Research Dataset Catalog and ShareScore Ratings | ScienceDex

zenodo40/100

AraHealth: A Dataset for Arabic Health-Related Advice Directed to the General Public on Twitter During the Early Spread of COVID-19 [Dataset]

Health-related advice directed to the general public on Twitter provides insight into the use of social media during health emergencies. This paper describes our data collection, sampling, and analysis of 24 million tweets in Arabic in March and early April 2020. We make reference to a parallel dataset and analysis of tweets in English during the same period. The contribution of this paper is a description of our dataset, our coding process to indiciate tweets with health related advice, and our analysis and comparisons of the characteristics of the tweets with and without health-related advice. These contributions provide the basis for future research on semi-automated classifiers for health-related advice and efforts to reduce the spread of harmful health advice.

opencc-by-4.0Jan 2021View details →

zenodo40/100

Supplementary material for "Investigating phoneme-dependencies of spherical voice directivity patterns"

The .pdf file contains <ul> <li>general information on the voice directivity files in the SOFA format</li> <li>information on the indices and names of the SOFA-files</li> </ul>   The .zip files contain <ul> <li>voice directivities in the SOFA format sampled on the sparse measuring grid</li> <li>voice directivities in the SOFA format upsampled to a dense grid</li> </ul>   The Matlab script provides <ul> <li>an example reading a dataset, performing spatial upsampling if required, and creating some basic plots. </li> </ul>

opencc-by-4.0Jun 2021View details →

zenodo40/100

ECOBREED Direct drilling of buckwheat

Direct drilling of buckwheat into the living mulch. Preliminary trials to test five differentmethods of drilling in order to find alternative ways of buckwheat establishment in arid environments.

opencc-by-4.0Jun 2021View details →

zenodo40/100

A Counterion-Directed Approach to the Diels-Alder Paradigm: Cascade Synthesis of Tricyclic Fused Cyclopropanes

An approach to the intramolecular Diels–Alder reaction has led to a cascade synthesis of complex carbocycles composed of three fused rings and up to five stereocenters with complete stereocontrol. Computational analysis reveals that the reaction proceeds by a Michael/Michael/cyclopropanation/epimerization cascade in which size and coordination of the counterion is key. This date set contains DFT optimised structures as described in the title paper.

openmit-licenseAug 2016View details →

zenodo40/100

FIGURE 3 in Sexual dimorphism in a freshwater atyid shrimp (Decapoda: Caridea) with direct development: a geometric morphometrics approach

FIGURE 3. Relative deformations grids illustrating the variation in the mean shape of the carapace for (a) females and (b) males.

opencc-zeroDec 2016View details →

zenodo40/100

FIGURE 2 in Sexual dimorphism in a freshwater atyid shrimp (Decapoda: Caridea) with direct development: a geometric morphometrics approach

FIGURE 2. Scatter plot of first versus second principal component axes for the total variation of the carapace shape for females, juvenile females and males of Neocaridina davidi.

opencc-zeroDec 2016View details →

zenodo40/100

Air pollution in a tropical city: the relationship between wind direction and lichen bio-indicators in San José, Costa Rica

Lichens are good bio-indicators of air pollution, but in most tropical countries there are few studies on the subject; however, in the city of San José, Costa Rica, the relationship between air pollution and lichens has been studied for decades. In this article we evaluate the hypothesis that air pollution is lower where the wind enters the urban area (Northeast) and higher where it exits San José (Southwest). We identified the urban parks with a minimum area of approximately 5 000m² and randomly selected a sample of 40 parks located along the passage of wind through the city. To measure lichen coverage, we applied a previously validated 10 x 20cm template with 50 random points to five trees per park (1.5m above ground, to the side with most lichens). Our results (years 2008 and 2009) fully agree with the generally accepted view that lichens reflect air pollution carried by circulating air masses. The practical implication is that the air enters the city relatively clean by the semi-rural and economically middle class area of Coronado, and leaves through the developed neighborhoods of Escazú and Santa Ana with a significant amount of pollutants. In the dry season, the live lichen coverage of this tropical city was lower than in the May to December rainy season, a pattern that contrasts with temperate habitats; but regardless of the season, pollution follows the pattern of wind movement through the city

opencc-by-4.0May 2011View details →

zenodo40/100

Air pollution in a tropical city: the relationship between wind direction and lichen bioindicators in San Jose, Costa Rica

Lichens are good bio-indicators of air pollution, but in most tropical countries there are few studies on the subject; however, in the city of San José, Costa Rica, the relationship between air pollution and lichens has been studied for decades. In this article we evaluate the hypothesis that air pollution is lower where the wind enters the urban area (Northeast) and higher where it exits San José (Southwest). We identified the urban parks with a minimum area of approximately 5 000m² and randomly selected a sample of 40 parks located along the passage of wind through the city. To measure lichen coverage, we applied a previously validated 10 x 20cm template with 50 random points to five trees per park (1.5m above ground, to the side with most lichens). Our results (years 2008 and 2009) fully agree with the generally accepted view that lichens reflect air pollution carried by circulating air masses. The practical implication is that the air enters the city relatively clean by the semi-rural and economically middle class area of Coronado, and leaves through the developed neighborhoods of Escazú and Santa Ana with a significant amount of pollutants. In the dry season, the live lichen coverage of this tropical city was lower than in the May to December rainy season, a pattern that contrasts with temperate habitats; but regardless of the season, pollution follows the pattern of wind movement through the city

opencc-by-4.0May 2011View details →

zenodo40/100

Air pollution in a tropical city: the relationship between wind direction and lichen bio-indicators in San José, Costa Rica

Lichens are good bio-indicators of air pollution, but in most tropical countries there are few studies on the subject; however, in the city of San José, Costa Rica, the relationship between air pollution and lichens has been studied for decades. In this article we evaluate the hypothesis that air pollution is lower where the wind enters the urban area (Northeast) and higher where it exits San José (Southwest). We identified the urban parks with a minimum area of approximately 5 000m² and randomly selected a sample of 40 parks located along the passage of wind through the city. To measure lichen coverage, we applied a previously validated 10 x 20cm template with 50 random points to five trees per park (1.5m above ground, to the side with most lichens). Our results (years 2008 and 2009) fully agree with the generally accepted view that lichens reflect air pollution carried by circulating air masses. The practical implication is that the air enters the city relatively clean by the semi-rural and economically middle class area of Coronado, and leaves through the developed neighborhoods of Escazú and Santa Ana with a significant amount of pollutants. In the dry season, the live lichen coverage of this tropical city was lower than in the May to December rainy season, a pattern that contrasts with temperate habitats; but regardless of the season, pollution follows the pattern of wind movement through the city

opencc-by-4.0May 2011View details →

zenodo40/100

Direct Visualization of Cryptographic Keys for Enhanced Security - Supplementary files

Supplementary files showing <ul> <li>Visualization of keys only with lines;</li> <li>Visualization of keys only using ellipses;</li> <li>Visualization of keys and hashes using ellipses;</li> <li>A synthetic example for key spoofing.</li> </ul> Title: Direct Visualization of Cryptographic Keys for Enhanced Security Author: Oleg Lobachev Affiliation: Visual Computing, University Bayreuth, Universitätsstr. 30, 95440 Bayreuth, Germany Email: oleg.lobachev@uni-bayreuth.de Web: https://orcid.org/0000-0002-7193-6258, https://www.researchgate.net/profile/Oleg_Lobachev Journal: The Visual Computer

opencc-by-4.0Jun 2017View details →

zenodo40/100

Going against the grain – Texture orientation affects direction of exploratory movement

In haptic perception sensory signals depend on how we actively move our hands. For textures with periodically repeating grooves, movement direction can determine temporal cues to spatial frequency. Moving in line with texture orientation does not generate temporal cues. In contrast, moving orthog-onally to texture orientation maximizes the temporal frequency of stimulation, and thus optimizes temporal cues. Participants performed a spatial frequency discrimination task between stimuli of two types. The first type showed the de-scribed relationship between movement direction and temporal cues, the second stimulus type did not. We expected that when temporal cues can be optimized by moving in a certain direction, movements will be adjusted to this direction. However, movement adjustments were assumed to be based on sensory infor-mation, which accumulates over the exploration process. We analyzed 3 indi-vidual segments of the exploration process. As expected, participants only ad-justed movement directions in the final exploration segment and only for the stimulus type, in which movement direction influenced temporal cues. We con-clude that sensory signals on the texture orientation are used online during ex-ploration in order to adjust subsequent movements. Once sufficient sensory evi-dence on the texture orientation was accumulated, movements were directed to optimize temporal cues. Lezkan, A. & Drewing, K. (2016). Going against the grain – Texture orientation affects direction of exploratory movement, part I. Haptics: Perception, Devices, Control, and Applications (pp. 430-440). The Zip file contains all data relative to the publication. A description of the variables is contained in the file VARIABLE_CODES.txt

opencc-by-4.0May 2017View details →

zenodo40/100

Direct chromosome-length haplotyping by single-cell sequencing.

Selected Strand-seq libraries from PMID:27646535 study. Data were originally shared on the European Nucleotide Archive (http://www.ebi.ac.uk/ena) under the accession number: PRJEB14185

opencc-by-4.0Nov 2016View 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 →

zenodo40/100

Computationally directed manipulation of cross-linked covalent organic frameworks for membrane applications - PCCP

The dataset uploaded herein is associated with the paper published under the title "Computationally directed manipulation of cross-linked covalent organic frameworks for membrane applications" with the Royal Society of Chemistry - Physical Chemistry Chemical Physics Journal. This dataset includes the .vasp files for all modeled structures, an example dftb_in.hsd file, which is the instructional file for geometry optimization with DFTB+, an Excel spreadsheet with atom number densities and total energy values for all modeled geometries, and, finally, a Python script that was used to calculate the Enthalpy of Formation and Cohesive Energies for all structures. This data has been made available to the scientific community in the interest of open-source and accessible data. The authors request that you please cite the associated paper and Zenodo dataset if used.AbstractTwo-dimensional covalent organic frameworks (2D-COFs) exhibit characteristics ideal for membrane applications, such as high stability, tunability and porosity along with well-ordered nanopores. However, one of the many challenges with fabricating these materials into membranes is that membrane wetting can result in layer swelling. This allows molecules that would be excluded based on pore size to flow around the layers of the COF, resulting reduced separation. Cross-linking between these layers inhibits swelling to improve the selectivity of these membranes. In this work, computational models were generated for a quinoxaline-based COF cross-linked with oxalyl chloride (OC) and hexafluoroglutaryl chloride (HFG). Enthalpy of formation and cohesive energy calculations from these models show that formation of these COFs is thermodynamically favorable and the resulting materials are stable. The cross-linked COF with HFG was synthesized and characterized with Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), thermogravimetric analysis with differential scanning calorimetry (TGA-DSC), and water contact angles. Additionally, these frameworks were fabricated into membranes for permeance testing. The experimental data supports the presence of cross-linking and demonstrates that varying the amount of HFG used in the reaction does not change the amount of cross-linking present. Computational models indicate that the effect of varying cross-linking concentration on the framework stability is negligible and less cross-linking still results in stable materials. This work sheds light on the nature of the cross-linking in these 2D-COFs and their application in membrane separations.

opencc-by-4.0Nov 2023View details →

zenodo40/100

Supporting information for "Illuminating the nanostructure of diffuse interfaces: Recent advances and future directions in reflectometry techniques"

This deposition contains the data and analysis (Jupyter notebooks) detailed in Illuminating the nanostructure of diffuse interfaces: Recent advances and future directions in reflectometry techniques. All Jupyter notebooks have also been converted into PDF files for ease of viewing. All data and code (notebooks) required to reproduce the analysis can be found within the "supporting_data_analysis.zip" archive. This archive contains two sub-directories:<ul><li>insituAnalysis<ul><li>Jupyter notebook outlining how to perform the `on-the-fly' analysis.</li><li>Data directory containing all temporally sliced neutron reflectometry data.</li></ul></li><li>MaxEnt<ul><li>Jupyter notebook outlining how to perform the maximum entropy modelling approach for polymer volume fraction profiles.</li><li>Data directory containing relevant neutron reflectometry data and PCHIP spline modelling by Gresham et al. (<a href="www.doi.org/10.1107/S160057672100251X">10.1107/S160057672100251X</a>).</li><li>Code available on the <a href="https://github.com/refnx/refnx-models/tree/master/MaxEntVFP">refnx-models GitHub repo</a>.</li></ul></li></ul>

opencc-by-4.0Nov 2023View details →

zenodo40/100

Vertical profiles of air temperature, relative humidity, wind speed and direction observed using UAV over the Mukhrino peatland in June 2022

Vertical profiles of air temperature and relative humidity were measured using the iMetXQ2 sensor onboard DJI Phantom 4 quad-copter; vertical profiles of wind speed and direction were obtained from the Phantom 4 flight logs as produced by the DJI proprietary algorithm.

opencc-by-4.0Nov 2023View details →

zenodo40/100

Data set for "Distributed and specific encoding of sensory, motor and decision information in the mouse neocortex during goal-directed behavior"

Data set for: Oryshchuk A, Sourmpis C, Weverbergh J, Asri R, Esmaeili V, Modirshanechi A, Gerstner W, Petersen CCH, Crochet S (2024) Distributed and specific encoding of sensory, motor and decision information in the mouse neocortex during goal-directed behavior. Cell Reports 43: 113618. https://doi.org/10.1016/j.celrep.2023.113618   There are 2 files in this upload:   1. The file named "2024_Oryshchuk_CellReports.pdf" is the Open Access pdf of the online publication in Cell Reports.   2. The file named " Oryshchuk _data_code.zip" (~1.8 GB) is a zipped version of a folder "Oryshchuk _data_code" (~2.3 GB), which contains the preprocessed data analyzed in the study along with the Matlab and Python codes used to generate the published figures. To access the data and codes, first unzip the file. ·       The subfolder “Atlas” contains templates from the Allen Mouse Brain Reference Altas of anatomical brain sections used to map the location of the silicon probes (Supplementary Figure S1). ·       The subfolder “Clustering-master” contains the Matlab codes used for the clustering on neuronal activity (Figure 1). The output is the data structure ‘Data_Clustering.mat’ file already provided in the folder ‘Data’. ·       The subfolder “Code” contains the main Matlab codes used to analyze the data and plot the figures. The ouput from the clustering and decoding analyses are provided in the ‘Data’ folder, thus the Matlab codes can be run independently, without running the ‘clustering’ or ‘decoding’ codes first. ·       The subfolder “Data” contains the Matlab  data structures containing the electrophysiological and behavioral data from whisker rewarded (‘DataWR.mat’) and non-rewarded (‘DataWnonR.mat’) mice, the behavioral data for optogenetic inactivation in rewarded mice, the clustering results (‘Data_Clustering.mat’) and a subfolder containing the results from the decoding analyses (“Decoding”). ·       The subfolder “decoding” contains the Python codes used for the decoding analyses. The required configuration can be found in the file ‘requirements.txt’. To run the codes, follow instructions from the ‘README.md’ file. ·       The subfolder “Figures” will be populated with figures saved in .png and .eps formats as well as a ‘Methods.txt’ files when running the main Matlab codes. ·       The subfolder “Functions” contains subfunctions used by the main Matlab codes to analyze the data and plot the figures. ·       The subfolder “Results” will be populated with Matlab data structures as well as a ‘.xlsx’ files when running the main Matlab codes. When running the code, you need to set the Matlab file path to be "Oryshchuk _data_code". In addition, you should add the folder "Oryshchuk_data_code" with subfolders to the Matlab path. Some parts of the code rely upon previous results, and need to be executed sequentially in the order of the figure panels in the journal publication. Please note that some of the code can take several hours to execute.

opencc-by-4.0Dec 2023View details →

zenodo40/100

Identifying Unexpected Neurotoxicity Drivers with Acetylcholinesterase Inhibition by Virtual Effect-Directed Analysis in Nationwide Estuarine Waters

Neurotoxicity is frequently observed in the global aquatic environment, threatening aquatic ecosystems and human health. However, a very limited proportion of neurotoxic effects (~1%) has been explained by known chemicals of concern. Here, we integrated machine learning, nontargeted analysis, and in vitro biotesting to identify neurotoxic drivers of acetylcholinesterase (AChE) inhibition in estuarine waters along the coastline of China. Machine learning was used as a virtual fractionation tool to reduce the complexity of chemical mixtures, thus guiding nontargeted screening of AChE inhibitors. Ultimately, sixty chemicals with diverse known and presently unknown structures were identified, explaining 82.1% of the observed AChE inhibition in estuarine water samples. Polyunsaturated fatty acids were unexpectedly found to be neurotoxic drivers, accounting for 80.5% of the overall effect. This proof-of-concept study demonstrates that our approach enables rapid and comprehensive screening of causative organic pollutants associated with various in vitro endpoints for large-scale monitoring of water quality.

opencc-by-4.0Feb 2024View details →

dryad40/100

Directional epistasis is common in morphological divergence

Epistasis is often portrayed as unimportant in evolution. While random patterns of epistasis may have limited effects on the response to selection, systematic directional epistasis can have substantial effects on evolutionary dynamics. Directional epistasis occurs when allelic substitutions that change a trait also modify the effects of allelic substitutions at other loci in a systematic direction. In this case, trait evolution may induce correlated changes in allelic effects and effective genetic variance (evolvability) that modify further evolution. Although theory thus suggests a potentially important role for directional epistasis in evolution, we still lack empirical evidence about its prevalence and magnitude. Using a new framework to estimate systematic patterns of epistasis from line-crosses experiments, we quantify its effects on 197 size-related traits from diverging natural populations in 24 animal and 17 plant species. We show that directional epistasis is common and tends to become stronger with increasing morphological divergence. In animals, most traits displayed negative directionality toward larger size, suggesting that epistasis constraints reducing evolvability toward larger size may be common. Dominance was also common and did not systematically alter the effects of epistasis.

opencc-zeroMar 2024View details →

dryad40/100

Data from: Direct quantification of ion composition and mobility in organic mixed ionic-electronic conductors

Ion transport in organic mixed ionic-electronic conductors (OMIECs) is crucial due to its direct impact on device response time and fundamental operating mechanisms but are often assessed indirectly or rely on extra assumptions. Operando X-ray fluorescence (XRF) is a powerful, direct probe useful for elemental characterization of bulk OMIECs, and was employed to directly quantify ion composition and mobility in a model OMIEC, PEDOT:PSS, during device operation. The first cycle revealed slow electrowetting and cation-proton exchange. Subsequent cycles showed rapid response with minor cation fluctuation (~5%). Comparison with optical-tracked electrochromic fronts revealed a mesoscale structure dependent proton transport. The calculated effective ion mobility demonstrated thickness-dependent behavior, emphasizing an interfacial ion transport pathway with a higher mobile ion density. The decoupling of bulk and interfacial effects on ion mobility, and the decoupling of cation and proton transport contributes to our understanding of ion transport in conventional and emerging OMIEC-based devices, and has broader implications for ion transport in other ionic conductors writ large.

opencc-zeroMar 2024View details →

Find research datasets worth reusing

Dataset results

AraHealth: A Dataset for Arabic Health-Related Advice Directed to the General Public on Twitter During the Early Spread of COVID-19 [Dataset]

Supplementary material for "Investigating phoneme-dependencies of spherical voice directivity patterns"

ECOBREED Direct drilling of buckwheat

A Counterion-Directed Approach to the Diels-Alder Paradigm: Cascade Synthesis of Tricyclic Fused Cyclopropanes

FIGURE 3 in Sexual dimorphism in a freshwater atyid shrimp (Decapoda: Caridea) with direct development: a geometric morphometrics approach

FIGURE 2 in Sexual dimorphism in a freshwater atyid shrimp (Decapoda: Caridea) with direct development: a geometric morphometrics approach

Air pollution in a tropical city: the relationship between wind direction and lichen bio-indicators in San José, Costa Rica

Air pollution in a tropical city: the relationship between wind direction and lichen bioindicators in San Jose, Costa Rica

Air pollution in a tropical city: the relationship between wind direction and lichen bio-indicators in San José, Costa Rica

Direct Visualization of Cryptographic Keys for Enhanced Security - Supplementary files

Going against the grain – Texture orientation affects direction of exploratory movement

Direct chromosome-length haplotyping by single-cell sequencing.

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

Computationally directed manipulation of cross-linked covalent organic frameworks for membrane applications - PCCP

Supporting information for "Illuminating the nanostructure of diffuse interfaces: Recent advances and future directions in reflectometry techniques"

Vertical profiles of air temperature, relative humidity, wind speed and direction observed using UAV over the Mukhrino peatland in June 2022

Data set for "Distributed and specific encoding of sensory, motor and decision information in the mouse neocortex during goal-directed behavior"

Identifying Unexpected Neurotoxicity Drivers with Acetylcholinesterase Inhibition by Virtual Effect-Directed Analysis in Nationwide Estuarine Waters

Directional epistasis is common in morphological divergence

Data from: Direct quantification of ion composition and mobility in organic mixed ionic-electronic conductors

Understand access before you commit

Allen Brain Atlas

Annotated Behaviour and Observability Dataset (ABODe)

DANDI Archive for NWB datasets

International Brain Laboratory public data

OpenNeuro