Find research datasets worth reusing

Data from: Dynamic simulations of microbial communitiesunder perturbations: opportunities formicrobiome engineering

Input and output files of the application of our MDPbiomeGEM system (available at https://github.com/beatrizgj/MDPbiomeGEM) to different microbial communities, in particular from human gut microbiome and soil microbiome. Each case study (i.e. microbial community) has the following structure, corresponding to the different steps performed by our system MDPbiomeGEM: 1. InputGEM: Genome-scale metabolic models (GEM) and medium composition 2. OutputMMODES: simulated microbial community timeseries by MMODES 3. MmodesToMDPbiome: transformation of output from MMODES in input to MDPbiome 4. OutRobustClustering: identification of microbiome states 5. OutputMDPbiome: output of MDPbiome, with prediction of microbiome state changes and interventions a. Human gut microbiome (BifFae): the microbes in this community are Bifidobacterium adolescentis L2-32 (iBif452) and Faecalibacterium prausnitizii A2-135 (iFap484), whose GEMs were taken from the original publication [El-Semman et al.,2014] (https://doi.org/10.1186/1752-0509-8-41). We upload here those GEMs adjusted to satisfy the scenario we model, that is described in our manuscript. b. Soil microbiome (Atrazine): the microbes in this community are P. aurescens, H. stevensii, Halobacillus sp. The GEMs used in this consortium were kindly provided by the authors of [Xu et al.,2019] (https://doi.org/10.1038/s41396-018-0288-5). Model construction was lead by Raphy Zarecki. With the exception of P.aurescens TC1 [Ofaim et al.,2019] (http://dx.doi.org/10.1101/536011) they had not yet been made publicly available. With the permission of the authors of the original models [Xu et al.,2019], we upload here those GEMs adjusted to satisfy the scenario we model, that is described in our manuscript.

Molecular dynamics simulation of Conus textile conotoxin Txd13 in complex with a3b2, a3b4, a6b2 or a6b4 nAChR subtypes.

This folder contains the coordinate and parameter files used to run molecular dynamics simulations of the toxin Txd13 (sequence GCCSNPPCIANPMC) in complex with four nicotinic acetylcholine receptor (nAChR) subtypes: a3b2, a3b4, a6b2 and a6b4 nAChRs. For each system several files are provided: 1) an homology model that was used as a starting conformation is provided (eg   'a3b2_txd13.B99990023.pdb'), 2) an Amber Parm7 topology file (eg 'a3b2_txd13_0023.prmtop'), 3) a trajectory files containig 1250 frames extracted from a 100 ns molecular dynamics simulation (eg 'a3b2_txd13_0023_md_smaller.nc') created using pmemd from the Amber 18 package, 4) the log file of this simulations (eg 'a3b2_txd13_0023_md.log'), and 5) the coordinate file representing the minimized verion of the centroid frame of each simulation (with water and ions removed for conveniance) (eg 'a3b2_txd13_0023_md_centroid_min_nowat.pdb') The parameters used for the molecular dynamics simulations are provided in the 'md.in' file. All the text files have been compressed in the 'xz' format

Dataset of Synchrotron Low Energy XRF and STXM files used in a manuscript on "Compressive Sensing for Dynamic XRF Scanning"

Synchrotron Low Energy XRF and STXM Dataset used in a research manuscript on "Compressive Sensing for Dynamic XRF Scanning". This dataset includes HDF5 files with XRF (/dante) and STXM (/andor) maps and metadata such as XRF lifetime and sample stage positions (/sample_motors). The dataset also includes as TIFF images various outputs such as the sparse maps, the masked areas and the results of in-painting methods. In the DAT file, there is the output of the fitted XRF data as ASCII from PyMCA. In HTML there is included the relevant part of the electronic logbook (DonkiLOG). These data were acquired during the beamtime experiments 20180178 and 20192072 in the <a href="http://www.elettra.eu/elettra-beamlines/twinmic.html">TwinMic</a> soft X-ray microscopy beamline of Elettra Sincrotrone Trieste.

opencc-by-4.0Mar 2020View details →

Files for Molecular Dynamics Simulation of COSAN

Topology file (.top), force field parameters file (.prm) and coordinate file (.pdb) for Molecular Dynamics Simulations of COSAN molecule compatible with CHARMM force field. Files based on the parameters reported in: D.C. Malaspina, C. Viñas, F. Teixidor, J. Faraudo "Atomistic Simulations of COSAN: Amphiphiles without a Head-and-Tail Design Display “Head and Tail” Surfactant Behavior", Angewandte Chemie International Edition  Vol 59(8), Pages 3088-3092 (2020) DOI: <a href="https://doi.org/10.1002/anie.201913257">10.1002/anie.201913257</a>

Dynamic 3D X-ray micro-CT data of a tablet dissolution in a water-based gel with dynamic changes in the scanning geometry

Summary This submission contains a dynamic tomographic X-ray data of a tablet dissolving in a water-based gel. The data is collected over a 5-minute period during which the sample is rotated rapidly as effervescent bubbles are formed and travelling to the surface of the gel. This is the second experiment detailed in Case Study 3 in [Coban 2020], and this submission can be treated as a follow up to [Coban&Lucka 2019].    Apparatus The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban 2020].   Sample Information The setup consists of a store-bought denture cleaning tablet, placed at the bottom of a clear cylindrical plastic container. These tablets are typically designed to be fast-dissolving, and produce small and compact channels of bubbles. We use a denture cleaning tablet in particular as the dissolution time in water varies from 3 to 5 minutes, meaning the bubbles are produced at a slower rate. In addition, we use a store-bought water-based gel instead of water to slow down the bubble displacement during the experiment.   Experimental Plan This experiment is performed such that 150 projections are collected over 360 degrees, for a total of 166 rotations, with exposure time 12 ms for each projection. This means that in total the submission contains 25000 projections. This experiment took 5 minutes of acquisition time, during which we (at user's command) zoom in onto the bottom of the sample holder (i.e. where the tablet rests). We later (again, at user's command) shift the view (i.e. the tube and the detector) upwards to the top of the sample to observe foaming on the surface. Finally, before the end of the 5-minute acquisition period, we zoom out to the original magnification. Every time the geometry undergoes a major change such as zooming in (which would affect the reconstruction), the system creates a new data settings file with the new geometrical information, appended by the projection number, therefore marking the change. However, since there is no major change created by the vertical shift of the tube and detector (as in no change in geometry that would affect the reconstructed images), there is no new data settings file for this event.  The spatial resolution for this data is 193μm at the beginning (or end) of the experiment, which at an arbitrary point changes to 76μm. For a smooth data transfer, each projection image is binned down to the size of 486px-by-384px. No centrifugal force effect was observed on the bubbles travelling during the scan or in our test runs at the given rotational speed. All raw data (i.e. with no corrections) is made available in .tif format.   List of Contents The contents of the submission is given below. <ul> <li>scan_1: A 5-minute dynamic CT data folder containing <ul> <li>dark-field (or closed-shutter) image, di000000.tif,</li> <li>pre flat-field (or open-shutter before acquisition) image, io000000.tif,</li> <li>post flat-field (or open-shutter after acquisition) image, io000001.tif,</li> <li>raw (unprocessed or uncorrected) projections, scan_*.tif (25000 projections in total),</li> <li>data settings XRE.txt, a text file with scanner metadata (this is the final geometry info file),</li> <li>data settings XRE_5220.txt (geometry info recorded after the zoom-in)</li> <li>data settings XRE__22610.txt (geometry info recorded after the zoom-out, same as data settings XRE.txt)</li> </ul> </li> </ul>   Additional Links These datasets are produced by the <a href="https://www.cwi.nl/research/groups/computational-imaging">Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.   Contact Details For more information or guidance in using these dataset, please get in touch with  <ul> <li>s.b.coban [at] cwi.nl</li> </ul>   Acknowledgments We thank Dr. Samuel McDonald and Prof. Philip Withers for the useful discussion, and Dr. Manuel Dierick for his advice in making this experiment possible.

opencc-by-4.0Oct 2019View details →

Dynamic 3D X-ray micro-CT data of a tablet dissolution in a water-based gel

Summary This submission contains a dynamic tomographic X-ray data of a tablet dissolving in a water-based gel. The data is collected over a 5-minute period during which the sample is rotated rapidly as effervescent bubbles are formed and travelling to the surface of the gel. The data is made available as part of Case Study 3 in [Coban 2020].   Apparatus The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban 2020].   Sample Information The setup consists of a store-bought denture cleaning tablet, placed at the bottom of a clear cylindrical plastic container. These tablets are typically designed to be fast-dissolving, and produce small and compact channels of bubbles. We use a denture cleaning tablet in particular as the dissolution time in water varies from 3 to 5 minutes, meaning the bubbles are produced at a slower rate. In addition, we use a store-bought water-based gel instead of water to slow down the bubble displacement during the experiment.   Experimental Plan   This experiment is performed such that 120 projections are collected over 360 degrees, for a total of 83 rotations, with exposure time 30 ms for each projection. This means that in total the submission contains 10000 projections. This took a total of 5 minutes of acquisition time, during which the tablet moved due to saturation but did not completely dissolve. The spatial resolution is 95μm, and the field of view was cropped to the boundaries of the sample holder (each projection image is of size 647px×768px). Our experimental setup allowed the collection of open and closed shutter (flat- and dark-field) images before decanting the gel onto the tablet. No centrifugal force effect was observed on the bubbles travelling during the scan or in our test runs at the given rotational speed. All raw data (i.e. no corrections) is made available in .tif format.   List of Contents The contents of the submission is given below. <ul> <li>scan: A 5-minute dynamic CT data folder containing <ul> <li>dark-field (or closed-shutter) image, di000000.tif,</li> <li>pre flat-field (or open-shutter before acquisition) image, io000000.tif,</li> <li>post flat-field (or open-shutter after acquisition) image, io000001.tif,</li> <li>raw (unprocessed or uncorrected) projections, scan_*.tif (10000 projections in total),</li> <li>data settings XRE.txt, a text file with scanner metadata,</li> <li>script.txt and script_executed.txt are the text files containing the list of commands the apparatus has executed.</li> </ul> </li> </ul>   Additional Links These datasets are produced by the <a href="https://www.cwi.nl/research/groups/computational-imaging">Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.   Contact Details For more information or guidance in using these dataset, please get in touch with  <ul> <li>s.b.coban [at] cwi.nl</li> </ul>   Acknowledgments We thank Dr. Samuel McDonald and Prof. Philip Withers for the useful discussion, and Dr. Manuel Dierick for his advice in making this experiment possible.

opennotspecifiedDec 2007View details →

FIGURE 2 in Craniocervical feeding dynamics of Tyrannosaurus rex

FIGURE 2. Position vectors of major neck muscles in Tyrannosaurus rex (AMNH 5027; skeletal drawings modified from Paul 1988), with the neck in a neutral to slightly elevated posture. Note that 3-D summation of these vectors yields direction for lines of muscle pull. Muscle abbreviations, origins, and insertions are as listed in Table 1; dots indicate attachment sites and via points. The top figure shows the scale, and vector axes in the frontal and sagittal planes. A, C, and E depict the skeleton and lines of action in dorsal view, and B, D, and F show these in lateral view. In B and E, bones of the neck are shown as dashed lines so that muscle lines of action are not obscured. In C and D, bones are shown as dashed lines to indicate that M. r. c. v. passes ventral or medial to them. A, B, M. longissimus capitis superficialis (M. long. cap. sup.: dark lines) and M. complexus (lighter-shaded lines). C, D, M. longissimus capitis profundus (M. long. cap. prof.; dashed black lines in D) and M. rectus capitis ventralis (M. r. c. v.). E, F, M. transversospinalis capitis (M. trans. cap.: dark gray lines) and M. iliocostalis capitis (M. il. cap.: black lines).

Jovian and Kronian Magnetodisc Field and Guiding Centre Dynamics of Trapped Particles Data

Data files and basic Matlab visualisation functionality of Jovian and Kronian UCL magnetodisc model output and guiding centre dynamics of trapped particles data described in the JGR Space Physics paper 2020JA027827 Trapped Particle Motion In Magnetodisk Fields by Guio, P. and Staniland, N. and Achilleos, N. A. and Arridge, C. S.(<a href="https://doi.org/10.1029/2020JA027827">https://doi.org/10.1029/2020JA027827</a>)

War of Words: The Competitive Dynamics of Legislative Processes

Update: A newer version of this dataset is available <a href="https://zenodo.org/record/4709248#.YXesJS8itqs">here</a>. It comes with features extracted from the MEPs, the edits, and the dossiers, such as the nationality of MEPs, the type of law being edited, and the text of the edits. Check it out! This upload contains the dataset presented and used in the paper: <blockquote> Kristof, V., Grossglauser, M., Thiran, P., <a href="https://infoscience.epfl.ch/record/275473/">War of Words: The Competitive Dynamics of Legislative Processes</a>, The Web Conference, April 20-24, 2020, Taipei, Taiwan </blockquote> Read Section 2.2 of the paper to learn more about the European legislative process. The code to process and use the dataset can be found on <a href="https://github.com/indy-lab/war-of-words">GitHub</a>. The dataset is split into two legislature periods of the European Parliament, the 7th (war-of-words-ep7.txt) and the 8th (war-of-words-ep8.txt) legislature. Here is a snippet to load the dataset (for EP7 in this example) in Python: <pre><code class="language-python">import json with open('path/to/war-of-words-ep7.txt') as f: dataset = [json.loads(l) for l in f.readlines()] </code></pre> In the two text files, each line is a data point representing a conflict between edits. It is encoded as a JSON list of dictionaries, where each dictionary is an edit. Each edit has the following structure: <pre><code class="language-json">{ 'edit_id': 163187, // Unique edit identifier. 'accepted': True, // Label. 'dossier_ref': 'ENVI-AD(2012)487738', // Reference to dossier (see below). 'authors': [ // List of authors. { 'id': 4550, // Unique MEP identifier (see below). 'name': 'Jill EVANS', // MEP name. 'rapporteur': False // Whether the MEP is rapporteur. }, ], }</code></pre> You can assume that: <ul> <li>Each data point has at least one edit.</li> <li>If there is only one edit, then it is in conflict with the status quo (see Section 4 of the paper).</li> <li>If there are two or more edits in conflict, then they are all in conflict against each other and they are in conflict with the status quo (see Section 4 of the paper).</li> <li>At most one edit is accepted in each data point.</li> <li>In each legislature, each edit has a unique identifier.</li> <li>There are no timestamps associated with edits (see Section 3 of the paper). </li> </ul> The dossier_ref can be used to get more information on the dossier. It is formatted as COMM-TYPE(YEAR)PENUMBER (this follows the notation of file names used by the Parliament Secretariat), where <ul> <li>COMM is the <a href="https://www.europarl.europa.eu/committees/en/about/introduction">committee</a> identifier (4 capital letters)</li> <li>TYPE is either AD (opinion) or A{7,8} (report for EP7 or EP8, see Section 2.2 of the paper)</li> <li>YEAR is the year the dossier has been voted</li> <li>PENUMBER is the "PE number", a document identifier used by the European Parliament</li> </ul> You can browse the Parliament documents to find details about the dossier for <a href="https://www.europarl.europa.eu/committees/en/archives/7/document-search">EP7</a> and <a href="https://www.europarl.europa.eu/committees/en/archives/8/document-search">EP8</a> (the PE number field should be enough). The parliamentarians (MEPs, for Member of the European Parliament) have a unique identifier that you can use to get more details about them on the Parliament website: Go to https://www.europarl.europa.eu/meps/en/MEP_ID, where MEP_ID is the id of the MEP of interest. This dataset is vowed to become richer: I will add more features, as I am able to extract them.   Don't hesitate to <a href="mailto:victor.kristof@epfl.ch?subject=Question%20about%20the%20War%20of%20Words%20dataset">reach out to me</a> if you have any questions!   To cite this work: <pre><code>@inproceedings{kristof2020war, author = {Kristof, Victor and Grossglauser, Matthias and Thiran, Patrick}, title = {War of Words: The Competitive Dynamics of Legislative Processes}, year = {2020}, booktitle = {Proceedings of The Web Conference 2020}, pages = {2803–2809}, numpages = {7}, location = {Taipei, Taiwan}, series = {WWW '20} }</code></pre>

opencc-by-4.0Mar 2020View details →

Research data supporting "Dynamic pH responsivity of triazole-based self-immolative linkers"

Raw data supporting: Roberts D.A. et al., (2020), Chemical Science, DOI: 10.1039/d0sc00532k.

Inflow dynamics in weakly stratified lakes subject to large isopycnal displacements (figure data)

Data displayed in the figures of the article entitled "Inflow dynamics in weakly stratified lakes subject to large isopycnal displacements". Water Resources Research, 56, e2019WR026578. <a href="https://doi.org/10.1029/2019WR026578">https://doi.org/10.1029/2019WR026578</a>

Data and code for changes in contact patterns shape the dynamics of the novel coronavirus disease 2019 outbreak in China

Data and code for the paper "changes in contact patterns shape the dynamics of the novel coronavirus disease 2019 outbreak in China" published in Science.

Replica exchange molecular dynamics simulation data of designed β-hairpins (implicit solvent, AMBER ff99SB-ildn-nmr)

Raw REMD simulation data (protein only) of designed β-hairpins. AMBER ff99SB-ildn-nmr and implicit solvent model is used. More details can be found in this paper:  Yunhui Ge, Brandon Kier, Niels H. Andersen and Vincent A. Voelz. <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.7b00132">Computational and experimental evaluation of designed beta-cap hairpins using molecular simulations and kinetic network models.</a> J. Chem. Inf. Model., 2017, 57 (7), pp 1609–1620

Benchmark circuits used in the work entitled "Sequential Logic Encryption with Dynamic Keys"

Benchmark circuits used in the work entitled "Sequential Logic Encryption with Dynamic Keys". The original benchmarks are from LGSynth’91 (downloaded from https://ddd.fit.cvut.cz/prj/Benchmarks/). Contents: oriCircuit: original benchmark circuits. k5r5/circuit: encrypted circuits with k=5 and r=5. k5r5/key: secret keys of the encrypted circuits with k=5 and r=5. k5r10/circuit: encrypted circuits with k=5 and r=10. k5r10/key: secret keys of the encrypted circuits with k=5 and r=10. k10r5/circuit: encrypted circuits with k=10 and r=5. k10r5/key: secret keys of the encrypted circuits with k=10 and r=5. k10r10/circuit: encrypted circuits with k=10 and r=10. k10r10/key: secret keys of the encrypted circuits with k=10 and r=10. k15r5/circuit: encrypted circuits with k=15 and r=5. k15r5/key: secret keys of the encrypted circuits with k=15 and r=5. k15r10/circuit: encrypted circuits with k=15 and r=10. k15r10/key: secret keys of the encrypted circuits with k=15 and r=10.

opencc-by-sa-4.0Feb 2020View details →

When and How to Transfer Knowledge in Dynamic Multi-objective Optimization

This file is the output data obtained when running the experiments of the paper below: Ruan, G., Minku, L.L., Menzel, S., Sendhoff, B., Yao, X., "When and How to Transfer Knowledge in Dynamic Multi-objective Optimization," 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 2019, pp. 2034-2041.    Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is able to transfer useful information from one problem to help solving another related problem. This paper aims to investigate when and how transfer learning works or fails in dynamic multi-objective optimization. Through computational analyses on a number of dynamic bi- and tri-objective benchmark problems, we show that transfer learning fails on problems with fixed Pareto optimal solution sets and under small environmental changes. We also show that the Gaussian kernel function used in the existing transfer learning-based method is not always adequate. Therefore, transfer learning should be avoided when dealing with problems for which transfer learning fails and other kernel functions should be used when the Gaussian kernel is inadequate. This paper proposes novel strategies and kernel functions that can be used in such cases. Experimental studies have demonstrated the superiority of our proposed techniques to state-of-the-art methods, on a number of dynamic bi- and tri-objective test problems.

opencc-by-sa-4.0May 2020View details →

Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization

This file is the output data obtained when running the experiments from the paper below: Ruan, G., Minku, L., Menzel, S., Sendhoff, B., Yao., “Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization” 2020 IEEE Congress on Evolutionary Computation Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is believed to be able to transfer useful information from one problem instance to help solving another related problem instance. This paper aims to study how effective transfer learning is in dynamic multi-objective optimization (DMO). Through computation time analysis of transfer learning, we show that the ‘inner’ optimization problem introduced by transfer learning is very time-consuming. In order to enhance the efficiency, two alternatives are computationally investigated on a number of dynamic bi- and tri-objective test problems. Experimental results have shown that the greatly enhanced efficiency does not result in much degeneration on the performance of transfer learning. Considering the high computational cost of transfer learning, it is likely that the original purpose of using transfer learning in DMO might be negated. In other words, the computation time saved in optimization is eaten up by computationally expensive transfer learning. As a result, there is less gain than expected in the overall computational efficiency. To verify this, experiments have been conducted, regarding using computational cost of transfer learning to optimize randomly generated solutions. The results have demonstrated that the convergence and diversity of final solutions generated from the random solutions are significantly better than those generated from transferred solutions under the same total computational budget.

Intra- and inter-annual dynamics of evaporation over western Lake Erie

This dataset if for a manuscript: Intra- and inter-annual dynamics of evaporation over western Lake Erie, by CHANGLIANG SHAO, JIQUAN CHEN, CAROL A. STEPIEN, HOUSEN CHU, ZUTAO OUYANG. The dataset including six sheets, from figure2 to figure 7 in the manuscript. Information shows as following: Fig. 2. Monthly mean (a) air temperature (Ta), (b) wind speed (U), (c) vapor pressure deficit (VPD), (d) photosynthetically active radiation (PAR), and total and cumulative (e) rainfall of ~15 m above the water surface at the CB and LI sites over Lake Erie from September 2011 to May 2016. Long-term (1893–2015) average (gray lines) and its 90% quantile intervals (gray shaded areas) are presented in (a). Fig. 3. Monthly average diurnal course of evaporation at the CB and LI sites in western Lake Erie. Data covered all of the measurement period. Fig. 4. Seasonal changes of net radiation (Rn) and latent heat flux (LE) at the two Lake Erie sites from 2011 through 2016. The monthly LE variations also are shown. The sine function was used to fit the dynamic seasonal variations (see equation 6). The sinusoidal period was set to 12.   Fig. 5. Linear relationships between the monthly lake evaporation (E) and the same month net radiation (Rn) (a and b), and 1-month early Rn (c and d) at the two sites. The lines represent the linear fitted tendency. Fig. 6. Linear relationship between modeled evaporation (E, Eq. (4)) and the measured values during the entire measurement period, on a monthly basis. The line represents the linear fitted tendency. Fig. 7. Relationships between latent heat flux (E) and (a, b) the product of U (wind speed) and vapor pressure deficit (VPD), (c, d) VPD, (e, f) air temperature, and (g, h) wind speed at the two sites, on a monthly basis. A linear fit with 95% confidence intervals, the correlation between the variables, and the linear model P value are shown in all plots.

opencc-zeroMay 2020View details →

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Data from: Thoracic underreplication in Drosophila species estimates a minimum genome size and the dynamics of added DNA

Many cells in the thorax of Drosophila were found to stall during replication, a phenomenon known as underreplication. Unlike underreplication in nuclei of salivary and follicle cells, this stall occurs with less than one complete round of replication. This stall point allows precise estimations of early-replicating euchromatin and late-replicating heterochromatin regions, providing a powerful tool to investigate the dynamics of structural change across the genome. We measure underreplication in 132 species across the Drosophila genus and leverage this data to propose a model for estimating the rate at which additional DNA is accumulated as heterochromatin and euchromatin and also predict the minimum genome size for Drosophila. According to comparative phylogenetic approaches, the rates of change of heterochromatin differ strikingly between Drosophila subgenera. While these subgenera differ in karyotype, there were no differences by chromosome number, suggesting other structural changes may influence accumulation of heterochromatin. Measurements were taken for both sexes, allowing the visualization of genome size and heterochromatin changes for the hypothetical path of XY sex chromosome differentiation. Additionally, the model presented here estimates a minimum genome size in Sophophora remarkably close to the smallest insect genome measured to date, in a species over 200 million years diverged from Drosophila.

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Quantifying spatiotemporal occupancy dynamics and multi-year core use areas at a species range boundary

Aim Many species face large-scale range contractions and predicted distributional shifts in response to climate change, shifting forest characteristics, and anthropogenic disturbances. Canada lynx (Lynx canadensis) are listed as threatened under the U.S. Endangered Species Act and were recently recommended for delisting. Predicted climate-driven losses in habitat quality and quantity may negatively affect the northeastern Minnesota lynx population, one of six remaining resident populations in the contiguous United States. We develop a large-scale monitoring protocol and dynamic occupancy modeling framework to identify multi-year core use areas and track spatiotemporal occurrence at the southern periphery of the species range. Location Northeastern Minnesota lynx geographic unit, Superior National Forest, and designated critical habitat, Minnesota, USA. Methods Spatially and temporally replicated snow track surveys were used to collect lynx detection/non-detection data across five winters (2014–15 to 2018–19) covering >17,000 km within the 22,100 km2 study area. We used a dynamic occupancy model to evaluate lynx occupancy, persistence, colonization, and habitat covariates affecting these processes. Results Lynx occupancy probabilities displayed high spatial and temporal variability, with grid cell-specific probabilities ranging from 0.0 in periphery regions to consistently near 1.0 in multi-year core use areas, indicating low turnover rates in those areas. Lynx colonization and persistence increased in areas with more evergreen forest and greater average snowfall, while forest characteristics (3–5 m and 10–30 m vegetation density) had mixed relationships with occupancy dynamics. We identified 55 grid cells classified as multi-year core use areas across relatively contiguous regions of high average snowfall and percent conifer forest. Main conclusions Our study demonstrates a landscape-scale multi-year monitoring program assessing the effects of habitat characteristics and anthropogenic factors on species distributional changes and landscape-level occupancy dynamics. Our framework incorporating landscape-scale resource selection, core use area concepts, and dynamic occupancy models provides a flexible approach to identify population-level mechanisms driving species persistence and key areas for conservation protection.

opencc-zeroApr 2021View details →

Household Dynamics in COVID times

This dataset is the result of an anonymous survey carried out during the COVID pandemic in Chile. We focus on household dynamics and the division of work in Chilean households. We also provide demographic information on respondents. If you wish to have the questionnaire (in Spanish) please email the authors