Find research datasets worth reusing

opencc-by-4.0Nov 2016View details →

Fig. 1 in A new R package and web application for detecting bilateral asymmetry in parasitic infections

Fig. 1. Screenshot of the web application. The panel on the left contains the controls for the application, including file upload, selection of test, choice of multiplicity correction and significance threshold. Results are displayed on the four tabbed pages of the main panel: summary, individual hosts, histogram and volcano plot.

FIG. 3 in New data on the distribution of the genus Roeseliana Zeuner, 1941 (Orthoptera, Tettigoniidae, Tettigoniinae) in the southwestern Balkans, with description of R. epirotica n. sp.

FIG. 3. — Titillators of Roeseliana epirotica n. sp. ♂: A, dorsal view; B, side view on the right. Not to scale.

FIG. 2 in New data on the distribution of the genus Roeseliana Zeuner, 1941 (Orthoptera, Tettigoniidae, Tettigoniinae) in the southwestern Balkans, with description of R. epirotica n. sp.

FIG. 2. — Subgenital plates of females of species with closest distribution: A, Roeseliana epirotica n. sp. ♀ (the plate of the female from Albania is strictly identical); B, Roeseliana ambitiosa (Uvarov, 1924) ♀ from the Republic of North Macedonia; C, Roeseliana ambitiosa ♀ from Paramithia, located at only 10 km from R. epirotica n. sp. type locality. Not to scale. Photo: C, K. G. Heller.

FIG. 1 in New data on the distribution of the genus Roeseliana Zeuner, 1941 (Orthoptera, Tettigoniidae, Tettigoniinae) in the southwestern Balkans, with description of R. epirotica n. sp.

FIG. 1. — Map of known localities of Roeseliana Zeuner, 1941 genus. Symbols: triangle, Roeseliana bispina (Bolivar, 1899);circle, Roeseliana ambitiosa (Uvarov, 1924); crossed out circle, former localities of R. ambitiosa to be checked; concentric circles, Roeseliana epirotica n. sp.

FIG. 4 in New data on the distribution of the genus Roeseliana Zeuner, 1941 (Orthoptera, Tettigoniidae, Tettigoniinae) in the southwestern Balkans, with description of R. epirotica n. sp.

FIG. 4. — Comparison of the titillators of the two geographically close species: A, Roeseliana epirotica n. sp. ♂; B, Roeseliana ambitiosa (Uvarov, 1924) ♂.

opencc-by-4.0Aug 2023View details →

Dataset and R script: a longitudinal hospital study of febrile children in Ghana

This is the dataset and R script accompanying the manuscript "Fever in focus: symptoms, diagnoses, and treatment of febrile children in Ghana, a longitudinal hospital study."

opencc-by-4.0Aug 2023View details →

Fig. 4. Merianthera calyptrata R in Merianthera calyptrata sp. nov. (Melastomataceae, Myrtales), a new candelabriform species from Minas Gerais, Brazil

Fig. 4. Merianthera calyptrata R.Goldenb., Bochorny & Fraga sp. nov. in its habitat. A. Inselberg and granitic outcrops where the plant can be found. B. Vegetation cluster growing on a cleft, on a granitic outcrop, with M. calyptrata to the right and front, with flowers. C. Three individuals with spreading crowns, behind some Alcantarea (É.Morren ex Mez) Harms (Bromeliaceae). From Fraga & Couto 4144. Photos by C.N. Fraga.

opencc-by-4.0Aug 2023View details →

Fig. 3. Merianthera calyptrata R in Merianthera calyptrata sp. nov. (Melastomataceae, Myrtales), a new candelabriform species from Minas Gerais, Brazil

Fig. 3. Merianthera calyptrata R.Goldenb., Bochorny & Fraga sp. nov., scanning electron micrographs of the abaxial surface of the leaf. A. General view. B. Detail of the dense vermiform trichomes (arrow) mixed with sessile glands (arrowhead). Scale bars: A = 200 µm; B = 100 µm. From A.M Amorim et al. 11854 (UPCB).

opencc-by-4.0Dec 2021View details →

mapspamc_db: a database with global spatial datasets to support the implementation of the mapspamc R package.

This repository contains the mapspamc database (mapspamc_db), a collection of global spatial datasets to support the implementation of the  <a href="https://github.com/michielvandijk/mapspamc">mapspamc</a> R package. The database also includes subnational crop statistics and matching country shapefiles for several country examples. For more information on how to use the mapspamc package in combination with mapspamc_db, see the <a href="https://michielvandijk.github.io/mapspamc/">mapspamc documentation</a>. Detailed information on the contents of mapspam_db, such as the sources of information and pre-processing is described in the mapspamc_db documentation (pdf file) that is part of the repository.

F I G U R E 2 in Small and in-country herbaria are vital for accurate plant threat assessments: A case study from Peru

F I G U R E 2 The effect of including data from Peruvian herbaria upon estimated International Union for Conservation of Nature (IUCN) threat assessments. The percentage of species assessed as DD (Data Deficient, grey), LC (Least Concern, green), VU (Vulnerable, yellow), EN (Endangered, orange), and CR (Critically Endangered, red) based upon data from: Top, international herbaria, and bottom, Peruvian and international herbaria.

F I G U R E 3 in Small and in-country herbaria are vital for accurate plant threat assessments: A case study from Peru

F I G U R E 3 The effect of including data from different small and large herbaria upon estimated International Union for Conservation of Nature (IUCN) threat assessments. The percentage of species assessed as DD (Data Deficient, grey), LC (Least Concern, green), VU (Vulnerable, yellow), EN (Endangered, orange), and CR (Critically Endangered, red) based upon data from: top, large herbaria only (>1,000,000 sheets); middle, large and medium herbaria (>500,000 sheets); and bottom, large, medium, and small herbaria (all herbaria).

F I G U R E 1 in Small and in-country herbaria are vital for accurate plant threat assessments: A case study from Peru

F I G U R E 1 The proportion of specimens of Peruvian Begonia and Solanum held in Peruvian institutions (blue) versus international institutions (orange) in (a) the Global Biodiversity Information Facility (GBIF); (b) the Begonia Resource Centre and Solanaceae Source (see text for doi references for all datasets).

F I G U R E 4 in Small and in-country herbaria are vital for accurate plant threat assessments: A case study from Peru

F I G U R E 4 The effect of including data from herbaria in size order upon estimated International Union for Conservation of Nature (IUCN) threat assessments. (a) Percentage of species assessed as DD (Data Deficient, grey), LC (Least Concern, green), VU (Vulnerable, yellow), EN (Endangered, orange), and CR (Critically Endangered, red); (b) the size (number of accessions) of herbaria added (orange) and number of specimen records included (blue); (c) the mean range of species considered threatened when all data are considered. Dashed, vertical lines indicate the limits of large (>1,000,000 sheets) and medium (>500,000 sheets) herbaria. Grey shading indicates Peruvian herbaria. Selected herbaria are indicated in C. Herbarium abbreviations in panels B and C follow Thiers (2001).

opencc-by-4.0Nov 1997View details →

Figs 6-10. Saropogon incisuratus Wulp. 6-7. Head, lectotype r in Afrotropical Asilidae (Diptera) 29. A review of the genus Saropogon Loew, 1847 (Dasypogoninae)

Figs 6-10. Saropogon incisuratus Wulp. 6-7. Head, lectotype r:J (Lahej). 6. Lateral. 7. Anterior. 8-10. r:J genitalia, paralectotype () (Lahej). 8. Lateral. 9. Dorsal. 10. Ventral. Scale lines equal 1 mrn

openother-openSep 2023View details →

Dataset and R script used in "Local knowledge reconstructs historical resource use" published in Frontiers in Ecology and the Environment

Data and R code used in: L. Castello, E.G. Martins, M. Sorice, E. Smith, M. Almeida, G.C.C. Bastos, L.G. Cardoso, M. Clauzet, A.P. Dopona, B. Ferreira, M. Haimovici, M. Jorge, J. Mendonça, A.O. Ávila-da-Silva, A.P.O. Roman, M. Ramires, L.M. Villwock, P.F.M. Lopes. (Year) Local knowledge reconstructs historical resource use. Frontiers in Ecology and the Environment.

dryad40/100

R codes from: Breakdown of the growth–mortality trade-off along a soil phosphorus gradient in diverse neotropical forest

An ecological paradigm predicts that plant species adapted to low resource availability grow slower and live longer than those adapted to high resource availability when growing together. We tested this by using hierarchical Bayesian analysis to quantify variations in growth and mortality of ~ 40,000 individual trees from > 400 species in response to limiting resources in the tropical forests of Panama. In contrast to theoretical expectations of the growth–mortality paradigm, we find that tropical tree species restricted to low-phosphorus soils simultaneously achieve faster growth rates and lower mortality rates than species restricted to high-phosphorus soils. This result demonstrates that adaptation to phosphorus limitation in diverse plant communities modifies the growth–mortality trade-off, with important implications for understanding long-term ecosystem dynamics.

opencc-zeroSep 2023View details →

opencc-zeroSep 2023View details →

FIG. 2. — Oxneriaria immersa H.S.Asghar, R in New species of genus Oxneriaria S.Y.Kondr. & Lőkös (lichenized Ascomycota, Megasporaceae) from Khyber Pakhtunkhwa, Pakistan

FIG. 2. — Oxneriaria immersa H.S.Asghar, R.Zulfiqar & Khalid, sp. nov. (holotype): A, crustose-areolate thallus; B, apothecia; C, cross section of an apothecium; D, ascospores. Scale bars: A, 1 cm; B, 0.5 mm; C, 50 µm; D, 15 µm.

opencc-zeroSep 2023View details →

FIG. 3. — Oxneriaria insignis R in New species of genus Oxneriaria S.Y.Kondr. & Lőkös (lichenized Ascomycota, Megasporaceae) from Khyber Pakhtunkhwa, Pakistan

FIG. 3. — Oxneriaria insignis R.Zulfiqar & Khalid, sp. nov. (holotype): A, crustose-areolate thallus; B, apothecia; C, cross section of an apothecium; D, ascospores. Scale bars: A, 1 cm; B, 0.5 mm; C, 150 µm; D, 15 µm.

Data and R scripts associated with Clark, Moles, Fazlioglu, Brandenburger & Hartley, "Rapid loss of phenotypic plasticity in the introduced range of the beach daisy, Arctotheca populifolia."

Data to accompany article accepted for publication in Journal of Ecology. "Rapid loss of phenotypic plasticity in the introduced range of the beach daisy, Arctotheca populifolia" By Charlie D. Clark, Angela T. Moles, Fatih Fazlioglu, Claire R. Brandenburger, & Stephen Hartley 1 zip file that contains the following:       2 datasets (xlsx format)       9 R scripts to run the analyses and produce figures

Data and R-scripts for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland

Introduction A new method for estimating carbon dioxide emissions from rained peatland forest soils was developed for the Greenhouse Gas Inventory of Finland (GHG inventory). The method is based on a set of models (Ojanen et al. 2014, Tuomi et al., 2009) that dynamically compile all relevant carbon inputs and outputs into a time series of soil CO2 emission. A complete description of the method is described in Alm et al. (2023). Here we present the input data and R-scripts (R Core Team, 2020) for computing the time series from year 1990 to 2022 of CO2 emission from soil in forest land on drained organic soil, like it was reported by the Finnish GHG inventory (Statistics Finland, 2023). Time series data The source of forest and area data is the Finnish National Forest Inventory (NFI) as a part of Luke Statutory Services. The NFI standing forest data in the data files includes annual country-wide estimates of mean basal area and standing biomass of Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H. Karst) and all the broadleaved forest trees combined. The data concerns forest land on drained organic soil only (class FRA 1 according to the FAO forest land definition). The NFI data for each year has been averaged by different drained peatland forest site types (FTYPE) and by inventory regions of southern and northern Finland. The areas and proportions of FTYPEs of all drained peatland “forests remaining forests” (i.e., forests that have not undergone another change in land use in the past 20 years) in southern and northern Finland (Alm et al., 2023), derived from NFI12 (2014–2018). Annual litter input from harvest residues was estimated using statistics of harvested stem volumes by species, collected and published by Luke (Luke statistics). The stem volumes were converted to whole trees and further to litter fractions and further to The share of residues remaining in forest is estimated by subtracting the amount of the logging residues collected for energy use, the data obtained from Luke statistics/energy. The biomass of live trees, annual litterfall from live trees aboveground and root litter belowground are derived from the National Forest Inventory of Finland (inventory rounds NFI8 to NFI13). The R-code also includes calculation of annual litter production from the harvesting residues. The regression-based transfer models, implemented in the R-code, also need meteorological time series inputs: The soil organic matter decomposition model (Ojanen et al. 2014) uses May-October mean temperature. Decomposition model yasso07 (Tuomi et al., 2009), applied for estimating the CO2 release by decomposition of harvesting residues and above ground litter from natural mortality, is constrained by annual temperature, annual temperature amplitude and annual precipitation. Starting from the original country-wide grid produced by the Finnish Meteorological Institute (FMI) the weather time series were spatially averaged so that the FMI weather grid values were collected from those locations where peatlands representing each FTYPE in southern and northern Finland were observed by the NFI, respectively. The pre-prepared input data are given in files, see Table 1 for descriptions.     Table 1. Description of input data files. <table> <tbody> <tr> <td> File </td> <td> Description of data </td> </tr> <tr> <td> basal.areas.csv </td> <td> Time series of years 1990-2022 for annual average basal area (m2 ha-1) by year, by peatland forest site type (peat_type) and by tree species or group (tree_type).   Values of peat_type correspond to FTYPE: 1  Herb-rich type 2  Vaccinium myrtillus type 4  Vaccinium vitis-idaea type 6  Dwarf shrub type 7  Cladina type   Values of tree species or group correspond to: 1  Scots pine 2  Norway spruce 3  Broadleaved species </td> </tr> <tr> <td> biomass.csv </td> <td> Time series of years 1990-2022 for annual biomass (biomass, t ha-1 of dry mass) by year, by biomass component, by tree species and by peatland forest site type (tkg).   Values of peat_type correspond to FTYPE: 1  Herb-rich type 2  Vaccinium myrtillus type 4  Vaccinium vitis-idaea type 6  Dwarf shrub type 7  Cladina type   </td> </tr> <tr> <td> dead_litter.csv </td> <td> Time series of years 1990-2022 of annual aboveground litter from dead wood: Harvesting residues and natural mortality combined (C, t ha-1 of dry mass; lognat_litter).   Values of region correspond to GHG inventory region: south  South Finland north  North Finland </td> </tr> <tr> <td> ghgi_litter.csv </td> <td> Time series of years 1990-2022 for litter AWEN-fractions (A=acid soluble, W=water soluble, E=ethanol soluble, N=non-soluble; C, t ha-1) by different litter types: Above-ground coarse woody litter (coarse_woody_litter), fine woody litter (fine_woody_litter), non-woody litter (non_woody_litter) by litter source and deposition type by region. “org” denotes organic soil.   Values of region correspond to GHG inventory region: south  South Finland north  North Finland   Values of ground correspond to litter deposition environment: above  Above-ground litter below  Below-ground litter </td> </tr> <tr> <td> lognat_decomp.csv </td> <td> Time series of years 1990-2022 for C, t ha-1 of dry mass, decomposed from logging residues and natural mortality by region.   Values of variable “region” correspond to GHG inventory region: south  South Finland north  North Finland </td> </tr> <tr> <td> logyasso_weather_data.csv </td> <td> Time series of years 1990-2022 for regional (region) precipitation sum (mm, sum_P), average annual temperature (°C, mean_T) and amplitude of the annual temperature (°C , ampli_T).   Values of region correspond to GHG inventory region: south  South Finland north  North Finland   </td> </tr> <tr> <td> total_area.csv </td> <td> Areas (ha) of drained peatland forests remaining forest land by region and peat_type.   Values of variable “region” correspond to GHG inventory region: south  South Finland north  North Finland   Values of peat_type correspond to FTYPE: 1  Herb-rich type 2  Vaccinium myrtillus type 4  Vaccinium vitis-idaea type 6  Dwarf shrub type 7  Cladina type   </td> </tr> <tr> <td> weather_data.csv </td> <td> Time series of years 1990-2022 for 30-year rolling mean temperature for the May-October period (roll_T) used by the soil decomposition models. The values are calculated for each FTYPE (peat_type) using their spatial distributions (see details in Alm et al., 2023).   Values of variable “region” correspond to GHG inventory region: south  South Finland north  North Finland   Values of peat_type correspond to FTYPE: 1  Herb-rich type 2  Vaccinium myrtillus type 4  Vaccinium vitis-idaea type 6  Dwarf shrub type 7  Cladina type   </td> </tr> </tbody> </table>   The R-scripts The scripts are an excerpt from the Finnish greenhouse gas inventory code set, applying the necessary pre-processed input data and producing the soil CO2 emissions for each FTYPE separately. The necessary R-packages (R Core Team, 2020) are managed in the script LIBRARIES.R. Guidance for running the R-scripts is given in the README.txt. References Alm, J., Wall, A., Myllykangas, J-P., Ojanen, P., Heikkinen, J., Henttonen, H. M., Laiho, R., Minkkinen, K., Tuomainen, T. and Mikola, J. A new method for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland. Biogeosciences https://doi.org/10.5194/bg-20-1-2023, 2023. LUKE Statistics <ul> <li>https://www.luke.fi/en/statistics/total-roundwood-removals-and-drain, last access 8.12.2022.</li> </ul> <ul> <li>https://www.luke.fi/en/statistics/commercial-fellings/commercial-fellings-72023. last access 8.12.2022.</li> </ul> Statistics Finland 2023. URL: https://unfccc.int/documents/627718 (last access 13.9.2023). Ojanen, P., Lehtonen, A., Heikkinen, J., Penttilä, T., and Minkkinen, K.: Soil CO2 balance and its uncertainty in forestry drained peatlands in Finland, Forest Ecol. Manage., 325, 60–73, 2014. R Core Team: R: A language and environment for statistical computing. R Foundation forStatistical Computing, Vienna, Austria, URL https://www.R-project.org, 2020. Tuomi, M., Thum, T., Järvinen, H., Fronzek, S., Berg, B., Harmon, M., Trofymow, J.A., Sevanto, S. and Liski, J.: Leaf litter decomposition - Estimates of global variability based on Yasso07 model, Ecol. Modell. 220 (23):3362-3371, 2009.