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FIGURE 4 in Copepods associated with scleractinian corals: a worldwide checklist and a case study of their impact on the reef-building coral Pocillopora damicornis (Linnaeus, 1758) (Pocilloporidae)
FIGURE 4. Principal component analysis (PCA) of square root-transformed data from five functional categories of copepods (see text for descriptions.) defined based on behaviour (endo-/ectoparasitic or benthic) and the structure of the feeding appendages (mandibles vs. siphon) at various sampling periods. PC1 accounted for 77.2% of the variability, and PC2 accounted for 13.7%.
FIGURE 2 in Copepods associated with scleractinian corals: a worldwide checklist and a case study of their impact on the reef-building coral Pocillopora damicornis (Linnaeus, 1758) (Pocilloporidae)
FIGURE 2. Relationships between copepod infection and the amount of resources (Symbiodinium densities and surface areas) provided by host corals. A–E: Mean densities of symbiotic copepods (A: Siphonostomatoida, B: Cyclopoida, C: Harpacticoida, D: all copepods) among 480 Pocillopora damicornis colonies with varying Symbiodinium densities. E–H: Relationship between mean densities of symbiotic copepods (E: Siphonostomatoida, F: Cyclopoida, G: Harpacticoida, H: all copepods) and surface areas of host corals.
FIGURE 1 in Copepods associated with scleractinian corals: a worldwide checklist and a case study of their impact on the reef-building coral Pocillopora damicornis (Linnaeus, 1758) (Pocilloporidae)
FIGURE 1. Variation in seawater temperature and Symbiodinium density (mean ± SE) observed in Pocillopora damicornis colonies of Nanwan Bay, Southern Taiwan between July 2007 and November 2008. Lowercase and uppercase letters (a, b, c, and d) refer to the results of Tukey's post-hoc comparisons of monthly temperature and Symbiodinium density means, respectively, as a significant effect of time was detected in the overall ANOVA models (p <0.05 for both parameters).
FIGURE 5 in Apseudes talpa revisited (Crustacea; Tanaidacea). The impact on apseudidaen systematics
FIGURE 5. Apseudes talpa, Portugal (reg # MMF41156-3)., ovigerous simultaneous hermaphrodite. A, pereopod 1; B, pereopod 2; C, pereopod 3; D, pereopod 4; D1, same, dactylus; E, pereopod 5; F, pereopod 6. Scale bars = 1 mm.
FIGURE 6 in Apseudes talpa revisited (Crustacea; Tanaidacea). The impact on apseudidaen systematics
FIGURE 6. Apseudes talpa, UK. A, Scilly Island, ovigerous simultaneous hermaphrodite, dorsal view. B, syntype NHMUK, 1950.8.6-8, Salcombe, Devon, non-ovigerous male, dorsal view. Scale bar = 1 mm.
FIGURE 2 in Apseudes talpa revisited (Crustacea; Tanaidacea). The impact on apseudidaen systematics
FIGURE 2. Apseudes talpa, Portugal (reg # MMF41156-3). A, non-ovigerous female/juvenile, B–E, ovigerous simultaneous hermaphrodite. A, lateral view, Scale bar = 1 mm; B, lateral shield of pleonites, dorsal view; C, antennae; D, pleopod; E, uropod. Scale bars = 1 mm.
FIGURE 4. Apseudes talpa, Portugal. A in Apseudes talpa revisited (Crustacea; Tanaidacea). The impact on apseudidaen systematics
FIGURE 4. Apseudes talpa, Portugal. A, cheliped from simultaneous hermaphrodite (reg # MMF41156-3). B, cheliped from non-ovigerous female/juvenile (reg # MMF41156-4). Scale bar = 1 mm.
FIGURE 3 in Apseudes talpa revisited (Crustacea; Tanaidacea). The impact on apseudidaen systematics
FIGURE 3. Apseudes talpa, Portugal (reg # MMF41156-3), ovigerous simultaneous hermaphrodite. A, labrum; B, left mandible; C, right mandible; C1, same, molar. D, labium; E, maxillule; F, maxilla; G, maxilliped; H, epignath. Scale bars = 0.5 mm.
FIGURE 7 in Apseudes talpa revisited (Crustacea; Tanaidacea). The impact on apseudidaen systematics
FIGURE 7. Apseudes talpa, UK, ovigerous simultaneous hermaphrodites. A, left mandible, Scilly Island. B, right mandible, same specimen. C, left mandible, body, syntype NHMUK, 1950.8.6-8, Salcombe, Devon. D, same, incisor. E, same, molar. Scale bar = 0.5 mm.
FIGURE 1. Apseudes talpa, Portugal. A in Apseudes talpa revisited (Crustacea; Tanaidacea). The impact on apseudidaen systematics
FIGURE 1. Apseudes talpa, Portugal. A, ovigerous simultaneous hermaphrodite, dorsal view (reg # MMF41154-1). B, Nonovigerous female/juvenile, dorsal view (reg # MMF41155-2). Scale bar = 1 mm.
FIGURE 3 in The identification of Rana ocellata Linnaeus, 1758. Nomenclatural impact on the species currently known as Leptodactylus ocellatus (Leptodactylidae) and Osteopilus brunneus (Gosse, 1851) (Hylidae)
FIGURE 3. Leptodactylus latrans (Steffen, 1815), neotype (MNRJ 30733). A: Dorsal view of head. B: Lateral view of head. C: Hand. D: Foot. Bars = 5 mm.
FIGURE 2 in The identification of Rana ocellata Linnaeus, 1758. Nomenclatural impact on the species currently known as Leptodactylus ocellatus (Leptodactylidae) and Osteopilus brunneus (Gosse, 1851) (Hylidae)
FIGURE 2. Leptodactylus latrans (Steffen, 1815), neotype (MNRJ 30733; 107.0 mm SVL), dorsal and ventral views.
FIGURE 1 in The identification of Rana ocellata Linnaeus, 1758. Nomenclatural impact on the species currently known as Leptodactylus ocellatus (Leptodactylidae) and Osteopilus brunneus (Gosse, 1851) (Hylidae)
FIGURE 1. Osteopilus ocellatus (Linnaeus, 1758), neotype (USNM 251390; 49.9 mm SVL), A) dorsal and B) ventral views; C) lateral view of head.
Conditions météorologiques défavorables qui ont un impact sur 21 pratiques viticoles en moyenne vallée de la Loire au moment de leur réalisation
<p>L'image montre en générale l'impact des conditions climatiques défavorables sur le développement et la croissance d'une culture dans ce cas c'est le vignoble. Ces conditions se résument par le vent, le sol, l'humidité, la température et la pluviométrie.</p>
Conditions climatiques défavorables qui ont un impact sur 21 pratiques viticoles en moyenne vallée de la Loire au moment de leur réalisation
<p>L'image montre en générale l'impact des conditions climatiques défavorables sur le développement et la croissance d'une culture dans ce cas c'est le vignoble. Ces conditions sont le vent, le sol, l'humidité, la température et la pluviométrie.</p>
Impact of defects on the phase diagram and field response of ferroelectric (Ba,Sr)TiO3
<p>This repository contains the simulation results for (Ba,Sr)TiO3 using coarse-grained molecular dynamics package <a title="Feram" href="https://loto.sourceforge.net/feram/" target="_blank" rel="noopener">Feram</a>.</p> <p>These data can be visualized with scripts in the <a href="https://gitlab.ruhr-uni-bochum.de/tengssh/p1-defect_study/" target="_blank" rel="noopener">RUB gitlab</a> repository and are supplementary for an associated publication.<br>The publication link will be provided after publishing.</p> <p>All files (1: data.avg, 2: *.dipoRavg, 3: *.coord, 4: *.hl, 5: *.defects) use the space-separated format.</p> <p>(1) data.avg columns:<br>T: temperature in Kelvin<br>Ex Ey Ez: external_E_field along x,y,z in V/Angstrom.<br>exx eyy ezz eyz ezx exy: strain tensor<br>ux uy uz: dipole displacements in Angstrom<br>uxux uyuy uzuz uyuz uzux uxuy: cross-terms of dipole displacements in Angstrom^2<br>dk: dipo_kinetic in eV/u.c.<br>lr: long_range in eV/u.c.<br>dEf: dipole_E_field in V/Angstrom<br>unhar: unharmonic in eV/u.c.<br>s_ho: homo_strain in eV/u.c.<br>c_ho: homo_coupling in eV/u.c.<br>s_inho: inho_strain in eV/u.c.<br>c_inho: inho_coupling in eV/u.c.<br>etot: total energy in eV/u.c.<br>HNP: H_Nose_Poincare in eV/u.c.<br>e2: e2<br>dkt: dipo_kinetic_true in eV/u.c.<br>ak: acuou_kinetic in eV/u.c.<br>sr: short_range in eV/u.c.<br>mod: inho_modulation in eV/u.c.<br>px py pz: px py pz<br>ppx ppy ppz ppyz ppzx ppxy: ppx ppy ppz ppyz ppzx ppxy<br>mx my mz: <ux>, <uy>, <uz> in Angstrom<br>amx amy amz: <|ux|>, <|uy|>, <|uz|> in Angstrom</p> <p>(2) *.dipoRavg columns:<br>x y z: coordinates<br>ux uy uz: dipole displacements in Angstrom</p> <p>(3) *.coord columns:<br>x y z: coordinates<br>ux uy uz: dipole displacements in Angstrom<br>ppx ppy ppz: dipoP<br>ddx ddy ddz: dVddi<br>arx ary arz: acouR<br>apx apy apz: acouP</p> <p>(4) *.hl columns:<br>step: timestep<br>T: temperature in Kelvin<br>Ex Ey Ez: external_E_field in V/Angstrom <br>exx eyy ezz eyz ezx exy: strain tensor<br>ux uy uz: dipole displacements in Angstrom</p> <p>(5) *.defects columns:<br>x y z: coordinates<br>ux uy uz: dipole displacements in Angstrom</p> <p><br><br></p>
Historical changes in drought characteristics and its impact on vegetation cover over Madagascar
<p>The uploaded data represent the results of our research about "Historical changes in drought characteristics and its impact on vegetation cover over Madagascar".</p>
Data analysis scripts for Marsh et al. 2024 'Tropical forest clearance impacts biodiversity and function whereas logging changes structure'
<p>Data analysis scripts for the manuscript <strong>Marsh<em> </em>et<em> </em>al. 2024 'Tropical forest clearance impacts biodiversity and function whereas logging changes structure'</strong></p> <p><strong>Update for Version 2:</strong> The calculation of confidence intervals around the mean effects in Figure 2 has been updated to use the <code>marginaleffects</code> package (many thanks to Biao Wang and Shuang Zhang for pointing out an error in the original code). Using the Satterthwaite method for determining degrees of freedom, the updated confidence intervals are around 32% smaller than our original estimates (MLF = 32.0%, HLF = 32.1%, OP = 21.6%). Note, this change is only relevant to fig. 2 and figs. S2-4; the mean effect sizes and trends along the disturbance gradient, all statistical comparisons, and the constrast analyses in fig. 3 remain unaffected. The updated figures S2-4 and Table S6 can be seen in the file 'Updated figures S2-4 with recalculated confidence intervals.pdf'.</p> <p>In the zip file 'BALI_synthesis_analysis.zip' there are outputs from RMarkdown scripts that include all steps of the analysis for each dataset, including R code, incorporating data visualisation, exploration and standardisation, model building and evaluation, and visualisation of results. Fig. 2b can be regenerated using code in the zip file 'Marsh_etal_2024_Science_fig1b_chm_and_canopy_profiles-main.zip'.</p> <p>Each dataset presented in the manuscript has an html file within the folder 'Analyses'. For datasets involving bat, bird, dung beetle and tree traits additional markdown documents are available for steps take during data preparation in the folder 'Data preparation'.</p> <p>In the zip file 'BALI_synthesis_data.zip' are .rds data files that have been cleaned, prepared and z-score standardised following the procedures outlined in the respective markdown files.</p> <p>To repeat any given analysis, follow the respective rmarkdown document, excluding the data manipulation steps:</p> <ol> <li>Read in the data file as described above: dd <- readRDS(paste0("path/to/rds/file/", "name_of_file.rds"))</li> <li>Run the code at the top of the markdown workflow (sections "Data information" and "Load in necessary libraries")</li> <li>Do not run the sections "Read in data" through to "Visual inspection of the data"</li> <li>Continue the analysis from the 'Modelling' section</li> </ol> <div> <h3> </h3> <h3>Level 1 - Structure & Environment</h3> </div> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> <th>Collector</th> </tr> </tbody> <tbody> <tr> <td>Above-ground carbon</td> <td>Above ground carbon</td> <td>Above_ground_carbon</td> <td>Terhi Riutta</td> </tr> <tr> <td>Leaf-area index</td> <td>Leaf-area index</td> <td>Leaf_area_index</td> <td>Terhi Riutta</td> </tr> <tr> <td>Soil temperature</td> <td>Soil temp.</td> <td>Soil_temperature</td> <td>Terhi Riutta</td> </tr> <tr> <td>Soil moisture</td> <td>Soil moisture</td> <td>Soil_moisture</td> <td>Dafydd Elias</td> </tr> <tr> <td>Air temperature: Minimum</td> <td>Air temp.: Min.</td> <td>Air_temperature_minimum</td> <td>Benjamin Blonder</td> </tr> <tr> <td>Air temperature: Mean</td> <td>Air temp.: Mean</td> <td>Air_temperature_mean</td> <td>Benjamin Blonder</td> </tr> <tr> <td>Air temperature: Maximum</td> <td>Air temp.: Max.</td> <td>Air_temperature_maximum</td> <td>Benjamin Blonder</td> </tr> <tr> <td>Soil bulk density</td> <td>Soil bulk density</td> <td>Soil_bulk_density</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil horizon depth</td> <td>Soil horizon depth</td> <td>Soil_horizon_depth</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil pH</td> <td>Soil pH</td> <td>Soil_pH</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil nutrients: Carbon</td> <td>Soil nutrients (C)</td> <td>Soil_nutrients_C</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil nutrients: Nitrogen</td> <td>Soil nutrients (N)</td> <td>Soil_nutrients_N</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil nutrients: Inorganic Phosporous</td> <td>Soil nutrients (Inorganic P)</td> <td>Soil_nutrients_Inorganic_P</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil nutrients: Carbon:Phosphorous</td> <td>Soil nutrients (C:P)</td> <td>Soil_nutrients_C_P</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil nutrients: Carbon:Nitrogen</td> <td>Soil nutrients (C:N)</td> <td>Soil_nutrients_C_N</td> <td>Dafydd Elias</td> </tr> </tbody> </table> <div> <h3> </h3> <h3>Level 2 - Tree traits</h3> </div> <p>All tree traits were collected as part of the following study (details in this table have been extracted from table S1 of that publication): S. Both, T. Riutta, C.E.T. Paine, D.M.O. Elias, R.S. Cruz, A. Jain, D. Johnson, U.H. Kritzler, M. Kuntz, N. Majalap-Lee, N. Mielke, M.X. Montoya Pillco, N.J. Ostle, Y. Arn Teh, Y. Malhi, D.F.R.P. Burslem (2019) Logging and soil nutrients independently explain plant trait expression in tropical forests. New Phytologist. 221:4, 1853–1865.</p> <p> </p> <p><em><strong>Photosynthesis Traits</strong></em></p> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> </tr> </tbody> <tbody> <tr> <td>Aggregated photosynthesis traits</td> <td>Photosyn. traits</td> <td>Photosynthesis traits</td> </tr> <tr> <td>δ<sup>13</sup>C</td> <td>δ<sup>13</sup>C</td> <td>Traits_13C</td> </tr> <tr> <td>Light-saturated photosynthetic rate</td> <td>Photosyn. rate: A<sub>sat</sub></td> <td>Traits_Asat</td> </tr> <tr> <td>Maximum photosynthetic rate</td> <td>Photosyn. rate: A<sub>max</sub></td> <td>Traits_Amax</td> </tr> <tr> <td>Maximum photosynthetic rate: Nitrogen concentration</td> <td>Max. photosyn. rate: N(%)</td> <td>Traits_N_conc</td> </tr> <tr> <td>Maximum photosynthetic rate: Phosphorous mass (area)</td> <td>Max. photosyn. rate: P(mass)</td> <td>Traits_Phos_area</td> </tr> <tr> <td>Dark respiration (Rd)</td> <td>Dark respiration</td> <td>Traits_Dark_resp</td> </tr> <tr> <td>Specific leaf area (SLA)</td> <td>Specific leaf area</td> <td>Traits_SLA</td> </tr> <tr> <td>Carotenoids (area)</td> <td>Carotenoids: Area</td> <td>Traits_Carot_area</td> </tr> <tr> <td>Carotenoids (mass)</td> <td>Carotenoids: Mass</td> <td>Traits_Carot_mass</td> </tr> <tr> <td>Chlorophyll a (area)</td> <td>Chlorophyll a: Area</td> <td>Traits_Chl_a_area</td> </tr> <tr> <td>Chlorophyll a (mass)</td> <td>Chlorophyll a: Mass</td> <td>Traits_Chl_a_mass</td> </tr> <tr> <td>Chlorophyll b (area)</td> <td>Chlorophyll b: Area</td> <td>Traits_Chl_b_area</td> </tr> <tr> <td>Chlorophyll b (mass)</td> <td>Chlorophyll b: Mass</td> <td>Traits_Chl_b_mass</td> </tr> </tbody> </table> <p> </p> <p><em><strong>Nutrient Traits</strong></em></p> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> </tr> </tbody> <tbody> <tr> <td>Aggregated nutrient traits</td> <td>Nutrient traits</td> <td>Nutrient_traits</td> </tr> <tr> <td>δ<sup>15</sup>N</td> <td>δ<sup>15</sup>N</td> <td>Traits_15N</td> </tr> <tr> <td>Carbon concentration</td> <td>Carbon conc.</td> <td>Traits_Carbon_conc</td> </tr> <tr> <td>Nitrogen concentration</td> <td>Max. photosyn. rate: N(%)</td> <td>Traits_N_perc</td> </tr> <tr> <td>Phosphorous concentration</td> <td>Max. photosyn. rate: P(mass)</td> <td>Traits_Phos_mass</td> </tr> <tr> <td>Magnesium concentration</td> <td>Regulat. nutrients: Total Mg</td> <td>Traits_Total_Mg</td> </tr> <tr> <td>Potassium concentration</td> <td>Regulat. nutrients: Total K</td> <td>Traits_Total_K</td> </tr> <tr> <td>Calcium concentration</td> <td>Regulat. nutrients: Total Ca</td> <td>Traits_Total_Ca</td> </tr> </tbody> </table> <p> </p> <p><em><strong>Structural Traits</strong></em></p> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> </tr> </tbody> <tbody> <tr> <td>Aggregated structural traits</td> <td>Structural traits</td> <td>Structural_traits</td> </tr> <tr> <td>Branch specific density</td> <td>Branch wood density</td> <td>Traits_Branch_WD</td> </tr> <tr> <td>Leaf cellulose concentration</td> <td>Leaf fibre conc.: Cellul.</td> <td>Traits_Cellulose</td> </tr> <tr> <td>Leaf lignin concentration</td> <td>Leaf fibre conc.: Lignin</td> <td>Traits_Lignin</td> </tr> <tr> <td>Leaf hemicellulose concentration</td> <td>Leaf fibre conc.: Hemicel.</td> <td>Traits_Hemicellulose</td> </tr> <tr> <td>Leaf area</td> <td>Leaf size: Area</td> <td>Traits_Leaf_area</td> </tr> <tr> <td>Leaf dry weight</td> <td>Leaf size: Dry wgt</td> <td>Traits_Dry_weight</td> </tr> <tr> <td>Leaf force to punch</td> <td>Leaf strength: Tough.</td> <td>Traits_Leaf_toughness</td> </tr> <tr> <td>Leaf thickness</td> <td>Leaf strength: Thick.</td> <td>Traits_Leaf_thickness</td> </tr> <tr> <td>Leaf dry matter content</td> <td>Leaf strength: Dry mat.</td> <td>Traits_LDMC</td> </tr> <tr> <td>Total phenol concentration</td> <td>Leaf defence: Phenol</td> <td>Traits_Phenol</td> </tr> <tr> <td>Total tannin concentration</td> <td>Leaf defenct: Tannin</td> <td>Traits_Tannin</td> </tr> </tbody> </table> <div> <h3> </h3> <h3>Level 3 - Biodiversity</h3> </div> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> <th>Collector</th> </tr> </tbody> <tbody> <tr> <td>Soil bacterial richness</td> <td>Soil microbial richness: Bacteria</td> <td>Soil_richness_Bacteria</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil protist richness</td> <td>Soil microbial richness: Protists</td> <td>Soil_richness_Protist</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil ectomycorrhizal richness</td> <td>Soil fungal richness: Ectomycorrhiza</td> <td>Soil_richness_Ectomycorrhiza</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil fungal richness</td> <td>Soil fungal richness: Fungi</td> <td>Soil_richness_Fungi</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil arbuscular mycorrhizal richness</td> <td>Soil fungal richness: Arbuscular mycorrhiza</td> <td>Soil_richness_Arbuscular_mycorrhizal</td> <td>Dafydd Elias</td> </tr> <tr> <td>Leaf spectral diversity</td> <td>Spectral diversity</td> <td>Spectral_diversity</td> <td>Matheus Nunes</td> </tr> <tr> <td>Liana abundance</td> <td>Liana abundance</td> <td>Liana_abundance</td> <td>Boris Bongalov</td> </tr> <tr> <td>Dung beetle abundance</td> <td>Dung beetle abund.</td> <td>Dung_beetle_abundance</td> <td>Eleanor Slade</td> </tr> <tr> <td>Dung beetle diversity: richness</td> <td>Dung beetle diversity: q=0</td> <td>Dung_beetle_diversity_q=0</td> <td>Eleanor Slade</td> </tr> <tr> <td>Dung beetle diversity: Shannon diversity</td> <td>Dung beetle diversity: q=1</td> <td>Dung_beetle_diversity_q=1</td> <td>Eleanor Slade</td> </tr> <tr> <td>Dung beetle diversity: Simpson diversity</td> <td>Dung beetle diversity: q=2</td> <td>Dung_beetle_diversity_q=2</td> <td>Eleanor Slade</td> </tr> <tr> <td>Bird abundance</td> <td>Bird abund.</td> <td>Bird_abundance</td> <td>Simon Mitchell</td> </tr> <tr> <td>Bird diversity: richness</td> <td>Bird diversity: q=0</td> <td>Bird_diversity_q=0</td> <td>Simon Mitchell</td> </tr> <tr> <td>Bird diversity: Shannon diversity</td> <td>Bird diversity: q=1</td> <td>Bird_diversity_q=1</td> <td>Simon Mitchell</td> </tr> <tr> <td>Bird diversity: Simpsons diversity</td> <td>Bird diversity: q=2</td> <td>Bird_diversity_q=2</td> <td>Simon Mitchell</td> </tr> <tr> <td>Bat abundance</td> <td>Bat abund.</td> <td>Bat_abundance</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat diversity (small scale)</td> <td>Bat diversity (sm scale)</td> <td>Bat_diversity_small_scale</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat diversity (large scale): richness</td> <td>Bat diversity (lg scale): q=0</td> <td>Bat_diversity_large_scale_q=0</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat diversity (large scale): Shannon diversity</td> <td>Bat diversity (lg scale): q=1</td> <td>Bat_diversity_large_scale_q=1</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat diversity (large scale): Simpson diversity</td> <td>Bat diversity (lg scale): q=2</td> <td>Bat_diversity_large_scale_q=2</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat β-diversity: Nestedness</td> <td>Bat β-diversity: Nested.</td> <td>Bat_beta_diversity_Nestedness</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat β-diversity: Turnover</td> <td>Bat β-diversity: Turn.</td> <td>Bat_beta_diversity_Turnover</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat β-diversity: Total</td> <td>Bat β-diversity: Total</td> <td>Bat_beta_diversity_Total</td> <td>David Hemprich-Bennett</td> </tr> </tbody> </table> <div> <h3> </h3> <h3>Level 4 - Functioning</h3> </div> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> <th>Collector</th> </tr> </tbody> <tbody> <tr> <td>Soil respiration</td> <td>Respiration: Soil</td> <td>Soil_respiration</td> <td>Terhi Riutta</td> </tr> <tr> <td>Stem respiration</td> <td>Respiration: Stem</td> <td>Stem_respiration</td> <td>Terhi Riutta</td> </tr> <tr> <td>Net primary productivity</td> <td>NPP</td> <td>NPP</td> <td>Terhi Riutta</td> </tr> <tr> <td>Litterfall</td> <td>Litterfall</td> <td>Litterfall</td> <td>Terhi Riutta</td> </tr> <tr> <td>Leaf litter decomposition</td> <td>Litter decomposition</td> <td>Litter_decomposition</td> <td>Sabine Both</td> </tr> <tr> <td>Soil mycelial production</td> <td>Mycelial production</td> <td>Hyphal_length</td> <td>Samuel Robinson</td> </tr> <tr> <td>Dung removal</td> <td>Dung removal</td> <td>Dung_removal</td> <td>Eleanor Slade</td> </tr> </tbody> </table> <p> </p> <h2>Funding</h2> <p>Analyses were carried out, and data were collected, as part of the BALI (Biodiversity And Land-use Impacts on tropical ecosystem function) and LOMBOK (Land-use Options for Maintaining BiOdiversity & eKosystem functions) projects using the following funding:</p> <ul> <li>NERC Human-modified Tropical Forests Programme (NE/K016377/1, NE/K016261/1, NE/K016148/1, NE/K016407/1);</li> <li>NERC grant (NE/I028068/1);</li> <li>British Ecological Society Small Ecological Project Grant (No.: 3256/4035);</li> <li>Varley-Gradwell Travelling Fellowship in Insect Ecology;</li> <li>Bat Conservation International Student Research Scholarship;</li> <li>NOMIS Foundation;</li> <li>ERC European Union's Horizon 2020 research and innovation programme (grant agreement No 865403);</li> <li>ERC Advanced Investigator Grant, GEM-TRAIT (321131);</li> <li>The SAFE Project is funded by the Sime Darby Foundation.</li> </ul>
Data for Cumulative hydrological impacts of forest disturbance follow the Law of Diminishing Returns
<p>This dataset supports the analysis of the manuscript, entitled "<span lang="EN-US">Long-term Cumulative Hydrological Effects of Forest Disturbances Follow the Law of Diminishing Returns in British Columbia, Canada</span>". It includes forest disturbance history, i.e., cumulative equivalent clear-cut area (CECA), hydrological sensitivities in the post-threshold period in the study watersheds, and the estiamted CECA at the upper limit and environmental variables. </p>
Supplementary Material for The Impact of COVID-19 on Open Source Development Activities: A Multi-Method Study
<p><strong>Context:</strong> The social isolation measures resulting from the COVID-19 outbreak changed work practices in various sectors, especially with the shift to working from home. However, it is still unclear the implications of the pandemic on the maintenance and evolution of open-source software (OSS). Objective: In this study, we analyze the effects of COVID-19 on the development activity of OSS and how social isolation changed the productivity and emotional state of OSS contributors. <strong>Method:</strong><br>To investigate this issue, we have conducted a multi-method study. We first quantitatively investigated how the COVID-19 outbreak impacted the development activity of open-source projects by mining the development history of 155 open-source projects. We then qualitatively investigated the perceptions of open-source core developers about the impact of COVID-19 on their activities, using a survey as an instrument. <strong>Results:</strong> Our results indicate that the pandemic led to some effect on the development<br>activity of OSS repositories, especially in the early days of the outbreak. For instance although we observe an increase in the number of pull requests accepted, our results also indicate a sudden increase in the turnover rate of core developers. However, a few months after the declaration of the current pandemic, our results suggest stabilization in these metrics. Other effects were more far-reaching, such as a decrease in newcomers throughout the observed period after the pandemic was declared. Some findings are supported by the results of our qualitative study, whose results indicate that most of the respondents of our survey consider that COVID-19 did not change their productivity substantially. <strong>Conclusions:</strong> Our findings can help practitioners and researchers to better understand the effect of COVID-19 on the software engineering field.</p>
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.