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dryad40/100

Conodont faunas across the Kasimovian–Gzhelian boundary (late Pennsylvanian) in South China and implications for the selection of the stratotype for the base of the global Gzhelian stage

The upper Pennsylvanian Naqing and Narao carbonate successions were deposited in intra-platform slope to basinal settings across the Kasimovian-Gzhelian boundary in Guizhou, South China. Conodont faunas comprise a mixture of the endemic taxa of the Idiognathodus luodianensis group and cosmopolitan species of the I. simulator group. The I. luodianensis group includes the new species I. fengtingensis, I. luodianensis, I. naqingensis and I. naraoensis. Platform landmark analysis demonstrates that the species of the I. luodianensis group differ in morphological features from co-occurring species of the I. simulator group. Both groups display similar increasing asymmetry in P1 element pairs across the Kasimovian-Gzhelian boundary, as recognized on the basis of the first occurrence of I. simulator. Many Kasimovian Idiognathodus species disappear and several Gzhelian species first appear with I. simulator, including two new species of Streptognathodus, S. nemyrovskae and S. zhihaoi. Just below the base of the Gzhelian, carbonate δ13C falls from 4‰ to 2‰ in both sections. The combination of an abrupt faunal turnover immediately above the prominent negative δ13C excursion might represent an oceanic event in South China, maybe recognizable on a global scale. One of these two South China sections may be the best location to place the GSSP for the base of the Gzhelian Stage.

opencc-zeroFeb 2020View details →

dryad40/100

The target of selection matters: an established resistance – development-time negative genetic trade-off is not found when selecting on development time.

Trade-offs are fundamental to evolutionary outcomes and play a central role in eco-evolutionary theory. They are often examined by experimentally selecting on one life-history trait and looking for negative correlations in other traits. For example, populations of the moth Plodia interpunctella selected to resist viral infection show a life-history cost with longer development times. However, we rarely examine whether the detection of such negative genetic correlations depends on the trait on which we select. Here we examine a well-characterised negative genotypic trade-off between development time and resistance to viral infection in the moth Plodia interpunctella and test whether selection on a phenotype known to be a cost of resistance (longer development time) leads to the predicted correlated increase in resistance. If there is tight pleiotropic relationship between genes that determine development time and resistance underpinning this trade-off, we might expect increased resistance when we select on longer development time. However, we show that selecting for longer development time in this system selects for reduced resistance when compared to selection for shorter development time. This shows how phenotypes typically characterised by a trade-off can deviate from that trade-off relationship, and suggests little genetic linkage between the genes governing viral resistance and those that determine response to selection on the key life-history trait. Our results are important for both selection strategies in applied biological systems and for evolutionary modelling of host-parasite interactions.

opencc-zeroMay 2020View details →

dryad40/100

Data from: An extinction event in planktonic Foraminifera preceded by stabilizing selection

Unless they adapt, populations facing persistent stress are threatened by extinction. Theoretically, populations facing stress can react by either disruption (increasing trait variation and potentially generating new traits) or stabilization (decreasing trait variation). In the short term, stabilization is more economical, because it quickly transfers a large part of the population closer to a new ecological optimum. However, canalization is deleterious in the face of persistently increasing stress, because it reduces variability and thus decreases the ability to react to further changes. Understanding how natural populations react to intensifying stress reaching terminal levels is key to assessing their resilience to environmental change such as that caused by global warming. Because extinctions are hard to predict, observational data on the adaptation of populations facing extinction are rare. Here, we make use of the glacial salinity rise in the Red Sea as a natural experiment allowing us to analyse the reaction of planktonic Foraminifera to stress escalation in the geological past. We analyse morphological trait state and variation in two species across a salinity rise leading to their local extinction. One species reacted by stabilization in shape and size, detectable several thousand years prior to extinction. The second species reacted by trait divergence, but each of the two divergent populations remained stable or reacted by further stabilization. These observations indicate that the default reaction of the studied Foraminifera is canalization, and that stress escalation did not lead to the emergence of adapted forms. An inherent inability to breach the global adaptive threshold would explain why communities of Foraminifera and other marine protists reacted to Quaternary climate change by tracking their zonally shifting environments. It also means that populations of marine plankton species adapted to response by migration will be at risk of extinction when exposed to stress outside of the adaptive range.

opencc-zeroOct 2019View details →

zenodo40/100

Fig. 5. Selected gastropods from the Goychay section. A in Magneto-biostratigraphic age constraints on the palaeoenvironmental evolution of the South Caspian basin during the Early-Middle Pleistocene (Kura basin, Azerbaijan)

Fig. 5. Selected gastropods from the Goychay section. A. Theodoxus pallasi; B. Theodoxus pallasi; C. Laevicaspia sp. D. Laevicaspia subcaspia; E. Caspia apsheronica; F. Caspia sp.; G. Clessiniola cf. subvariabilis; H. Ecrobia cf. grimmi; I. Laevicaspia subcaspia; J. Melanopsis bergeroni; K. Lymnaea sp.; L. Turricaspia sp.; M. Laevicaspia sp.; N. Streptocerella sp.; O. Gyraulus sp.; P. Valvata sp. (Scale bars 1 mm).

opencc-by-4.0Oct 2019View details →

zenodo40/100

Domain-specific model selection for structural identification of the Rab5-Rab7 dynamics in endocytosis - Additional Files

Additional files from the manuscript titled "Domain-specific model selection for structural identification of the Rab5-Rab7 dynamics in endocytosis" published in BMC Systems Biology: <ul> <li>Data containing delimited time points and measurements used for fitting the different model structures</li> <li>Supplementary material containing additional figures and tables</li> <li>Archive containing the complete library, the incomplete model and the task used for modeling the Rab5-Rab7 switch</li> </ul>

openbsd-3-clauseJun 2015View details →

zenodo40/100

Robot Self-Assembly as Adaptive Growth Process: Collective Selection of Seed Position and Self-Organizing Tree-Structures

Autonomous self-assembly allows to create structures and scaffolds on demand and automatically. The desired structure may be predetermined or alternatively it is the result of an artificial growth process that adapts to environmental features and to the intermediate structure itself. In a self-organizing and decentralized control approach the robots interact only locally and form the structure collectively. Designing a complete approach that allows the robot group to collectively decide on where to start the self-assembly, that adapts at runtime to environmental conditions, and that guarantees the structural stability is challenging and does not yet exist. We present an approach to self-assembly inspired by diffusion-limited aggregation that generates an adaptive structure reacting to environmental conditions in an artificial growth process. During a preparatory stage the robots collectively decide where to start the self-assembly also depending on environmental conditions. In the actual self-assembly stage, the robots create tree-like structures that grow towards light. We report the results of robot self-assembly experiments with 50 Kilobots. Our results demonstrate how an adaptive growth process can be implemented in robots. We explain how our approach will be extended to a 3-d growth process and how robot self-assembly as an open-ended adaptive growth process opens up a multiplicity of future opportunities.

opencc-by-4.0Jul 2016View details →

zenodo40/100

Optimized geometries for selected ions of ionic liquids and small molecules

The geometries of these selected chemical entities were optimized at the ab initio or semiempirical levels of theory. They can be conveniently used to create more complicated systems through combining species like the free PACKMOL software offers.

opencc-zeroAug 2016View details →

zenodo40/100

PanDDA analysis of SP100 screened against selection of Maybridge Fragment Library (HTML Summary)

Interactive summary page for "PanDDA analysis of SP100 screened against selection of Maybridge Fragment Library". Please click on "0_index.html" in the "Files" section to open the interactive summary. All datasets are also available as combined zip files from https://zenodo.org/record/48771 .

opencc-by-4.0Feb 2017View details →

zenodo40/100

Data for Applying Old Knowledge in Primary Study Selection

Three data sets (Hall, Wahono, Danijel) generated from existing systematic literature review publications. Hall.csv is generated from Hall, et al., "A systematic literature review on fault prediction performance in software engineering", 2012. Wahono.csv is generated from Wahono, et al., "A systematic literature review of software defect prediction: research trends, datasets, methods and frameworks", 2015. Danijel.csv is generated from Radjenović, et al., "Software fault prediction metrics: A systematic literature review", 2013. Each data set has two subset, which are derived from the original set by publication years. E.g. Hall2007-.csv contains examples in Hall.csv published before and in 2007, while Hall2007+.csv contains examples published after 2007. These data sets are used in our the newly submitted paper: "Testing Reading Tactics for Automated Reading Assistance: Is it Useful to Apply Old Knowledge?".

opencc-by-4.0Feb 2017View details →

zenodo40/100

2MASS star counts for Gaia TGAS selection function

This file contains star counts from the 2MASS point-source catalog (Skrutskie et al. 2006) for the purpose of determining the selection function of the Gaia DR1 Tycho-Astrometric Solution (Gaia collaboration et al. 2016; Lindegren et al. 2016). Counts are computed as a function of sky position (at HEALPix level 5 in RA, Dec), J-Ks color and a custom magnitude [J_T = J + (J − Ks )^2 + 2.5 (J − Ks )]. This file is designed to be used with the https://github.com/jobovy/gaia_tools package. The counts were performed using the following SQL query on a database that contains the full 2MASS PSC and a table that contains the HEALPix index of all stars in the 2MASS PSC at HEALPix level 12 in Ra, Dec (keys hp12index and pts_key in table twomass_psc_hp12): <blockquote> select floor((j_m+(j_m-k_m)*(j_m-k_m)+2.5*(j_m-k_m))*10), \ floor((j_m-k_m+0.05)/1.05*3), floor(hp12index/16384), count(*) as count \ from twomass_psc, twomass_psc_hp12 \ where (twomass_psc.pts_key = twomass_psc_hp12.pts_key \ AND (ph_qual like 'A__' OR (rd_flg like '1__' OR rd_flg like '3__')) \ AND (ph_qual like '__A' OR (rd_flg like '__1' OR rd_flg like '__3')) \ AND use_src='1' AND ext_key is null \ AND (j_m-k_m) > -0.05 AND (j_m-k_m) < 1.0 AND j_m < 13.5 AND j_m > 2) \ group by floor((j_m+(j_m-k_m)*(j_m-k_m)+2.5*(j_m-k_m))*10), \ floor((j_m-k_m+0.05)/1.05*3),floor(hp12index/16384) \ order by floor((j_m+(j_m-k_m)*(j_m-k_m)+2.5*(j_m-k_m))*10) ASC; </blockquote>

opencc-by-4.0Apr 2017View details →

zenodo40/100

Peak flow identified at selected GRDC stations and the corresponding hydrological and hydrometeorological state variables

Data set contains a list of peak flows at selected GRDC stations as well as the start, peak, and end dates of each selected event. Hydrometeorlogical variables and hydrological state variables simulated by a hydrological model E-HYPE corresponding to each selected event are also listed in the dataset. Further description and content of each data file is available in the included metadata.

opencc-by-4.0May 2017View details →

zenodo40/100

Supplementary material for "Playback experiments highlight the importance of nearest-neighbor distance and social information for nest site selection in the House Martin (Delichon urbicum)"

Abstract Understanding nest site selection is crucial for species conservation. Bird conservation often involves installing nesting aids to increase nest site availability and induce colonization of unoccupied sites. However, prospecting individuals must find nesting aids, which may be facilitated by social information. Here, we investigated the effectiveness of artificial nests and playback in the declining, migratory House Martin Delichon urbicum. We selected unoccupied sites with artificial nests along a distance gradient to occupied sites and broadcasted conspecific vocalizations during prospection times of House Martins in both the post- and the following pre-breeding periods. Visitation and colonization rates increased considerably in proximity to occupied sites. Playback during the post-breeding and pre-breeding periods enhanced visitation rates, while pre-breeding-only and post-breeding-only playback had smaller positive effects. Colonization rate increased exclusively with pre-breeding-only playback. Colonized playback and non-playback sites had similar breeding success, indicating that playback did not create ecological traps by attracting House Martins to suboptimal sites. Hence, broadcasting conspecific vocalizations informs prospecting birds of nest site availability, thereby increasing visitation, and to some degree, colonization of unoccupied House Martin sites. To boost colonization, we recommend installing artificial House Martin nests within approximately 500 meters of occupied sites and using playback of conspecific vocalizations.

opencc-by-4.0Jul 2024View details →

zenodo40/100

Data from: Stochastic phenotypic switching arises in response to directional selection in experimentally evolved multicellular yeast.

This BBC_2025__README.txt file was generated on 2025-09-24 by Beatriz Baselga Cervera GENERAL INFORMATION <ol> <li>Title of Dataset and code: Data from: Stochastic phenotypic switching arises in response to directional selection in experimentally evolved multicellular yeast.</li> </ol>   2. Author Information             Corresponding Investigator                         Name: Ph.D. Beatriz Baselga-Cervera                         Institution: University of Minnesota Twin cities, Minnesota, US.                         Email: <a href="mailto:bbaselga@umn.edu">bbaselga@umn.edu</a>; beabaselga@gmail.com             Co-investigator 1                         Name: Ph.D. Nahui Olin Medina-Chávez                         Institution: University of Minnesota Twin cities, Minnesota, US.                         Email: nmedinac@umn.edu             Co-investigator 2                         Name: Ph.D. Noah Gettle                         Institution: Wellcome Sanger Institute, Hinxton, UK.                         Email: nbgettle@gmail.com Co-investigator 3                         Name: Ph.D. Michael Travisano                         Institution: University of Minnesota Twin cities, Minnesota, US.                         Email: travisan@umn.edu     3. Data collectors: Ph.D. Beatriz Baselga-Cervera, Ph.D. Nahui Olin Medina-Chávez & Ph.D. Noah Gettle.   4. Date of data collection: 2022-2024   5. Geographic location of data collection: Saint Paul, US   6. Funding sources that supported the collection of the data: Fundación Alfonso Martín Escudero, Madrid, Spain (BBC).   7. Recommended citation for this dataset: Baselga-Cervera et al. (2024), Data from: Stochastic phenotypic switching arises in response to directional selection in experimentally evolved multicellular yeast.     DATA & FILE OVERVIEW   8. Description of dataset In this study, we address whether stochastic phenotypic switching can shape biological diversity contributing to evolutionary change across the transition from singles cells to multicellular clutters in Saccharomyces cerevisiae multicellular yeast system. Populations characterization was conducted with a Coulter Counter multisize 4, a FlowCam 3, under the optic microscope, via ACE2 gene sequencing and RNA sequencing and mathematical modeling. The populations studied were the genetically uniform diploid wild-type Saccharomyces cerevisiae Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T).   9. File list: ●      Coulter Counter size distribution data:  o   File 1 name:  File_1_Coulter_Counter_Counts_20h.csv o   File 1 description: Size distributions of Saccharomyces cerevisiae Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockout, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD at 20-hours growth. Data for: Fig. 1A, Fig. 3A and Fig. S2, Table S2 and Table S3.   o   File 2 name:  File_2_Coulter_Counter_Counts_24h.csv o   File 2 description: Size distributions of Saccharomyces cerevisiae Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockout, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD at 24-hours growth. Data for: Fig. 1A, Fig. 3A, Fig. S2, Table S2 and Table S3.   o   File 3 name:  File_3_Coulter_Counter_Counts_48h.csv o   File 3 description: Size distributions of Saccharomyces cerevisiae Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockout, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD at 48-hours growth. Data for: Fig. 1, Fig. 3A, Fig. S2, Table S2 and Table S3.   o   File name:  File_4_Coulter_Counter_Counts_Constructed_strains_diversity.csv o   File 4 description: Size distributions of the constructed ACE2 knockout and a strain containing the homozygous missense mutation (ACE2 c.1934 A>T) in YPD at 24h growth. Size distributions were obtained from populations before (initial) and five resuspended colonies obtained from small-size particles by plating the top fraction of the population after gravitational selection from three isolates per strain. Data for: Fig. 1, Fig. S2, Table S2 and Table S3.   o   File 5 name:  File_5_Coulter_Counter_Counts_Selection_Experiment.xlsx o   File 5 description: Size distributions of C1W8.1 and C1W8.2 multicellular evolved strains in YPD at 24h growth. Size distributions from the selection experiment for small-size particles by plating the top fraction of the population after gravitational selection over three cycles of selection. Data for: Fig. 2B and Fig. S6.   o   File 6 name:  File_6_Coulter_Counter_Counts_12h.xlsx o   File 6 description: Size distributions of C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockout, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD at 12-hours growth. Data for: Fig. 3A and Fig. S3.     ●      FlowCam data: o   File 7 name: File_7_Rawdata_FlowCam_all.csv o   File 7 description: FlowCam data from Saccharomyces cerevisiae Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD at 24h growth. Data for: Fig. S4.     ●      Data generated statistically: o   File 8 name: File_8_C1W8.2_overlapPairs_Selection_Experiment.csv o   File 8 description: overlapping indexes (η) of the KDE distributions were computed using the R-package ‘overlapping’ from the Coulter Counter data of the C1W8.2 derived strain over the selection experiment. Data for: Fig. S6D.   o   File 9 name: File_9_C1W8.1_overlapPairs_Selection_Experiment.csv o   File 9 description: overlapping indexes (η) of the KDE distributions were computed using the R-package ‘overlapping’ from the Coulter Counter data o   of the C1W8.1 derived strain over the selection experiment. Data for: Fig. S6C.     o   File 10 name: File_10_ overlapPairs_Constructed_strains_diversity.xlsx o   File 10 description: overlapping indexes (η) of the KDE distributions were computed using the R-package ‘overlapping’ from the Coulter Counter data of the constructed ACE2 knockout and a strain containing the homozygous missense mutation (ACE2 c.1934 A>T) in YPD at 24h growth. Size distributions were obtained from populations before (initial) and after gravitational selection of five resuspended colonies from three isolates per strain. Data for: Fig. S7.   ●      Data from ImageJ: o   File 11 name: File_11_ImageJ_analyses.xlsx o   File 11 description: ImageJ analyses of the microphotographs from Saccharomyces cerevisiae Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T). Cultures were grown in culture tubes with 10 ml of media, 50 mL Erlenmeyer flasks with 10 mL and 30 mL of media, in YPD under non-shaking and shaking at 250 rpm. YPD media was used across all conditions. Cultures were assessed after 24 hours growth at 30°C.  Microphotographs of each condition and strain were obtained with a Nikon TE2000 microscope using 10x objective.   ●      Pictures: o   File 12 name: File_12_ FlowCam_Pictures.zip o   File 12 description FlowCam IMAGES from Saccharomyces cerevisiae Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD at 24h growth. Data for: Fig. 1B.   o   File 13 name: File_13_Microphotography_controled_experimental_conditions.zip o   File 13 description: Microphotographs from Saccharomyces cerevisiae Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T). Cultures were grown in culture tubes with 10 mL of media, 50 mL Erlenmeyer flasks with 10 mL and 30 mL of media, in YPD under non-shaking and shaking at 250 rpm. YPD media was used across all conditions. Cultures were assessed after 24 hours of growth at 30°C. Pictures were obtained with a Nikon TE2000 microscope using 10x objective.     ●      Mathematical Model o   File 14 name: File_14_Mathematical_model.zip o   File 14 description: Mathematical model R code and generated values.   ●      ARN data o   File 15 name: File_15_rnaseq-final-results-Top_v_Bottom.xlsx o   File 15 description: RNA analyses final results Top vs Bottom phenotypic subdistributions. Top is used as control. o   File 16 name: File_16_Variant_Call_format_file.vcf o   File 16 description: Variant Calling analyses of the sample ARN sample Top 1. Adhesion number: SRR32105384.   ●      Time-lapse videos o   File 17 name: Supp. Video 1. C1W8.1 from 17 to 22 hours growth o   File 17 description: Supplementary Video 1. Experimentally evolved multicellular yeast video between 17 and 22 hours of growth (C1W8.1-derived strain) — time-lapse video of the formation of a single-cell propagule from a multicellular cluster. o   File 18 name: Supp. Video 2. Ace2x2KO over 26 hours growth. o   File 18 description: Supplementary Video 2. ace2Δ knockout constructed strain growth — time-lapse video of a single large multicellular cluster over 26 hours. o   File 19 name: Supp. Video 3. C1W8.1 over 6 hours growth o   File 19 description: Supplementary Video 3. Experimentally evolved multicellular yeast growth between 6 and 12 hours of growth (C1W8.1-derived strain). o   File 20 name: Supp. Video 4. C1W8.1 over 24 hours growth o   File 20 description: Supplementary Video 4. Experimentally evolved multicellular yeast growth over 24 hours (C1W8.1-derived strain) — cell division stops in small ancestral-like phenotypes. o   File 21 name: Supp. Video 5. Ace2x2KO over 24 hours growth o   File 21 description: Supplementary Video 5. ace2Δ knockout constructed strain growth — time-lapse video of multiple large multicellular clusters over 24 hours. o   File 22 name: Supp. Video 6. Ace2x2missense from 0 to 3h45m hours growth o   File 22 description: Supplementary Video 6. ace2Δ missense constructed strain growth — time-lapse video of multiple large multicellular clusters up to 3 hours 45 min.   METHODOLOGICAL INFORMATION Strains: ancestral wildtype (Y55 strains), C1W8.1 and C1W8.2 multicellular derived strains isolated after 60 days of selection in YPD media, constructed ACE2 gene knockouts, and strains containing the ACE2 missense mutation (ACE2 c.1934 A>T). Media: Growth media used in this study were Yeast Peptone Dextrose media (YPD; 1% (v/w) yeast extract, 2% (v/w) peptone, 2% (v/w) D-glucose, pH 5.8). Phenotypic characterization of the different strains was conducted in a Coulter Counter Multisizer 4 and FlowCam® 3.0 Fluid Imaging Technologies, optic microscopy and a mathematical model. Replicate populations of different individual isolates per strain were analyzed to obtain the population distributions in YPD media. RNA was extracted using an Invitrogen® PureLink RNA Mini Kit. Three out of four extracted samples per treatment with the highest RNA integrity score were submitted for TrueSeq Stranded RNA-Seq.     10. Detailed description ●      Coulter Counter size distribution data of all the populations:  o   File 1 name:  File_1_Coulter_Counter_Counts_20h.csv o   File 1 description: strains naming convention; strain_Isolate_run.pseudoreplicate. Strains: ace2x2m=strains containing the ACE2 missense mutation (ACE2 c.1934 A>T); ace2x2= ACE2 knockout; C1W8.1= C1W8.1 evolved multicellular strain; C1W8.2= C1W8.2 evolved multicellular strain; Y55= ancestral strain. §  Page 1: Column 1: Volume (um3) Column 2: Diameter (um2) Columns 3 to the last column: strains counts.   o   File 2 name:  File_2_Coulter_Counter_Counts_24h.csv o   File 2 description: strains naming convention; strain_Isolate_run.pseudoreplicate. Strains: ace2x2m=strains containing the ACE2 missense mutation (ACE2 c.1934 A>T); ace2x2= ACE2 knockout; C1W8.1= C1W8.1 evolved multicellular strain; C1W8.2= C1W8.2 evolved multicellular strain; Y55= ancestral strain. §  Page 1: Column 1: Volume (um3) Column 2: Diameter (um2) Columns 3 to the last column: strains counts.   o   File 3 name:  File_3_Coulter_Counter_Counts_48h.csv o   File 3 description: strains naming convention; strain_Isolate_run.pseudoreplicate. Strains: ace2x2m=strains containing the ACE2 missense mutation (ACE2 c.1934 A>T); ace2x2= ACE2 knockout; C1W8.1= C1W8.1 evolved multicellular strain; C1W8.2= C1W8.2 evolved multicellular strain; Y55= ancestral strain. §  Page 1: Column 1: Volume (um3) Column 2: Diameter (um2) Column 3 to the last column: strains counts.   o   File_4_Coulter_Counter_Counts_Constructed_strains_diversity.csv o   File 4 description: strains naming convention; strain_Isolate_colony_run.pseudoreplicate. Strains: ace2x2m=strains containing the ACE2 missense mutation (ACE2 c.1934 A>T); ace2x2= ACE2 knockout. §  Page 1: Column 1: Volume (um3) Column 2: Diameter (um2) Column 3 to the last column: strains counts.   o   File_5_Coulter_Counter_Counts_Selection_Experiment.xlsx o   File 5 description: strains naming convention; strain_colony.phenotype_selection.cycle_run.pseudoreplicate. Strains: C1W8.2= C1W8.2 evolved multicellular strain and C1W8.1= C1W8.1 evolved multicellular strain. §  Page 1: Column 1: Volume (um3) Column 2: Diameter (um2) Column 3 to the last column: strains counts.   o   File 6 name:  File_6_Coulter_Counter_Counts_12h.xlsx o   File 6 description: Size distributions of C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockout, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD at 12-hours growth. Data for: Fig. 3A and Fig. S3. §  Page 1: Column 1: Volume (um3) Column 2: Diameter (um2) Column 3: Time Column 4: replicate Column 5: Strain name (strain_f) Column 6: Isolate (isolate_f)         o   File 7 name: File_3_Rawdata_Flowcam_all.csv o   File 7 description: strains naming convention; ace2_isolate= ACE2 knockout; Ace2m_isolate= strain containing the ACE2 missense mutation (ACE2 c.1934 A>T); c1w82_isolate=C1W8.2 evolved multicellular strain; C1W81_isoalte C1W8.1 evolved multicellular strain; Y55_isolate=ancestral strain. §  Page 1:                                                                         Column 1: Particle ID                                                                         Column 2: Area ABD                                                                         Column 3: Aspect Ratio (Width/Length)                                                                         Column 4: Circle Fit                                                                         Column 5: Area base Diameter (ABD)                                                                         Column 6: Equivalent Spherical Diameter (ESD)                                                                         Column 7: Elongation                                                                         Column 8: Perimeter                                                                         Column 9: Roughness                         Column 10: Volume ABD-based                                                                         Column 11: Volume ESD-based                                                                         Column 12: Width                                                                         Column 13: Source. Name of the sample.   o   File 8 name: File_8_C1W8.2_overlapPairs_Selection_Experiment.csv o   File 8 description: C1W8.2 _lineage_selection.cycle= C1W8.2 evolved multicellular strain, lineage (A=ancestral, M1= lineage 1,  M2= lineage 2 , M3= lineage 3) and selection cycle  (0, 1, 2 and 3). §  Page 1: Column 1: Var1= strain 1 Column 2: Var2= strain 2 Column 3: overlap value of both strains compared.   o   File 9 name: File_9_C1W8.1_overlapPairs_Selection_Experiment.csv o   File 9 description: C1W8.1 _lineage_selection.cycle =C1W8.1 evolved multicellular strain, lineage (A=ancestral, M1= lineage 1,  M2= lineage 2 , M3= lineage 3) and selection cycle  (0, 1, 2 and 3). §  Page 1: Column 1: Var1= strain 1 Column 2: Var2= strain 2 Column 3: overlap value of both strains compared.   o   File 10 name: File_10_overlapPairs_Constructed_strains_diversity.xlsx o   File 10 description: variables naming convention; strain _isolate_colony.number. Strains; ace2x2m=strains containing the ACE2 missense mutation (ACE2 c.1934 A>T); ace2x2= ACE2 knockout. Isolate; 1,2 and 3. Colony.number; Initial=initial population and colony number (1,2,3,4 and 5). §  Page 1: Column 1: Var1= strain 1 Column 2: Var2= strain 2 Column 3: overlap value of both strains compared.   o   File 11 name: File_11_ ImageJ _analyses.xlsx o   File 11 description: ImageJ analyses of the microphotographs from Saccharomyces cerevisiae Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T). Cultures were grown in culture tubes with 10 mL of media, 50 mL Erlenmeyer flasks with 10 mL and 30 mL of media, in YPD under non-shaking and shaking at 250 rpm. YPD media was used across all conditions. Cultures were assessed after 24 hours growth at 30°C.  Microphotographs of each condition and strain were obtained with a Nikon TE2000 microscope using 10x objective. §  Page 1: Column 1: Var1= strain 1 Column 2: Var2 = strain 2 Column 3: overlap value of both strains compared.     o   File 12 name: File_12_ FlowCam_Pictures.zip o   File 12 description: FlowCam runs, images, and raw data of Saccharomyces cerevisiae Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD at 24h growth. Data for: Fig. 1B.   o   File 13 name: File_13_Microphotography_controled_experimental_conditions.zip o   File 13 description: 149 microphotographs. § Folder 1:  Images of Erlenmeyer flasks with 30ml of YPD § Folder 2:  Images of Erlenmeyer’s and tubes with 10ml of YPD   o   File 14 name: File_14_Mathematical_model.zip o   File 14 description: Mathematical model, R code, and generated values. §  Document 1:  R code of the model §  Document 2:  Resulted data from the mathematical model with different inset values of k, alpha, and beta. §  Document 2:  Resulted data from the mathematical model with different inset values of k, alpha, gamma, and beta.       o   File 15 name: File_15_rnaseq-final-results-Top_v_Bottom.xlsx o   File 15 description: §  Page 1: Column 1: number Column 2: ID Column 3: protID             Column 4: gene_symbol                     Column 5: chr             Column 6: chr_latin Column 7: location Column 8: baseMean             Column 9: log2FoldChange Column 10: lfcSE Column 11: stat             Column 12: pvalue     padj Column 13: test Column 14: log10padj             Column 15: log10baseMean Column 16: blast_pident Column 17: transcript_length             Column 18: blast_evalue Column 19: blast_bitscore Column 20: rnaID             Column 21: feature Column 22: accession Column 23: strain             Column 24: gene_accession   o   File 16 name: File_16_Variant_Call_format_file.vcf o   File 16 description: Variant Calling analyses of the  ARN sample Top 1. Adhesion number: SRR32105384.   o   File 17 name: Supp. Video 1. C1W8.1 from 17 to 22 hours growth o   File 17 description: Supplementary Video 1. Experimentally evolved multicellular yeast video between 17 and 22 hours of growth (C1W8.1-derived strain) — time-lapse video of the formation of a single-cell propagule from a multicellular cluster. The time-lapse video captures growth dynamics over this period, highlighting the formation of a single-cell propagule from a multicellular cluster on two occasions (visible in the lower left region of the frame). Images were acquired every 15 minutes using a 10x objective lens.  o   File 18 name: Supp. Video 2. Ace2x2KO over 26 hours of growth. o   File 18 description: Supplementary Video 2. ace2Δ knockout constructed strain growth — time-lapse video of a single large multicellular cluster over 26 hours. The video captures large, multicellular clusters that produce both large, multicellular and small, ancestral-like clusters. The video shows a single large multicellular cluster fragmenting into two large multicellular clusters at ~ 13 hours of growth (from 02:09 to 02:10 minutes in the time-lapse) and generating two small ancestral-like propagules at ~19 hours of growth (from 03:07 to 03:09 minutes in the time-lapse). Microphotographs were obtained at 3-minute intervals under a 10x objective over 26 hours.   o   File 19 name: Supp. Video 3. C1W8.1 over 6 hours growth o   File 19 description: Supplementary Video 3. Experimentally evolved multicellular yeast growth between 6 and 12 hours of growth (C1W8.1-derived strain). The time-lapse video captures large, multicellular clusters of the C1W8.1 strains, which produce both large, multicellular and small, ancestral-like clusters. Additionally, small ancestral-like clusters are observed undergoing cellular division —no separation is observed— during the first 2 to 3 hours, followed by a cessation of division for the remainder of the time-lapse. Images were acquired every 30 seconds using a 10x objective lens. o   File 20 name: Supp. Video 4. C1W8.1 over 24 hours growth o   File 20 description: Supplementary Video 4. Experimentally evolved multicellular yeast growth over 24 hours (C1W8.1-derived strain) — cell division stops in small ancestral-like phenotypes. The footage captures multiple large multicellular clusters undergoing fragmentation into propagules. Additionally, a small ancestral-like cluster is observed undergoing division during the first 2 to 3 hours, followed by a cessation of division for the remainder of the time-lapse (visible in the lower left region of the frame). This early division phase is evident during the first 10 seconds of the video. Images were acquired every 5 minutes using a 10x objective lens.  o   File 21 name: Supp. Video 5. Ace2x2KO over 24 hours growth o   File 21 description: Supplementary Video 5. ace2Δ knockout constructed strain growth — time-lapse video of multiple large multicellular clusters over 24 hours. The video shows multiple large multicellular clusters fragmenting into large clusters and several small ancestral-like clusters being dragged by Brownian motion and evaporation of the sample. Microphotographs were obtained at fixed intervals of 3 minutes under the 10x objective over 24 hours.  o   File 22 name: Supp. Video 6. Ace2x2missense from 0 to 3h45m hours growth o   File 22 description: Supplementary Video 6. ace2Δ missense constructed strain growth — time-lapse video of multiple large multicellular clusters up to 3 hours 45 min. The video shows multiple large multicellular clusters fragmenting into large clusters, generating two small ancestral-like propagules before being dragged by Brownian motion and evaporation of the sample. Microphotographs were obtained at 3-minute intervals under the 10x objective.

opencc-by-4.0Sep 2024View details →

dryad40/100

Data for: Environmental complexity mitigates the demographic impact of sexual selection

Sexual selection and the evolution of costly mating strategies can negatively impact population demography and adaptive potential. While laboratory studies have documented outcomes stemming from these processes, theory suggests that the demographic impact of sexual selection is contingent on the environment and therefore may have been overestimated in simple laboratory settings. Here we find support for this claim. We exposed copies of beetle lines, previously evolved with or without sexual selection, to a 10-generation heatwave while maintaining half of them in a simple environment and the other half in a complex environment. Populations with an evolutionary history of sexual selection maintained larger sizes and more stable growth rates in complex (relative to simple) environments, an effect not seen in populations that evolved without sexual selection. These results have implications for evolutionary forecasting and suggest that the demographic impact of sexual selection in natural populations might be lower than predicted.

opencc-zeroOct 2023View details →

zenodo40/100

skDER Representative Genomes for Select Bacterial Taxa

Genomes belonging to a single genus or order were gathered using a loose search of taxonomic classifications in GTDB R214. By loose we required the string 'g__{GENUSNAME}' to be found in taxonomic info column by GTDB, thus allowing gathering of associated genera (which GTDB suggests are different, but literature/domain experts have yet to rename).Genomes belonging to a taxa were dereplicated using skDER (v1.0.7) in "greedy" clustering mode with default values for parameters (99% ANI cutoff, 90% AF cutoff).Overview of Files:- The 'Genome_Dereplication_Overview.tsv' contains details of all the genomes considered as potential representatives for each taxonomic group and their GTDB R214 taxonomic classifications.- 18 _Clustering_Information.txt files which contains the relationship information of non-representative genomes to their nearest representative genome. Generated using the `-n` argument in skder v.1.0.7.  - 18 tar.gz compressed directories are provided. Each compressed directory features representative genomes in FASTA format determined for a particular taxon using skDER with greedy clustering and default cutoffs. Genome assemblies are renamed to feature both the GTDB taxonomic classification and the GCA identifier.         - Acinetobacter - 1,643 rep genomes (17.8% of 9,221 total genomes considered)         - Bacillales - 3,150 rep genomes (35.9% of 8,766 total genomes considered)         - Corynebacterium - 726 rep genomes (43.0% of 1,688 total genomes considered)         - Cutibacterium - 27 rep genomes (5.4% of 502 total genomes considered)         - Enterobacter - 878 rep genomes (19.9% of 4,408 total genomes considered)         - Enterococcus - 937 rep genomes (14.6% of 6,426 total genomes considered)         - Escherichia - 2,436 rep genomes (7.1% of 34,358 total genomes considered)         - Klebsiella - 1,022 rep genomes (5.6% of 18,145 total genomes considered)         - Lactobacillus - 541 rep genomes (30.9% of 1,747 total genomes considered)         - Listeria - 353 rep genomes (6.9% of 5,062 total genomes considered)         - Micromonospora - 211 rep genomes (73.3% of 288 total genomes considered)         - Mycobacterium - 744 rep genomes (6.9% of 10,657 total genomes considered)         - Neisseria - 414 rep genomes (12.8% of 3,235 total genomes considered)         - Pseudomonas - 2,666 rep genomes (18.9% of 14,066 total genomes considered)         - Salmonella - 308 rep genomes (2.2% of 14,109 total genomes considered)         - Staphylococcus - 496 rep genomes (2.5% of 19,627 total genomes considered)         - Streptococcus - 2,452 rep genomes (13.3% of 18,492 total genomes considered)         - Streptomyces - 1,555 rep genomes (57.7% of 2,697 total genomes considered)

opencc-by-4.0Oct 2023View details →

zenodo40/100

Supported PdZn nanoparticles for selective CO2 conversion, through the grafting of a heterobimetallic complex on CeZrOx

Supplementary material:  IR, PXRD, N2 adsorption, EDS, TEM, XPS, EXAFS, testing data

opencc-by-4.0Mar 2022View details →

dryad40/100

Efficient genomics based 'end-to-end' selective tree breeding framework

Since their initiation in the 1950s, worldwide selective tree breeding programs followed the recurrent selection scheme of repeated cycles of selection, breeding (mating), and testing phases and essentially remained unchanged to accelerate this process or address environmental contingences and concerns. Here, we introduce an "end-to-end" selective tree breeding framework that: 1) leverages strategically preselected GWAS-based sequence data capturing trait architecture information, 2) generates unprecedented resolution of genealogical relationships among tested individuals, and 3) leads to the elimination of the breeding phase through the utilization of readily available wind-pollinated (OP) families. Individuals' breeding values generated from multi-trait multi-site analysis were also used in an optimum contribution selection protocol to effectively manage genetic gain/co-ancestry trade-offs and traits' correlated response to selection. The proof-of-concept study involved a 40-year-old spruce OP testing population growing on three sites in British Columbia, Canada, clearly demonstrating our method's superiority in capturing most of the available genetic gains in a substantially reduced timeline relative to the traditional approach. The proposed framework is expected to increase the efficiency of existing selective breeding programs, accelerate the start of new programs for ecologically and environmentally important tree species, and address climate-change caused biotic and abiotic stress concerns more effectively.

opencc-zeroDec 2022View details →

dryad40/100

Continuously fluctuating selection reveals extreme granularity and parallelism of adaptive tracking

Temporally fluctuating environmental conditions are a ubiquitous feature of natural habitats. Yet, how finely natural populations adaptively track fluctuating selection pressures via shifts in standing genetic variation is unknown. We generated high-frequency, genome-wide allele frequency data from a genetically diverse population of Drosophila melanogaster in extensively replicated field mesocosms from late June to mid-December, a period of ~12 generations. Adaptation throughout the fundamental ecological phases of population expansion, peak density, and collapse was underpinned by extremely rapid, parallel changes in genomic variation across replicates. Yet, the dominant direction of selection fluctuated repeatedly, even within each of these ecological phases. Comparing patterns of allele frequency change to an independent dataset procured from the same experimental system demonstrated that the targets of selection are predictable across years. In concert, our results reveal fitness-relevance of standing variation that is likely to be masked by inference approaches based on static population sampling, or insufficiently resolved time-series data. We propose such fine-scaled temporally fluctuating selection may be an important force maintaining functional genetic variation in natural populations and an important stochastic force affecting levels of standing genetic variation genome-wide.

opencc-zeroNov 2023View details →

dryad40/100

Data from: Convergent rates of protein evolution identify novel targets of sexual selection in primates

Sexual selection is the differential reproductive success of individuals, resulting from competition for mates, mate choice, or success in fertilization. In primates, this selective pressure often leads to the development of exaggerated traits which play a role in sexual competition and successful reproduction. In order to gain insight into the mechanisms driving the development of sexually selected traits, we used an unbiased genome-wide approach across 21 primate species to correlate individual rates of protein evolution to relative testes size and sexual dimorphism in body size, two anatomical hallmarks of sexual selection in mammals. Among species with presumed high levels of sperm competition, we detected strong conservation of testes-specific proteins responsible for spermatogenesis and ciliary form and function. In contrast, we identified accelerated evolution of female reproductive proteins expressed in the vagina, cervix, and fallopian tubes in these same species. Additionally, we found accelerated protein evolution in lymphoid tissue, indicating that adaptive immune functions may also be influenced by sexual selection. This study demonstrates the distinct complexity of sexual selection in primates revealing contrasting patterns of protein evolution between male and female reproductive tissues.

opencc-zeroOct 2023View details →

dryad40/100

Phage selection drives resistance-virulence trade-offs in Ralstonia solanacearum plant pathogenic bacterium irrespective of the growth temperature

While temperature has been shown to affect the survival and growth of bacteria and their phage parasites, it is unclear if trade-offs between phage resistance and other bacterial traits depend on the temperature. Here, we experimentally compared the evolution of phage resistance-virulence trade-offs and underlying molecular mechanisms in phytopathogenic Ralstonia solanacearum bacterium at 25 °C and 35 °C temperature environments. We found that experimental growth conditions selected for small colony variants (SCVs) with increased growth rate and mutations in the quorum-sensing (QS) signalling receptor gene, phcS. Interestingly, SCVs were also phage-resistant and reached higher frequencies in the presence of phages in both temperature environments. Evolving phage resistance was costly in terms of reduced carrying capacity, biofilm formation and reduced virulence in planta possibly due to loss of QS-mediated expression of key virulence genes. We also observed mucoid phage-resistant colonies that showed loss of virulence and reduced twitching motility likely due to parallel mutations in prepilin peptidase gene pilD. Moreover, phage-resistant SCVs from 35 °C-phage treatment had parallel mutations in genes encoding type II secretion system (T2SS) genes (gspE and gspF), indicating that defects in pseudopilus made bacterium resistant to the phage. Additional transcriptomic analysis revealed upregulation of CBASS and type Ⅰ restriction-modification phage defence systems in response to phage exposure, which coincided with reduced expression of motility and virulence-associated genes, including pilD and type II and III secretion systems. Together, these results suggest that phage resistance-virulence trade-offs are not affected by the growth temperature but can be mediated through both pre- and post-infection phage resistance mechanisms.

opencc-zeroNov 2023View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record