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Figure S1 in Capelin beach spawning diaries: an analysis of 30 years of citizen science data from the island of Newfoundland, Canada
Figure S1. – Histograms of permutation test statistics testing the null hypothesis that the timing of first day of spawning was random amongst the three NAFO divisions (3KLPs). A) First day of spawning in Div. 3Ps was significantly earlier than in Div. 3L (two-tailed permutation test statistic: p = 0.0005) and B) Div. 3K (two-tailed permutation test statistic: p = 0.0005). C) There was no significant difference in first spawning day between Div. 3L and Div. 3K (two-tailed permutation test statistic: p = 0.588). The vertical line in each panel is the original test statistic.
Figure 1. – The 56 in Capelin beach spawning diaries: an analysis of 30 years of citizen science data from the island of Newfoundland, Canada
Figure 1. – The 56 capelin spawning beaches (red dots) that were monitored as part of the citizen science capelin spawning diary program along the southeastern and eastern coasts of Newfoundland, Canada (NAFO Divs. 3KLPs) for the years 1991-2021. FB (Fortune Bay), WB (White Bay), SPM (St. Pierre and Miquelon). There are four capelin stocks in the Northwest Atlantic: NAFO Divs. 2J3KL, NAFO Div. 3Ps, NAFO Div. 4RST, NAFO Divs. 3NO. Grey contours are 100 m and dark grey contours are 500 m bathymetry.
FIGURE 8 in An analysis of fossil identification guides to improve data reporting in citizen science programs
FIGURE 8. Cluster analyses of all subjects as individuals. Subject labels denote which field guide the subject tested, a red 'C' for color photos, a black 'G' for grayscale photos, and a blue 'I' for illustrations. Author names are abbreviated "DaBu" for Dava Butler, "DoEs" for Donald Esker and "KrJu" for Kristopher Juntunen.
FIGURE 1 in An analysis of fossil identification guides to improve data reporting in citizen science programs
FIGURE 1. Map of the United States and surrounding regions, showing the location of the state of Florida and FMMS (Google 2017). 1B: Geologic map of the State of Florida, showing the geographic distribution of rocks and the location of the FMMS (Google 2017; Scott et al. 2001). 1C: Geologic map of the region around the FMMS, showing the distribution of rocks and location of the site (Google 2017; Scott et al. 2001). 1D: Photograph of FMMS, taken by Fred Mazza.
FIGURE 7 in Novel analysis of locality data can inform better inventory and monitoring practices for paleontological resources at John Day Fossil Beds National Monument Oregon, USA
FIGURE 7. Boxplot of yield difference over area index by hiatus class. Note the median for each hiatus class is close to zero. This indicates similar amounts of collection between the earlier year and the later year regardless of the number of years the area has been left to erode.
FIGURE 6. Map showing estimated collection area. Points are field localities and 15 meter buffer shows estimated prospecting areas for 2008 in Novel analysis of locality data can inform better inventory and monitoring practices for paleontological resources at John Day Fossil Beds National Monument Oregon, USA
FIGURE 6. Map showing estimated collection area. Points are field localities and 15 meter buffer shows estimated prospecting areas for 2008 (yellow), 2009 (green), and 2010 (blue). Precise locality information is available to qualified researchers upon request from JODA's museum program.
FIGURE 9. All field collections from 2013 - early 2019 in Novel analysis of locality data can inform better inventory and monitoring practices for paleontological resources at John Day Fossil Beds National Monument Oregon, USA
FIGURE 9. All field collections from 2013 - early 2019 to be used for tracking previous collection area. Points outside of JODA boundaries are BLM, USFS, or private localities. Precise locality information is available to qualified researchers upon request from JODA's museum program.
Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis
<p><strong><span>Background: </span></strong><a name="_Hlk170909708"></a><span>The study analyzes the impact of public health spending on malnutrition among Peruvians, using data from the National Household Survey, the Central Reserve Bank of Peru, the National Institute of Statistics and Informatics and the Ministry of Economy and Finance from 2010. -2020. Previous studies have revealed the existing relationship of health spending with the reduction of malnutrition</span><span>.</span></p> <p><strong><span>Methods:</span></strong><span> A quantitative approach is considered, with an explanatory type of research using panel data methodology considering the bidimensionality of the data, which allows quantifying this effect for the Peruvian case using the National Household Survey, data from the Central Reserve Bank of Peru, as well as information from the National Institute of Statistics and Informatics and the</span><strong><span> </span></strong><span>Transparency Portal of the Ministry of Economy and Finance in the period 2010-2020.</span><strong><span> </span></strong></p> <p><strong><span>Results: </span></strong><span>The results show that public expenditure on health has a negative relationship with malnutrition; the rural sector has a positive relationship with malnutrition given the limitations present for access to adequate food. Similarly, the unemployment rate shows a positive relationship with malnutrition, given that being unemployed leads to a higher cause of malnutrition in the population, and the gross domestic product has a negative relationship with malnutrition, given that greater economic growth produces an impact on reducing malnutrition, with the greatest impact being on the rural population and the gross domestic product. </span></p> <p><strong><span>Conclusions:</span></strong><span> In the analysis period 2010-2020 in Peru, based on the panel data analysis, the impact of public health expenditure on reducing malnutrition is observed in 10 departments, achieving a reduction in malnutrition; while in 14 departments, this indicator has not been reduced.</span></p>
Data and scripts for: Bayesian Phylogenetic Analysis on multi-core Compute Architectures: Implementation and evaluation of BEAGLE in RevBayes with MPI
<p>Phylogenies are central to many research areas in biology and commonly estimated using likelihood-based methods. Unfortunately, any likelihood-based method, including Bayesian inference, can be restrictively slow for large datasets–with many taxa and/or many sites in the sequence alignment–or complex substitution models. The primary limiting factor when using large datasets and/or complex models in probabilistic phylogenetic analyses is the likelihood calculation, which dominates the total computation time. To address this bottleneck, we incorporated the high-performance phylogenetic library BEAGLE into RevBayes, which enables multi-threading on multi-core CPUs and GPUs, as well as hardware-specific vectorized instructions for faster likelihood calculations. Our new implementation of RevBayes+BEAGLE retains the flexibility and dynamic nature that users expect from vanilla RevBayes. Additionally, we implemented a native parallelization within RevBayes without an external library using the message passing interface (MPI); RevBayes+MPI. We evaluated our new implementation of RevBayes+BEAGLE using multi-threading on CPUs and a powerful NVidia Titan V GPU against our native implementation of RevBayes+MPI. We found good improvements in speedup when multiple cores were used with up to 20-fold speedup when using multiple CPUs and over 90-fold speedup when using multiple GPU cores. The improvement depended on the data type used, DNA or amino acids, and the size of the alignment, but less on the size of the tree. We additionally investigated the cost of rescaling partial likelihoods to avoid numerical underflow and showed that unnecessarily frequent rescaling can increase runtimes 2.5 to 3-fold. Finally, we presented and compared a new approach to store partial likelihoods on branches instead of nodes which can speed up computations but comes at twice the memory requirements.</p> <p>Availability: The software described in the paper is available at https://github.com/revbayes/revbayes with documentation and tutorials found at https://revbayes.github.io.</p>
Data & Analysis Script for: Phylogenetic relatedness to native congeners drives insect abundance and diversity hosted by non-native trees
<p>The dataset contains all necessary data to reproduce the findings presented in Schweiger et al. 2023 - Phylogenetic relatedness to native congeners drives insect abundance and diversity hosted by non-native trees (submitted).</p> <p>The code necessary to reproduce the findings is included within this repository. The code contains comments. Please note, if you want to reproduce the findings you will have to change file path information matching your personal computer to be able to re-run the code.</p> <p>This data includes the biodiversity raw data collected for the manuscript. It <strong>does not </strong>include data used to calculate geographic, climatic or phylogenetic distances, as these data are freely available and necessary information to reproduce calculations are given within the Material & Methods section.</p> <p>All data is provided within one Excel file. Please, pay attention to the provided ReadMe sheet containing metadata information on the dataset.</p> <p>Please carefully read provided information within ReadMe, Metadata and Code description.</p>
Experimental data related to the publication: "In-situ analysis of the effect of residual fcc phase and special grain boundaries on the deformation dynamics in pure cobalt"
<p>The article figures were produced solely from these data sets employing data processing methods described therein. For experimental conditions and naming conventions please refer to the paper.</p> <p><br>1. Deformation data files contained within "deformation_data.zip":</p> <p>The .zip archive contains five files related to five samples of thermally treated cobalt:<br>def_co600.csv<br>def_co800.csv<br>def_co1100.csv<br>def_co1100-10c.csv<br>def_co1100-20c.csv</p> <p>The data were recorded during compression of the above-listed samples at room temperature. </p> <p><br>2. Acoustic emission (AE) data files contained within "AE_data.zip":</p> <p>The .zip archive contains four files related to four samples of thermally treated cobalt:<br>AE_co600.wav<br>AE_co800.wav<br>AE_co1100.wav<br>AE_co1100-20c.wav</p> <p>The AE data were recorded in continuous mode ("data streaming" at 2 MHz) during compression of the above-listed samples at room temperature. </p> <p> </p> <p>3. Electron back-scatter diffraction (EBSD) data files contained within "EBSD_data.zip":</p> <p>The .zip archive contains fifty-three .osc files related to samples of as-drawn and thermally treated cobalt within four folders:<br>0c - as-drawn and annealed samples (i.e. without thermal cycling)<br>10c - annealed samples after thermal cycling of 10 cycles<br>20c - annealed samples after thermal cycling of 20 cycles<br>ex-situ_def - ex-situ EBSD during deformation of selected samples</p> <p>The .osc data files represent EBSD data after clean-up procedures described in detail in the manuscript.</p> <p> </p> <p> </p> <p> </p>
Fig. 3. Phylogenetic trees from reported 18S in Molecular systematics analysis of Lymantria dispar based on 18S rRNA and cox1 mtDNA sequence data
Fig. 3. Phylogenetic trees from reported 18S rRNA genes of insects according to NJ. A. Based on sequences of full-length. B. Based on second conserved region.
Fig. 4 in Molecular systematics analysis of Lymantria dispar based on 18S rRNA and cox1 mtDNA sequence data
Fig. 4. Phylogenetic trees based on partial sequences from reported cox1 genes of insects according to NJ.
Fig.1 in Molecular systematics analysis of Lymantria dispar based on 18S rRNA and cox1 mtDNA sequence data
Fig.1. PCR result of 18S rRNA of Lymantria dispar. Separated bands (from left to right). 18S1, 18S2, 18S rRNA, DL2000 marker.
Results of a Galaxy metagenomic analysis of bee gut microbiome data from PRJNA977416
<p>This dataset contains the outputs of a metagenomic Galaxy workflow run on the raw data of the project PRJNA977416, including the CSV file of associated metadata and the workflow.ga used for the analysis.</p> <p>Firstly, it has information on taxonomic assignment with :</p> <ul> <li>the reports of all samples for Kraken2, Bracken, and MetaPhlan taxonomic profilers. </li> <li>two tabular files obtained with Taxpasta, which merge samples and standardize taxonomic abundances.</li> <li>for the Bracken standardised abundance, a file with the measures of alpha diversity calculated </li> <li>two HTML files giving access to the Krona diagram for this taxonomic composition.</li> </ul> <p>Secondly, it contains functional informations with :</p> <ul> <li>a tabular file with the relative abundance of all GO terms for all samples</li> <li>a directory detailing pathways and genes families detected.</li> </ul>
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019) in Variability of the gene cyt b in the Korean field mouse Apodemus peninsulae Thomas, 1906 - a reservoir host of AMRV in the Khasansky District of Primorsky Krai
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019)
Fig. 3 Trees obtained under the implied weighting using three concavity values k in First phylogenetic analysis of the tribe Oligaphorurini (Collembola: Onychiuridae) inferred from morphological data, with implications for generic classification
Fig. 3 Trees obtained under the implied weighting using three concavity values k = 6 (a), 9 (b), and 12 (c)
Fig. 4 Abdominal sternite IV in First phylogenetic analysis of the tribe Oligaphorurini (Collembola: Onychiuridae) inferred from morphological data, with implications for generic classification
Fig. 4 Abdominal sternite IV showing organization of furcal remnant. a, b Oligaphorura ursi Fjellberg, 1984; c Micraphorura gamae Buşmachiu and Weiner, 2013; d Oligaphorura groenlandica (Tullberg, 1876); e Dimorphaphorura inya Weiner and Kaprus, 2014; f Protaphorura eichhorni (Gisin, 1954)
Data and code for behavioral analysis of: Structural and Molecular Properties of Insect Type II Motor Axon Terminals.
<p>Data and code for behavioral analysis of: Structural and Molecular Properties of Insect Type II Motor Axon Terminals.</p> <p>v1.2: typos corrected and all files available in a single .zip file for download</p>
Microsat Data for 'Simulated Disperser Analysis: determining the number of loci required to genetically identify dispersers'
<p>Microsattelite data from 94 samples (<em>Stunus vulgaris</em>) from 3 populations and including 29 loci. Used in the paper 'Simulated Disperser Analysis: determining the number of loci required to genetically identify dispersers'. </p>
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.