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opencc-zeroJan 2024View details →

Linked collectors and determiners for: Stolonochloa, a new Australian genus segregated from Panicum (Poaceae: Panicoideae: Paniceae: Boivinellinae) based on phenetic analysis of morphological data.

Natural history specimen data linked to collectors and determiners held within, "Stolonochloa, a new Australian genus segregated from Panicum (Poaceae: Panicoideae: Paniceae: Boivinellinae) based on phenetic analysis of morphological data". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/4d791cf1-3b5b-4f5f-b07f-3bb277298706">https://bionomia.net/dataset/4d791cf1-3b5b-4f5f-b07f-3bb277298706</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/4d791cf1-3b5b-4f5f-b07f-3bb277298706">https://gbif.org/dataset/4d791cf1-3b5b-4f5f-b07f-3bb277298706</a>. Formatted as a Frictionless Data package.

opencc-zeroJan 2024View details →

Linked collectors and determiners for: Nephoanthus (Melastomataceae: Sonerileae), a new genus segregated from Phyllagathis s. l., with a new species from Southern Vietnam.

Natural history specimen data linked to collectors and determiners held within, "Nephoanthus (Melastomataceae: Sonerileae), a new genus segregated from Phyllagathis s. l., with a new species from Southern Vietnam". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/7b100ae6-1e4b-4da3-bb71-1c1cd01ea70c">https://bionomia.net/dataset/7b100ae6-1e4b-4da3-bb71-1c1cd01ea70c</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/7b100ae6-1e4b-4da3-bb71-1c1cd01ea70c">https://gbif.org/dataset/7b100ae6-1e4b-4da3-bb71-1c1cd01ea70c</a>. Formatted as a Frictionless Data package.

opencc-zeroMay 2024View details →

Linked collectors and determiners for: Huchimingia, a new genus segregated from Millettia (Leguminosae, Millettieae) based on morphological and molecular evidence.

Natural history specimen data linked to collectors and determiners held within, "Huchimingia, a new genus segregated from Millettia (Leguminosae, Millettieae) based on morphological and molecular evidence". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/9cb10461-a420-4771-8a40-afec4ced8332">https://bionomia.net/dataset/9cb10461-a420-4771-8a40-afec4ced8332</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/9cb10461-a420-4771-8a40-afec4ced8332">https://gbif.org/dataset/9cb10461-a420-4771-8a40-afec4ced8332</a>. Formatted as a Frictionless Data package.

opencc-by-4.0Sep 2019View details →

Refining Bulk Segregant Analyses: Ontology-Mediated Discovery of Flowering Time Genes in Brassica oleracea

This record comprises the main supplement to study <a href="https://doi.org/10.1101/2021.08.11.455982">10.1101/2021.08.11.455982</a>. Contained in the archive identified by this record are the following files: <ul> <li>SUPPLEMENTARY_METHODS.pdf contains an extended description of the methods of the study, including external links to raw, intermediate, and result data</li> <li>st[1-5]_* supplementary tables referenced in the main manuscript body of the study</li> <li>*.zip files contain intermediate data archives referenced in the supplementary methods, *.txt files with the same names list the contents of the zip files </li> <li>go-basic.obo is an unmodified copy of the Gene Ontology (in OBO format) as used in this study</li> </ul> NOTE 1: this record contains additional references (related identifiers) that identify all other data and source code used in this study NOTE 2: for this record and all related records, the authors, their affiliations and their respective funding as relevant to this project are identified in the main manuscript, whose contents take precedence over equivalent metadata fields in supplementary records.

Fig. 5 in Barn Swallows Hirundo rustica in Peninsular Malaysia: urban winter roost counts after 50 years, and dietary segregation from house-farmed swiftlets Aerodramus sp.

Fig. 5. Diet distribution of swallows and house-farmed swiftlets at Bentong identified by NGS molecular analysis.

Fig. 1 in Barn Swallows Hirundo rustica in Peninsular Malaysia: urban winter roost counts after 50 years, and dietary segregation from house-farmed swiftlets Aerodramus sp.

Fig. 1. (Left) Map of Peninsular Malaysia, with an enlarged plan of Pahang State; the circle indicates the study area, Bentong District. (Right) Google view of Bentong municipality, showing old town centre (red) and suburbs where house-farmed swiftlet colonies (blue) were counted.

Fig. 2 in Barn Swallows Hirundo rustica in Peninsular Malaysia: urban winter roost counts after 50 years, and dietary segregation from house-farmed swiftlets Aerodramus sp.

Fig. 2. Swallows roosting on utility wires along streets of the Bentong town centre. Pacific Swallows, recognisable from below by the grey belly, were present in very low numbers during the passage and wintering period.

Fig. 4 in Barn Swallows Hirundo rustica in Peninsular Malaysia: urban winter roost counts after 50 years, and dietary segregation from house-farmed swiftlets Aerodramus sp.

Fig. 4. Comparative diets of swallows and house-farmed swiftlets in Bentong, identified by morphological analysis.

Fig. 3 in Barn Swallows Hirundo rustica in Peninsular Malaysia: urban winter roost counts after 50 years, and dietary segregation from house-farmed swiftlets Aerodramus sp.

Fig. 3. Number of Barn Swallows in the urban roost in Bentong town, Pahang, in 2015–16 and averaged for 1966–68.

Fig. 6 in Spatio-temporal segregation and size distribution of fish assemblages as related to non-native species occurrence in the middle rio Doce Valley, MG, Brazil

Fig. 6. Least-square means and 95% confidence intervals from ANCOVA of the first three environmental factors from PCA. Different markers represent significantly different means as detected by planned contrasts with 5% significance level, first comparing lakes with any non-native species with those without them, and then comparing the two categories of lakes with non-natives (non-piscivores vs. piscivores).

Fig. 4 in Spatio-temporal segregation and size distribution of fish assemblages as related to non-native species occurrence in the middle rio Doce Valley, MG, Brazil

Fig. 4. Scatterplot of species body size (mean standard length) vs. a relative index of native affinity to lakes containing piscivorous invaders (the proportion of biomass of a given native species in lakes with piscivorous invaders). The estimated regression line is also presented (Y = 0.044*X - 0.279; R2 = 0.443; p = 0.007). Species codes: ast = Astyanax sp.; aus = Australoheros facetus; cyp = Cyphocharax gilbert; cre = Crenicichla lacustris; geo = Geophagus brasiliensis; gym = Gymnotus gr. carapo; hop = Hoplias malabaricus; lep = Leporinus steindachneri; lor = Loricariidae (unidentified species); lyc = Lycengraulis sp.; moe = Moenkhausia doceana; oli = Oligosarcus solitarius; pac = Pachyurus adspersus; pro = Prochilodus vimboides; tra = Trachelyopterus striatulus.

Fig. 5 in Spatio-temporal segregation and size distribution of fish assemblages as related to non-native species occurrence in the middle rio Doce Valley, MG, Brazil

Fig. 5. Least-square means and 95% confidence intervals from ANCOVA of mean individual size and temporal turnover as related to the three lake categories. Different markers represent significantly different means as detected by planned contrasts with 5% significance level, first comparing lakes with any non-native species with those without them, and then comparing the two categories of lakes with non-natives (non-piscivores vs. piscivores).

Fig. 2 in Spatio-temporal segregation and size distribution of fish assemblages as related to non-native species occurrence in the middle rio Doce Valley, MG, Brazil

Fig. 2. Alpha (mean) and beta richness. a) Comparison among the temporal and spatial components of richness. b) Species richness for each lake. The alpha (mean) and beta richness were taken along the temporal component. Lake codes: No = Nova; Ca = Capim; Fe = Ferrugem; Cr = Crentes; Po = Poço Redondo; Ro = Romoalda; Ti = Timburé; Ag = Águas Claras; Pa = Palmeirinha; Ar = Ariranha. "Natives" represents lakes without non-native species; "Non-piscivores" represents lakes with non-piscivorous non-native species; "Piscivores" represents lakes with invasive piscivorous species.

opencc-by-4.0Dec 2008View details →

Fig. 1 in Scientific Note Vertical segregation of two species of Hyphessobrycon (Characiformes: Characidae) in the Cabiúnas coastal lagoon, southeastern Brazil

Fig. 1. Parque Nacional da Restinga de Jurubatiba (shaded area within the circle) in Rio de Janeiro State, southeastern Brazil. The satellite image shows Cabiúnas Lagoon, located in the southern part of the park. Asterisks indicate observation sites in the lagoon.

opencc-by-4.0Sep 2021View details →

Quantifying ethnic segregation in cities through random walks

Overview This repository contains the coverage time distributions used to produce the figures and statistics for the paper: S. Sousa, V. Nicosia "Quantifying ethnic segregation in cities through random walks". arXiv: <a href="https://arxiv.org/abs/2010.10462">https://arxiv.org/abs/2010.10462</a> Data The ccp.zip file contains two subfolders with the coverage time distributions for the US and UK systems. Each file contains a line per node of the network with the format: <pre><code>"Node ID" "[list with the CCT for each fraction c]"</code></pre> Note that each line will always contain 101 columns where the first column identifies the node and the remaining ones represent the average coverage time to reach a fraction c of classes. The dfa.zip file contains the following folders: <ul> <li>distances: each line corresponds to one repetition of the walk, it shows the area travelled by the walker, the length of the trajectory and perimeter.</li> <li>exponents:  The output file contains two columns, respectively for \epsilon and F(\epsilon).</li> <li>results_ids:  Time series of the visited nodes</li> </ul> The synthetic.zip file contains the coverage time distributions for the experiment with different lattice sizes (scale-test) and the experiment with distinct spatial patterns for the population distribution (topology-test). The format follows the same as in ccp.zip folder.   Code The reader interested in replicating the methods used to create the data can obtain the python scrips in the following repository: <a href="https://github.com/segregation-rw/ethnic-segregation-rw">https://github.com/segregation-rw/ethnic-segregation-rw</a> Note that the repository also includes the code to simulate the CCT random walks on the adjacency graphs so that the whole simulation can be replicated.

opencc-by-4.0Nov 2022View details →

Dataset belonging to the paper "Atomic resolution observations of silver segregation in a [111] tilt grain boundary in copper"

This repository contains the raw data of the experimental (S)TEM imaging and the data corresponding to the simulations and theoretical calculations of the paper "Atomic resolution observations of silver segregation in a [111] tilt grain boundary in copper",. A pre-print version of the paper is available on arXiv: <a href="http://doi.org/10.48550/arXiv.2212.01180">http://doi.org/10.48550/arXiv.2212.01180</a> See the file README.md for a detailed description.

dryad40/100

Data from: Transgressive segregation in mating traits drives hybrid speciation

Hybridization can instantaneously generate novel genetic variation, which can promote ecological speciation by creating novel adaptive phenotypes. However, it remains unclear how hybridization, creating novel mating phenotypes (e.g., mating season, genitalia shapes, sexual displays, mate preferences), affects speciation especially when the phenotypes do not confer adaptive advantages. Here, based on individual-based evolutionary simulations, we propose that transgressive segregation of mating traits can drive incipient hybrid speciation. Simulations demonstrated that incipient hybrid speciation occurred most frequently when the hybrid population received moderate continued immigration from parental lineages causing recurrent episodes of hybridization. Recurrent hybridization constantly generated genetic variation, which promoted the rapid stochastic evolution of mating phenotypes in a hybrid population. The stochastic evolution continued until a novel mating phenotype came to dominate the hybrid population, which reproductively isolates the hybrid population from parental lineages. However, too frequent hybridization rather hindered the evolution of reproductive isolation by inflating the variation of mating phenotypes to produce phenotypes allowing mating with parental lineages. Simulations also revealed conditions for long-term persistence of hybrid species after their incipient emergence. Our results suggest that recurrent transgressive segregation of mating phenotypes can offer a plausible explanation for hybrid speciation and radiations that involved little adaptive ecological divergence.

opencc-zeroMay 2023View details →

opencc-by-4.0Jun 2023View details →

Distinct frequencies balance segregation with interaction between different memory types within a prefrontal circuit

2023-05-30 This archive holds the original data used to derive the results reported in the publication: 'Distinct frequencies balance segregation with interaction between different memory types within a prefrontal circuit' Authors: Martina Bracco, Tuomas P. Mutanen, Domenica Veniero, Gregor Thut & Edwin M. Robertson Corresponding Author: Edwin M. Robertson Contact: edwin.robertson@glasgow.ac.uk Please refer to the License for the correct use of the data.  If the use of this data leads to any public dissemination, including (but not restricted to) scientific articles, conference presentations, and posters, please remember to cite the original article. General Description The dataset is shared in BIDS format. To facilitate the upload of files to Zenodo, each subject folder was compressed separately. To ensure a valid bids structure of the dataset, extract the contents of each zip file and place them in the same directory as the other files from the dataset. By doing so, you will have all the necessary files in the correct structure for the BIDS format. Data type: raw TMS-EEG data organized in BIDS format, and epoched ±800 ms around the pulse.            preprocessed motor skill and word recall data organized in BIDS format (.tsv = data file; .json = data description). Key to Filenames: sub = Participant                   ses-preLearning = Session 1 data folder                   ses-postLearnng = Session 2 data folder                   eeg = TMS-EEG data folder                   beh = behaviral data folder Participants: 45 healthy participants (30 females, 23.9±3.6 years; mean ± std)             Groups: Participants were randomly assigned to three different groups with different behavioural conditions:     - In the 'motorSkill'-group, participants performed a sequential motor-skill task followed by a wordlist memory task.          (sub-01 to sub-15)     - In the 'wordRecall'-group,  participants performed a  wordlist memory followed by a sequential task motor-skill task.        (sub-16 to sub-30)     - In the 'control'-group, participants performed a random motor-skill task followed by a wordlist memory task.         (sub-31 to sub-45) TMS-EEG data description Stimulation sites: Right dorsolateral prefrontal cortex (rDLPFC).                     Left primary motor cortex (lM1). Sessions: Session 1: TMS-EEG recordings before the behavioural tasks.           Session 2: TMS-EEG recordings after the behavioural tasks. Number of TMS pulses: 126 pulses delivered to either DLPFC or M1 before, before and after behavioural tasks (a total of 252 pulses for Session 1 and Session 2, each). Inter-Stimulus Interval: 4-6 s (4.9±0.6 s, mean ± std). TMS intensity: 80% of participants' individual motor threshold. TMS machine: Magstim Rapid2, Magstim Company. Coil: figure-of-eight coil (Double 70-mm Alpha Coil). Number of channels: 62-channel EEG channels (AFz and TP9 are used as reference and ground, respectively)                      + 1 FDI EMG channel                 + 1 electrode on the outer canthus of the left eye to monitor eye movements. Sampling rate: 5000 Hz.  Voltage resolution: 0.1 uV. EEG system: TMS-EEG compatible BrainAmp (Brain Products). Noise masking during TMS-EEG recordings: Yes.    Memory tasks data description Motor skill task 'motorSkill'-group (sub-01 to sub-15): TestSerial; averaged response time of the last 50 serial trials immediately after learning.                        TestRandom; averaged response time for the 50 subsequent random trials immediately after learning.                         RetestSerial; averaged response time for the last 50 serial trials 10h after learning.                                        RetestRandom; averaged response time for the 50 subsequent random trials 10h after learning. 'wordRecall'-group (sub-16 to sub-30): TestSerial; averaged response time for the last 50 serial trials immediately after learning.                        TestRandom; averaged response time for the 50 subsequent random trials immediately after learning.       'control'-group (sub-31 to sub-45):    TestRandom; averaged response time of the last 100 random trials at testing.                        RetestRandom; averaged response time of the last 100 random trials 10h after the tasks. Word recall task 'motorSkill'-group (sub-01 to sub-15): Test; number of words recalled immediately after learning. 'wordRecall'-group (sub-16 to sub-30): Test; number of words recalled immediately after learning.                        Retest; number of words recalled 10h after learning.       'control'-group (sub-31 to sub-45):    Test; number of words recalled immediately after learning.