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8,819 results for “new data”
Linked collectors and determiners for: A new subgenus of Sonchus (Asteraceae: Cichorieae) inferred from molecular data.
Natural history specimen data linked to collectors and determiners held within, "A new subgenus of Sonchus (Asteraceae: Cichorieae) inferred from molecular data". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/b9ea8044-2429-43a3-be7d-ad9201326aff">https://bionomia.net/dataset/b9ea8044-2429-43a3-be7d-ad9201326aff</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/b9ea8044-2429-43a3-be7d-ad9201326aff">https://gbif.org/dataset/b9ea8044-2429-43a3-be7d-ad9201326aff</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: New data on the Oriental Xantholinini. 44. New species and new records from Nepal, Malaysia, Vietnam, Philippines, Sumatra, and Sabah (Coleoptera Staphylinidae).
Natural history specimen data linked to collectors and determiners held within, "New data on the Oriental Xantholinini. 44. New species and new records from Nepal, Malaysia, Vietnam, Philippines, Sumatra, and Sabah (Coleoptera Staphylinidae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/90bd4bd8-cf57-4acf-9088-8b167b1df709">https://bionomia.net/dataset/90bd4bd8-cf57-4acf-9088-8b167b1df709</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/90bd4bd8-cf57-4acf-9088-8b167b1df709">https://gbif.org/dataset/90bd4bd8-cf57-4acf-9088-8b167b1df709</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Revision of the genus Miridiba Reitter, 1902 (Coleoptera, Scarabaeidae, Melolonthinae): genital morphotypes and new taxonomic data.
Natural history specimen data linked to collectors and determiners held within, "Revision of the genus Miridiba Reitter, 1902 (Coleoptera, Scarabaeidae, Melolonthinae): genital morphotypes and new taxonomic data". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/83109e54-596a-4be0-ac2c-c4e1702cfb8a">https://bionomia.net/dataset/83109e54-596a-4be0-ac2c-c4e1702cfb8a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/83109e54-596a-4be0-ac2c-c4e1702cfb8a">https://gbif.org/dataset/83109e54-596a-4be0-ac2c-c4e1702cfb8a</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: An update of morphological and distributional data of the genus Patrera Simon (Araneae: Anyphaenidae: Anyphaeninae) with the description of twenty-five new species from Colombia.
Natural history specimen data linked to collectors and determiners held within, "An update of morphological and distributional data of the genus Patrera Simon (Araneae: Anyphaenidae: Anyphaeninae) with the description of twenty-five new species from Colombia". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/5c5c8aad-73f1-4a26-bdca-d1d02ba3fec1">https://bionomia.net/dataset/5c5c8aad-73f1-4a26-bdca-d1d02ba3fec1</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/5c5c8aad-73f1-4a26-bdca-d1d02ba3fec1">https://gbif.org/dataset/5c5c8aad-73f1-4a26-bdca-d1d02ba3fec1</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: New taxonomic and faunistic data to species of the genus Laena Dejean from Turkey (Coleoptera, Tenebrionidae, Lagriinae), with descriptions of two new species.
Natural history specimen data linked to collectors and determiners held within, "New taxonomic and faunistic data to species of the genus Laena Dejean from Turkey (Coleoptera, Tenebrionidae, Lagriinae), with descriptions of two new species". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/9b512d6d-956c-45a3-9265-8ae1167ba019">https://bionomia.net/dataset/9b512d6d-956c-45a3-9265-8ae1167ba019</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/9b512d6d-956c-45a3-9265-8ae1167ba019">https://gbif.org/dataset/9b512d6d-956c-45a3-9265-8ae1167ba019</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Eight new species of the genus Anaplecta Burmeister, 1838 (Blattodea: Blattoidea: Anaplectidae) from China based on molecular and morphological data.
Natural history specimen data linked to collectors and determiners held within, "Eight new species of the genus Anaplecta Burmeister, 1838 (Blattodea: Blattoidea: Anaplectidae) from China based on molecular and morphological data". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/b7ec7b7c-5b54-401f-a513-7776957b6a09">https://bionomia.net/dataset/b7ec7b7c-5b54-401f-a513-7776957b6a09</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/b7ec7b7c-5b54-401f-a513-7776957b6a09">https://gbif.org/dataset/b7ec7b7c-5b54-401f-a513-7776957b6a09</a>. Formatted as a Frictionless Data package.
Raw data and figures for "Increasing Sensitivity and Versatility in NMR Supersequences with New HSQC-based Modules"
<p>Datasets, pulse programmes, processing scripts, and figures for the publication "Increasing Sensitivity and Versatility in NMR Supersequences with New HSQC-based Modules" (J. Magn. Reson., accepted, 2021).</p> <p>Please see the README.txt file for more information.</p> <p>If you are only interested in pulse programmes and processing scripts, then this dataset will likely be too big for your liking (it is ca. 9 GB because there is a lot of 2D data). You can download the pulse programmes and processing scripts from the Bruker User Library https://www.bruker.com/en/services/bruker-user-library.html.</p> <p><strong>Changelog</strong></p> <p>v1.0.1: some quality of life improvements to the Python NUS script to stop it from being accidentally used on old pulse programmes.</p> <p>v1.0.2: fixed a bug in the v1.0.1 Python script. It now also resets FnTYPE in case the user has changed it.</p>
Data and Software associated with the paper "``A New Likelihood-based Test for Natural Selection''"
<p>Data and Software associated with the paper ``A New Likelihood-based Test for Natural Selection''</p>
Simulation Results Data for 'Modelling new insecticide-treated bed nets for malaria-vector control: How to strategically manage resistance?'
<p>GENERAL INFORMATION</p> <p>1. Title of Dataset: Simulation Results Data for 'Modelling new insecticide-treated bed-nets for malaria-vector control: How to strategically manage resistance?'</p> <p>2. Author Information<br> A. Investigator Contact Information<br> Name: Philip G. Madgwick<br> Institution: Syngenta <br> Address: Jealott’s Hill International Research Centre, Bracknell, RG42 6EY, UK<br> Email: philip.madgwick@syngenta.com</p> <p> B. Investigator Contact Information<br> Name: Ricardo Kanitz<br> Institution: Syngenta <br> Address: Syngenta Crop Protection, Rosentalstrasse 67, CH-4058 Basel, Switzerland<br> Email: ricardo.kanitz@syngenta.com</p> <p><br> 3. Date of data collection (single date, range, approximate date): 2021-01-13 to 2021-02-01 </p> <p>4. Geographic location of data collection: UK </p> <p>5. Information about funding sources that supported the collection of the data: </p> <p>This work was conducted during a postdoctoral research position for PGM funded by the Innovative Vector Control Consortium (IVCC).</p> <p><br> SHARING/ACCESS INFORMATION</p> <p>1. Licenses/restrictions placed on the data: NA</p> <p>2. Links to publications that cite or use the data: [UPDATE]</p> <p>3. Links to other publicly accessible locations of the data: NA</p> <p>4. Links/relationships to ancillary data sets: NA</p> <p>5. Was data derived from another source? No</p> <p>6. Recommended citation for this dataset: [UPDATE]</p> <p><br> DATA & FILE OVERVIEW</p> <p>1. File List: <br> PSData_random6.csv - 10^6 random samples of each of the 17 parameters in the model, where rows are samples and columns are parameters (with column names corresponding to the parameters identified in the rows of Table 1 of the manuscript; see also DATA-SPECIFIC INFORMATION)<br> Data_random6_maxpsMixture_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_maxpsMixture_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_maxpsMixture_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Mixture_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_Mixture_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Mixture_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Mosaic_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_Mosaic_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Mosaic_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Rotation_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_Rotation_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Rotation_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Rotation_Fixed_revmm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_Rotation_Fixed_revmn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_Rotation_Fixed_revnn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_SoloA_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_SoloA_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_SoloA_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance <br> Data_random6_SoloB_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_SoloB_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has mitochondrial inheritance <br> Data_random6_SoloB_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance </p> <p>2. Relationship between files, if important: </p> <p>Files are named in accordance with the variables that describe each simulation setup, as described above. Each simulation dataset has 10^6 runs that correspond to the 10^6 random samples of each of the 17 parameters in the model in 'PSData_random6.csv'.</p> <p>3. Additional related data collected that was not included in the current data package: NA</p> <p>4. Are there multiple versions of the dataset? No</p> <p><br> METHODOLOGICAL INFORMATION</p> <p>1. Description of methods used for collection/generation of data: Data were collected using Simulator.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript. </p> <p>2. Methods for processing the data: Data were processed using Figures.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript. </p> <p>3. Instrument- or software-specific information needed to interpret the data: Analysis was conducted in R version 4.0.3 (2020-10-10), using R packages identified in Figures.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript. </p> <p>4. Standards and calibration information, if appropriate: NA</p> <p>5. Environmental/experimental conditions: NA</p> <p>6. Describe any quality-assurance procedures performed on the data: NA</p> <p>7. People involved with sample collection, processing, analysis and/or submission: NA </p> <p><br> DATA-SPECIFIC INFORMATION FOR: PSData_random6.csv</p> <p>1. Number of variables: </p> <p>17 variables with column names that have the following parameter meanings (see Table 1 in the manuscript): <br> Population Size = N = starting population size (and carrying capacity in logistic model); random sample range on log-scale: 10^2 - 10^9<br> Intrinsic Birth Rate = b = % population growth rate (in logistic model); random sample following a standard log-normal distribution with mean=0 and sd=1<br> Intrinsic Death Rate = d = % breeding mosquitoes that die into next generation; random sample range: 0 - 1<br> Female Exposure = x_[female-symbol] = % female mosquitoes that receive a dose; random sample range: 0 - 1<br> Male Exposure x_[male-symbol] = % male mosquitoes that receive a dose; random sample range: 0 - 1<br> Initial Frequency A = f_0,A = starting frequency of allele A; random sample range on log-scale: 10^-9 - 10^-2, limited to be within the range 1/N - N/100 where N is Population Size<br> Effectiveness 1 = m_1 = % dosed mosquitoes that die from insecticide 1; random sample range: 0 - 1<br> Resistance Restoration A = r_A = % return to baseline fitness with resistance allele A; random sample range: 0 - 1<br> Dominance of Resistance Restoration A = h^r_A = % resistance restoration in heterozygote with allele A; random sample range: 0 - 1<br> Resistance Cost A = c_A = % non-dosed mosquitoes that die from carrying allele A; random sample range on log-scale: 10^-3 - 10^-0.5<br> Dominance of Resistance Cost A = h^c_A = % resistance cost in heterozygote with allele A; random sample range: 0 - 1<br> Initial Frequency B = f_0,B = starting frequency of allele B; random sample range on log-scale: 10^-9 - 10^-2, limited to be within the range 1/N - N/100 where N is Population Size<br> Effectiveness 2 = m_2 = % dosed mosquitoes that die from insecticide 2; random sample range: 0 - 1<br> Resistance Restoration B = r_B = % return to baseline fitness with resistance allele B; random sample range: 0 - 1<br> Dominance of Resistance Restoration B = h^r_B = % resistance restoration in heterozygote with allele B; random sample range: 0 - 1<br> Resistance Cost B = c_B = % non-dosed mosquitoes that die from carrying allele B; random sample range on log-scale: 10^-3 - 10^-0.5<br> Dominance of Resistance Cost B = h^c_B = % resistance cost in heterozygote with allele B; random sample range: 0 - 1</p> <p>2. Number of cases/rows: </p> <p>10^6, corresponding to the number of random samples </p> <p>3. Variable List: NA </p> <p>4. Missing data codes: NA</p> <p>5. Specialized formats or other abbreviations used: NA</p> <p><br> DATA-SPECIFIC INFORMATION FOR: all other dataset files (e.g. Data_random6_maxpsMixture_Fixed_mm.csv) </p> <p>1. Number of variables: </p> <p>10 variables with column names that have the following meanings:<br> A_t_50% = the recorded number of generations that it takes for resistance allele A to reach >50% frequency; 0 means that resistance allele A never reaches >50% frequency <br> A_f_250 = the frequency of resistance allele A at the 250th generation <br> A_f_bar = the mean frequency of resistance allele A over the first 250 generations <br> B_t_50% = the recorded number of generations that it takes for resistance allele B to reach >50% frequency; 0 means that resistance allele B never reaches >50% frequency <br> B_f_250 = the frequency of resistance allele B at the 250th generation <br> B_f_bar = the mean frequency of resistance allele B over the first 250 generations <br> nf_80% = the recorded number of generations that it takes for the female population size to recover to >80% of its original size in the 0th generation; 0 means that the female population size never reaches >80% recovery; 1 means that the female population size never drops below 80% of its original size in the 1st generation<br> nf_250 = the female population size at the 250th generation<br> nf_bar = the mean female population size over the first 250 generations <br> nf_ext = the recorded number of generations that it takes for the female population size to drop below 1 (i.e. population extinction); 0 means that female population size never reaches <1</p> <p>2. Number of cases/rows: </p> <p>10^6, corresponding to the number of random samples </p> <p>3. Variable List: NA</p> <p>4. Missing data codes: all missing data is recorded as 0 </p> <p>5. Specialized formats or other abbreviations used: NA</p>
Data and scripts used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p>Images, script and data used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
A new lepto-hadronic model applied to the first simultaneous multiwavelength data set for Cygnus X–1
<p>This is a basic reproduction package for the paper "A new lepto-hadronic model applied to the first simultaneous multiwavelength data set for Cygnus X-1" by [Kantzas et al. (2020)](<a href="https://doi.org/10.1093/mnras/staa3349">https://doi.org/10.1093/mnras/staa3349</a>).</p>
FIG. 4 in A new edible yam (Dioscorea L.) species endemic to Mayotte, new data on D. comorensis R.Knuth and a key to the yams of the Comoro Archipelago
FIG. 4. — Dioscorea mayottensis Wilkin and its tuber: A, habit; B, immature capsules; C, tubers and lower stem; D, close-up of tubers and crown, showing the tuber of the previous rainy season from which the plant is growing, and the new rainy season's tuber in early development. On the latter, the insertion of the crown (upper part) on the tuber (lower part) can be seen, with the thick roots emanating from the crown. Photos Fabien Barthelat.
FIG. 3 in A new edible yam (Dioscorea L.) species endemic to Mayotte, new data on D. comorensis R.Knuth and a key to the yams of the Comoro Archipelago
FIG. 3. — Female flowering and fruiting morphology in Dioscorea mayottensis Wilkin: A, leaf and female inflorescence, showing the lateral nodal spines; B, flower showing its insertion onto the inflorescence axis, the floral bract and bracteole, ovary and the floral stipe at its apex; C, flower with three tepals removed to show four of the staminodia, the styles and stigmas; D, half-flower showing the torus shape; E, infructescence with immature capsules, showing the persistent floral stipe forming an apical apiculus; F, a ba- sally winged seed. Drawn from Hladik et al. 6469 (A-D), Barthelat et al. 796 (E) and Hladik 6494 (F) by Lucy Smith. Scale bar: A, 3 cm; B, 4 mm; C, D, 0.9 mm; E, 3 cm; F, 1 cm.
FIG. 1. — Dioscorea comorensis R in A new edible yam (Dioscorea L.) species endemic to Mayotte, new data on D. comorensis R.Knuth and a key to the yams of the Comoro Archipelago
FIG. 1. — Dioscorea comorensis R.Knuth: A, female inflorescences with basal ovaries reflexed and enlarging; B, female inflorescence with more ovaries reflexed and enlarging; C, immature infructescence; D, part of male inflorescence showing the flowers in cymules; E, male inflorescences; F, male flowers. Photos Nicole Crestey (A, B, D, E) and Jean-Noël Labat (C, F).
Figure 3 in Use of morphological and molecular data to identify three new sibling species of the genus Munida Leach, 1820 (Crustacea, Decapoda, Galatheidae) from New Caledonia
Figure 3. Munida pectinata sp. nov., ovigerous female 6.9 mm, holotype from stn 1727 (NORFOLK 1). (A) Carapace, dorsal view; (B) sternal plastron; (C) ventral view of cephalic region, showing antennular and antennal peduncles; (D) right third maxilliped, lateral view; (E) right cheliped, dorsal view; (F) right first walking leg, lateral view; (G) dactylus of right first walking leg, lateral view.
Figure 2 in Use of morphological and molecular data to identify three new sibling species of the genus Munida Leach, 1820 (Crustacea, Decapoda, Galatheidae) from New Caledonia
Figure 2. Munida simulatrix sp. nov., male 7.7 mm, holotype from stn 1721 (NORFOLK 1). (A) Carapace, dorsal view; (B) sternal plastron; (C) ventral view of cephalic region, showing antennular and antennal peduncles; (D) right third maxilliped, lateral view; (E) right cheliped, dorsal view; (F) right first walking leg, lateral view; (G) dactylus of right first walking leg, lateral view.
Figure 1 in Use of morphological and molecular data to identify three new sibling species of the genus Munida Leach, 1820 (Crustacea, Decapoda, Galatheidae) from New Caledonia
Figure 1. Munida parile sp. nov., ovigerous female 3.7 mm, holotype from stn 1701 (NORFOLK 1). (A) Carapace, dorsal view; (B) sternal plastron; (C) ventral view of cephalic region, showing antennular and antennal peduncles; (D) left third maxilliped, lateral view; (E) right cheliped, dorsal view; (F) left first walking leg, lateral view; (G) dactylus of left first walking leg, lateral view.
Figures 14–19 in Suspended mummies in Aleiodes species (Hymenoptera: Braconidae: Rogadinae) with descriptions of six new species from western Uganda based largely on DNA sequence data
Figures 14–19. Automontage photographs of features of the Aleiodes buzurae-group. (14) Aleiodes kasenenei sp. n., habitus; (15)–(19) Aleiodes kanyawarensis sp. n., habitus, face, mesosoma, wings and metasoma, respectively.
Figure 3 in Suspended mummies in Aleiodes species (Hymenoptera: Braconidae: Rogadinae) with descriptions of six new species from western Uganda based largely on DNA sequence data
Figure 3. Bootstrap consensus tree from analysis of CO1 sequence data for Aleiodes reared from suspended mummies in Uganda plus another member of the A. buzurae-group and outgroups.
Figures 20–25 in Suspended mummies in Aleiodes species (Hymenoptera: Braconidae: Rogadinae) with descriptions of six new species from western Uganda based largely on DNA sequence data
Figures 20–25. Automontage photographs of features of Aleiodes trevelyanae sp. n. (20)–(24) Habitus, face, metasoma, mesosoma and wings of holotype; (25) metasoma of paratype.
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.