terrain Tiles. Total precipitation during the growing season, as well as for early, flowering, and grain filling phases, was retrieved from CHIRP. For observations with field-level coordinates data, root-zone plant-available water-holding capacity was retrieved from the World Soil Information database, and soil clay content, pH, organic carbon, and effective cation exchange capacity from iSDA. Lastly, the topography wetness index (TWI) was calculated from the elevation data. </p> <p>Table 1. List of survey-derived variables.</p> <div> <div> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Type</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>plant_date_dev</td> <td>discrete</td> <td>days</td> <td>sowing date deviation from cluster average</td> </tr> <tr> <td>pl_m2</td> <td>continuous</td> <td># m2</td> <td>plant density (plants per area)</td> </tr> <tr> <td>row_spacing</td> <td>continuous</td> <td>cm</td> <td>distance between rows</td> </tr> <tr> <td>hybrid</td> <td>binary</td> <td>-</td> <td>Was a commercial hybrid seed used?</td> </tr> <tr> <td>hyb_mat</td> <td>ordinal</td> <td>-</td> <td>hybrid maturity (early, medium, late)</td> </tr> <tr> <td>hyb_yor</td> <td>continuous</td> <td>-</td> <td>Year of release of the cultivar</td> </tr> <tr> <td>hyb_tol_mln</td> <td>binary</td> <td>-</td> <td>Tolerance to maize lethal necrosis</td> </tr> <tr> <td>hyb_tol_msv</td> <td>binary</td> <td>-</td> <td>Tolerance to maize streak virus</td> </tr> <tr> <td>hyb_tol_gls</td> <td>binary</td> <td>-</td> <td>Tolerance to gray leaf spot</td> </tr> <tr> <td>hyb_tol_nclb</td> <td>binary</td> <td>-</td> <td>Tolerance to northern corn leaf blight</td> </tr> <tr> <td>hyb_tol_rust</td> <td>binary</td> <td>-</td> <td>Tolerance to rust</td> </tr> <tr> <td>hyb_tol_ear_rot</td> <td>binary</td> <td>-</td> <td>Tolerance to ear rot</td> </tr> <tr> <td>N_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>N fertilization rate</td> </tr> <tr> <td>P_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>P fertilization rate</td> </tr> <tr> <td>K_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>K fertilization rate</td> </tr> <tr> <td>compost</td> <td>binary</td> <td>-</td> <td>Was compost applied?</td> </tr> <tr> <td>comp_t_ha</td> <td>continuous</td> <td>t/ha</td> <td>compost rate</td> </tr> <tr> <td>manure</td> <td>binary</td> <td>-</td> <td>Did the compost contain manure?</td> </tr> <tr> <td>fert_in_hole</td> <td>binary</td> <td>-</td> <td>Was the fertilizer applied in a hole?</td> </tr> <tr> <td>lime_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>lime rate</td> </tr> <tr> <td>weeding</td> <td>discrete</td> <td>#</td> <td>number of times the plot was weeded</td> </tr> <tr> <td>pesticide</td> <td>binary</td> <td>-</td> <td>Was any pesticide applied?</td> </tr> <tr> <td>disease</td> <td>binary</td> <td>-</td> <td>Was yield affected by diseases?</td> </tr> <tr> <td>pest</td> <td>binary</td> <td>-</td> <td>Was yield affected by pests?</td> </tr> <tr> <td>striga</td> <td>binary</td> <td>-</td> <td>Was yield affected by the Striga weed?</td> </tr> <tr> <td>water_excess</td> <td>binary</td> <td>-</td> <td>Was yield affected by water excess (heavy rain or flooding)?</td> </tr> </tbody> </table> <p> </p> <p><strong>Table 2. List of environmental variables. </strong></p> <div> </div> <div> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Unit</strong></td> <td><strong>Spatial resolution</strong></td> <td><strong>Description</strong></td> <td><strong>Source</strong></td> </tr> <tr> <td>GDD</td> <td>°C days</td> <td>30 arc-sec (1km)</td> <td>Growing degree days</td> <td>www.worldclim.org</td> </tr> <tr> <td>AI</td> <td>unitless</td> <td>30 arc-sec (1km)</td> <td>Aridity Index (annual precipitation over potential evapotranspiration)</td> <td>www.worldclim.org</td> </tr> <tr> <td>TS</td> <td>°C</td> <td>30 arc-sec (1km)</td> <td>Temperature seasonality</td> <td>www.worldclim.org</td> </tr> <tr> <td>season_prec</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Total rainfall during the maize season (10% of planting to 50% of the harvest)</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>season_prec_1</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Rainfall during the first third of the season</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>season_prec_2</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Rainfall during the second third of the season</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>season_prec_3</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Rainfall during the last third of the season</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>elev</td> <td>m.a.s.l.</td> <td>75 meters</td> <td>Elevation (altitude) above sea level</td> <td>registry.opend26ata.aws/terrain-tiles</td> </tr> <tr> <td>soil_rzpawhc</td> <td>mm</td> <td>1 km</td> <td>Root zone plant-available water holding capacity</td> <td>www.isric.org</td> </tr> <tr> <td>soil_clay</td> <td>%</td> <td>30 meters</td> <td>Clay content at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>soil_pH</td> <td>-</td> <td>30 meters</td> <td>pH (H2O) at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>soil_orgC</td> <td>g/kg</td> <td>30 meters</td> <td>Organic carbon at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>soil_ECEC</td> <td>cmolc/kg</td> <td>30 meters</td> <td>Effective cation exchange capacity at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>twi</td> <td>unitless</td> <td>75 meters</td> <td>Topographic Wetness Index</td> <td>calculated from elevation</td> </tr> </tbody> </table> </div> </div> </div>
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.