Find research datasets worth reusing

opencc-by-4.0Nov 2023View details →

USPTO Dataset for: Fast Chemical Reaction Condition Suggestion via Rule-Based Classification and Similarity Search

USPTO database that is analyzed with Rxn-INSIGHT (<a href="https://github.com/mrodobbe/Rxn-INSIGHT">https://github.com/mrodobbe/Rxn-INSIGHT</a>).This gzip file contains a very large Pandas DataFrame that can be loaded via pd.read_parquet('uspto_rxn_insight.gzip'). Because of the large size of the data, PyArrow version 13.0 must be used. To use parquet in Pandas, install PyArrow and fastparquet using pip:pip install pyarrow==13.0 pip install fastparquet

opencc-by-4.0Dec 2023View details →

Stability (similarity) values of output maps generated using different background point sampling strategies

These tables represent the model settings and similarity values of Species Distribution Models generated using different background pointssampling strategies. The similarity values are our own newly designed way to investigate model stability and were determined by comparing an output map of an SDM fitted using real data (original map) with an output map of an SDM fitted using virtual occurrences generated from the original map.

opencc-zeroJan 2024View details →

Data from: Same places, same stories? Genomics reveals similar structuring and demographic patterns for four Pocillopora coral species in the southwestern Indian Ocean

Aim Efficiently protecting species requires knowing their ecological, life history and reproductive traits. This is particularly decisive for scleractinian corals, key components of coral reefs, which are experiencing critical declines. Yet their connectivity remains insufficiently documented. Here, we focused on four distinct species of the coral genus Pocillopora found in diverse habitats of the southwestern Indian Ocean and presenting various reproductive strategies. We aimed to understand whether these traits affect species connectivity. Location Archipelagos and islands of the southwestern Indian Ocean. Taxon Pocillopora spp. Methods We used target-capture to collect single-nucleotide polymorphisms (SNPs) from over a thousand colonies sampled across nine localities. From the ca. 1,400 SNPs retained per species, Bayesian clustering methods, networks and demographic inferences were applied to first infer the population genetic structure and connectivity of each species, then the demographic history of each population. Results All four Pocillopora species exhibited almost the same genetic structuring pattern, reflecting the sampled ecoregions (Madagascar and surrounding islands vs. Mascarene Islands). However, the genetic differentiation was stronger ( FST about 10 times higher) for P. acuta, the species inhabiting more enclosed habitats, such as lagoons and shallow waters, and reproducing mainly asexually. Similarly, all populations, except those from P. acuta, showed a signature of population expansion ca. 100,000 years ago, following the penultimate glacial period. Main conclusions These results indicate reduced gene flow between Madagascar and the Mascarenes, probably linked to currents, suggesting distinct connectivity networks that should be considered independently when setting up conservation plans. In addition, shared demographic histories reflect that populations from these species have probably met the same environmental constraints and reacted similarly, something that should be considered in light of the ongoing rapid climate change.

opencc-zeroJan 2024View details →

Actuarial senescence progresses similarly across sites and species in four boreal orchids

Whole-plant senescence, defined as a decrease in individual fitness as an organism grows older, has often been assumed to not occur in plants; however, it has now been detected in a range of plant taxa. Still, reported senescence patterns vary substantially, and it remains unknown how consistent patterns are within phylogenetic groups and how they may be affected by environmental factors. Plants show a high diversity in life-history traits within phylogenetic groups and environments, but shared traits amongst related species are also common, making both diverse and similar patterns probable. Here, we explore how mortality changes with advancing age in four closely related species (Dactylorhiza incarnata, D. lapponica, D. maculata, and Gymnadenia conopsea) across two sites in Norway: the coastal Nordmarka and inland Sølendet. Using data collected over 34 years, following more than 2500 individual plants, we conduct Bayesian survival trajectory analysis to assess mortality age-trajectories. A simple Weibull model, illustrating increasing mortality at a decelerating rate with age, was the best fit for all species at both sites. From these models, we calculate rates of senescence and compare them using Kullback-Leibler divergences, finding no notable differences in rates between species or sites. Synthesis. Our findings suggest that actuarial senescence, an increase in mortality with advancing age, may be common in orchids and show that demographic ageing can proceed similarly in closely related taxa across different environments.

opencc-zeroJan 2024View details →

The Dynamic Assimilation Technique measures photosynthetic CO2 response curves with similar fidelity as steady-state approaches in half the time

The net CO2 assimilation (A) response to intercellular CO2 concentration (Ci) is a fundamental measurement in photosynthesis and plant physiology research. The conventional A/Ci protocols rely on steady-state measurements and take 15-40 minute per measurement, limiting data resolution or biological replication. Additionally, there are several CO2 protocols employed across the literature, without clear consensus as to the optimal protocol or systematic biases in their estimations. We compared the non-steady state Dynamic Assimilation Technique (DAT) protocol and the three most used CO2 protocols in steady-state measurements, and tested whether different CO2 protocols lead to systematic differences in estimations of the biochemical limitations to photosynthesis. The DAT protocol reduced the measurement time by almost half without compromising estimations accuracy or precision. The monotonic protocol was the fastest steady-state method. Estimations of biochemical limitations to photosynthesis were very consistent across all CO2 protocols, with slight differences in ribulose 1·5- bisphosphate carboxylase/oxygenase carboxylation limitation. The A/Ci curves were not affected by the direction of the change of CO2 concentration but rather the time spent under TPU-limited conditions. Our results suggest that maximum rate of ribulose 1·5- bisphosphate carboxylase/oxygenase carboxylation (Vcmax), linear electron flow for NADPH supply (J) and triose phosphate utilization (TPU) measured using different protocols within the literature are comparable, or at least not systematically different based on the measurement protocol used.

opencc-by-4.0Mar 2024View details →

NSRC-Search: Efficient searching for similar protein sequences of non-standard amino acid composition

This research was funded by the National Science Centre in Poland (grant number 2021/41/N/ST6/01919)

opencc-zeroMar 2024View details →

Data and code from: Similar trait-based successional assembly in native and introduced plants despite species pool differences

What drives the composition of invaded communities and the local abundance of introduced species are key questions in ecology. Community-assembly theory provides a useful framework for addressing these questions. Specifically, the environmental filtering model of community assembly predicts that a species' presence and abundance in a community depends on the interaction between its functional traits and the local environmental filters. However, for introduced species, larger-scale dispersal and introduction-related filters may restrict their regional trait pool. Here we tested this framework using long-term data from 50+ years of old-field vegetation succession. We asked whether native and introduced plant assemblages followed the same trait-based assembly rules. We also asked whether local functional dissimilarities between the two can be explained by regional species pool differences, a possibility that has rarely been addressed. We found strong similarities in the assembly processes of native and introduced plants. Average height and seed mass of both groups increased over time, consistent with previous studies of old-field succession. Moreover, the two showed similar trait-abundance relationships. While there were also some differences, particularly in their trait-incidence relationships, these differences appeared to be minor.Further, we identified species pool constraints on introduced species, and found that the exotic species pool was biased towards early successional traits. Lastly, we found that highly invasive exotic species were also likely to deviate from the expected trait-abundance relationship, suggesting a link between the two. These results suggest that introduced species generally follow the same assembly rules as native species. They also indicate that species pool differences can result in local functional composition differences, even when the two groups follow the same assembly rules. Moreover, there may be a link between species invasiveness and deviation from assembly rules, which, if further confirmed, provides a potential method of identifying strong invaders.

The Tree of Life eDNA metabarcoding reveals a similar taxonomic richness but dissimilar evolutionary lineages between seaports and marine reserves (metazoa data)

This dataset is associated to the following publication: Macé, B., Mouillot, D., Dalongeville, A., Bruno, M., Deter, J., Varenne, A., Gudefin, A., Boissery, P., & Manel, S. (2024). The Tree of Life eDNA metabarcoding reveals a similar taxonomic richness but dissimilar evolutionary lineages between seaports and marine reserves. Molecular Ecology, e17373. <a href="https://doi.org/10.1111/mec.17373">https://doi.org/10.1111/mec.17373</a> It contains the data obtained with the metazoa marker: <ul> <li>fastq files are the raw NGS eDNA sequencing outputs</li> <li>dat file records the adapters names and oligos used for sequencing</li> </ul> Metadata associated to each eDNA sample are also provided.   Methods <blockquote> eDNA extractions were performed in a BSL-2 lab dedicated for eDNA samples following the protocol described in Polanco Fernández et al. (2021). Four PCR amplifications were conducted with different assays covering the whole tree of life. The teleo primer pair (Valentini et al., 2016) targets a 12S mitochondrial DNA marker from teleosts and elasmobranchs; the metazoa primer pair (Kelly et al., 2016) targets a 16S mitochondrial DNA marker from metazoans; the euka2 primer pair (Guardiola et al., 2015) targets a marker from eukaryotes located on the V7 region of the 18S ribosomal RNA; and the bact2 primer pair (Taberlet et al., 2018) targets a marker from prokaryotes located on the V4 region of the 16S ribosomal RNA. The idea of this experimental design is to give a holistic overview of communities, with a nested hierarchy euka2-metazoa-teleo to obtain a finer taxonomic resolution over animal communities, and particularly fish. Twelve PCR replicates per sample were run, with negative extractions and PCR positive and negative controls analyzed in parallel. Unique tags were used for each PCR replicate amplified with the teleo primers only, allowing to differentiate them in the bioinformatic analysis (see after). NGS library preparation and MiSeq paired-end sequencing (2 × 150 bp) were performed at DNA Gensee (Le Bourget-du-Lac, France). </blockquote>   References Guardiola, M., Uriz, M. J., Taberlet, P., Coissac, E., Wangensteen, O. S., & Turon, X. (2015). Deep-Sea, Deep-Sequencing: Metabarcoding Extracellular DNA from Sediments of Marine Canyons. PLOS ONE, 10(10), e0139633. https://doi.org/10.1371/journal.pone.0139633 Kelly, R. P., O’Donnell, J. L., Lowell, N. C., Shelton, A. O., Samhouri, J. F., Hennessey, S. M., Feist, B. E., & Williams, G. D. (2016). Genetic signatures of ecological diversity along an urbanization gradient. PeerJ, 4, e2444. https://doi.org/10.7717/peerj.2444 Polanco Fernández, A., Marques, V., Fopp, F., Juhel, J.-B., Borrero-Pérez, G. H., Cheutin, M.-C., Dejean, T., González Corredor, J. D., Acosta-Chaparro, A., Hocdé, R., Eme, D., Maire, E., Spescha, M., Valentini, A., Manel, S., Mouillot, D., Albouy, C., & Pellissier, L. (2021). Comparing environmental DNA metabarcoding and underwater visual census to monitor tropical reef fishes. Environmental DNA, 3(1), 142–156. https://doi.org/10.1002/edn3.140 Taberlet, P., Bonin, A., Zinger, L., & Coissac, E. (2018). Environmental DNA: For Biodiversity Research and Monitoring. Oxford University Press. Valentini, A., Taberlet, P., Miaud, C., Civade, R., Herder, J., Thomsen, P. F., Bellemain, E., Besnard, A., Coissac, E., Boyer, F., Gaboriaud, C., Jean, P., Poulet, N., Roset, N., Copp, G. H., Geniez, P., Pont, D., Argillier, C., Baudoin, J.-M., … Dejean, T. (2016). Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Molecular Ecology, 25(4), 929–942. https://doi.org/10.1111/mec.13428

The Tree of Life eDNA metabarcoding reveals a similar taxonomic richness but dissimilar evolutionary lineages between seaports and marine reserves (bact2 data)

This dataset is associated to the following publication: Macé, B., Mouillot, D., Dalongeville, A., Bruno, M., Deter, J., Varenne, A., Gudefin, A., Boissery, P., & Manel, S. (2024). The Tree of Life eDNA metabarcoding reveals a similar taxonomic richness but dissimilar evolutionary lineages between seaports and marine reserves. Molecular Ecology, e17373. <a href="https://doi.org/10.1111/mec.17373">https://doi.org/10.1111/mec.17373</a> It contains the data obtained with the bact2 marker: <ul> <li>fastq files are the raw NGS eDNA sequencing outputs</li> <li>dat file records the adapters names and oligos used for sequencing</li> </ul> Metadata associated to each eDNA sample are also provided.   Methods <blockquote> eDNA extractions were performed in a BSL-2 lab dedicated for eDNA samples following the protocol described in Polanco Fernández et al. (2021). Four PCR amplifications were conducted with different assays covering the whole tree of life. The teleo primer pair (Valentini et al., 2016) targets a 12S mitochondrial DNA marker from teleosts and elasmobranchs; the metazoa primer pair (Kelly et al., 2016) targets a 16S mitochondrial DNA marker from metazoans; the euka2 primer pair (Guardiola et al., 2015) targets a marker from eukaryotes located on the V7 region of the 18S ribosomal RNA; and the bact2 primer pair (Taberlet et al., 2018) targets a marker from prokaryotes located on the V4 region of the 16S ribosomal RNA. The idea of this experimental design is to give a holistic overview of communities, with a nested hierarchy euka2-metazoa-teleo to obtain a finer taxonomic resolution over animal communities, and particularly fish. Twelve PCR replicates per sample were run, with negative extractions and PCR positive and negative controls analyzed in parallel. Unique tags were used for each PCR replicate amplified with the teleo primers only, allowing to differentiate them in the bioinformatic analysis (see after). NGS library preparation and MiSeq paired-end sequencing (2 × 150 bp) were performed at DNA Gensee (Le Bourget-du-Lac, France). </blockquote>   References Guardiola, M., Uriz, M. J., Taberlet, P., Coissac, E., Wangensteen, O. S., & Turon, X. (2015). Deep-Sea, Deep-Sequencing: Metabarcoding Extracellular DNA from Sediments of Marine Canyons. PLOS ONE, 10(10), e0139633. https://doi.org/10.1371/journal.pone.0139633 Kelly, R. P., O’Donnell, J. L., Lowell, N. C., Shelton, A. O., Samhouri, J. F., Hennessey, S. M., Feist, B. E., & Williams, G. D. (2016). Genetic signatures of ecological diversity along an urbanization gradient. PeerJ, 4, e2444. https://doi.org/10.7717/peerj.2444 Polanco Fernández, A., Marques, V., Fopp, F., Juhel, J.-B., Borrero-Pérez, G. H., Cheutin, M.-C., Dejean, T., González Corredor, J. D., Acosta-Chaparro, A., Hocdé, R., Eme, D., Maire, E., Spescha, M., Valentini, A., Manel, S., Mouillot, D., Albouy, C., & Pellissier, L. (2021). Comparing environmental DNA metabarcoding and underwater visual census to monitor tropical reef fishes. Environmental DNA, 3(1), 142–156. https://doi.org/10.1002/edn3.140 Taberlet, P., Bonin, A., Zinger, L., & Coissac, E. (2018). Environmental DNA: For Biodiversity Research and Monitoring. Oxford University Press. Valentini, A., Taberlet, P., Miaud, C., Civade, R., Herder, J., Thomsen, P. F., Bellemain, E., Besnard, A., Coissac, E., Boyer, F., Gaboriaud, C., Jean, P., Poulet, N., Roset, N., Copp, G. H., Geniez, P., Pont, D., Argillier, C., Baudoin, J.-M., … Dejean, T. (2016). Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Molecular Ecology, 25(4), 929–942. https://doi.org/10.1111/mec.13428

The Tree of Life eDNA metabarcoding reveals a similar taxonomic richness but dissimilar evolutionary lineages between seaports and marine reserves (euka2 data)

This dataset is associated to the following publication: Macé, B., Mouillot, D., Dalongeville, A., Bruno, M., Deter, J., Varenne, A., Gudefin, A., Boissery, P., & Manel, S. (2024). The Tree of Life eDNA metabarcoding reveals a similar taxonomic richness but dissimilar evolutionary lineages between seaports and marine reserves. Molecular Ecology, e17373. <a href="https://doi.org/10.1111/mec.17373">https://doi.org/10.1111/mec.17373</a> It contains the data obtained with the euka2 marker: <ul> <li>fastq files are the raw NGS eDNA sequencing outputs</li> <li>dat file records the adapters names and oligos used for sequencing</li> </ul> Metadata associated to each eDNA sample are also provided.   Methods <blockquote> eDNA extractions were performed in a BSL-2 lab dedicated for eDNA samples following the protocol described in Polanco Fernández et al. (2021). Four PCR amplifications were conducted with different assays covering the whole tree of life. The teleo primer pair (Valentini et al., 2016) targets a 12S mitochondrial DNA marker from teleosts and elasmobranchs; the metazoa primer pair (Kelly et al., 2016) targets a 16S mitochondrial DNA marker from metazoans; the euka2 primer pair (Guardiola et al., 2015) targets a marker from eukaryotes located on the V7 region of the 18S ribosomal RNA; and the bact2 primer pair (Taberlet et al., 2018) targets a marker from prokaryotes located on the V4 region of the 16S ribosomal RNA. The idea of this experimental design is to give a holistic overview of communities, with a nested hierarchy euka2-metazoa-teleo to obtain a finer taxonomic resolution over animal communities, and particularly fish. Twelve PCR replicates per sample were run, with negative extractions and PCR positive and negative controls analyzed in parallel. Unique tags were used for each PCR replicate amplified with the teleo primers only, allowing to differentiate them in the bioinformatic analysis (see after). NGS library preparation and MiSeq paired-end sequencing (2 × 150 bp) were performed at DNA Gensee (Le Bourget-du-Lac, France). </blockquote>   References Guardiola, M., Uriz, M. J., Taberlet, P., Coissac, E., Wangensteen, O. S., & Turon, X. (2015). Deep-Sea, Deep-Sequencing: Metabarcoding Extracellular DNA from Sediments of Marine Canyons. PLOS ONE, 10(10), e0139633. https://doi.org/10.1371/journal.pone.0139633 Kelly, R. P., O’Donnell, J. L., Lowell, N. C., Shelton, A. O., Samhouri, J. F., Hennessey, S. M., Feist, B. E., & Williams, G. D. (2016). Genetic signatures of ecological diversity along an urbanization gradient. PeerJ, 4, e2444. https://doi.org/10.7717/peerj.2444 Polanco Fernández, A., Marques, V., Fopp, F., Juhel, J.-B., Borrero-Pérez, G. H., Cheutin, M.-C., Dejean, T., González Corredor, J. D., Acosta-Chaparro, A., Hocdé, R., Eme, D., Maire, E., Spescha, M., Valentini, A., Manel, S., Mouillot, D., Albouy, C., & Pellissier, L. (2021). Comparing environmental DNA metabarcoding and underwater visual census to monitor tropical reef fishes. Environmental DNA, 3(1), 142–156. https://doi.org/10.1002/edn3.140 Taberlet, P., Bonin, A., Zinger, L., & Coissac, E. (2018). Environmental DNA: For Biodiversity Research and Monitoring. Oxford University Press. Valentini, A., Taberlet, P., Miaud, C., Civade, R., Herder, J., Thomsen, P. F., Bellemain, E., Besnard, A., Coissac, E., Boyer, F., Gaboriaud, C., Jean, P., Poulet, N., Roset, N., Copp, G. H., Geniez, P., Pont, D., Argillier, C., Baudoin, J.-M., … Dejean, T. (2016). Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Molecular Ecology, 25(4), 929–942. https://doi.org/10.1111/mec.13428

The Tree of Life eDNA metabarcoding reveals a similar taxonomic richness but dissimilar evolutionary lineages between seaports and marine reserves (teleo data)

This dataset is associated to the following publication: Macé, B., Mouillot, D., Dalongeville, A., Bruno, M., Deter, J., Varenne, A., Gudefin, A., Boissery, P., & Manel, S. (2024). The Tree of Life eDNA metabarcoding reveals a similar taxonomic richness but dissimilar evolutionary lineages between seaports and marine reserves. Molecular Ecology, e17373. <a href="https://doi.org/10.1111/mec.17373">https://doi.org/10.1111/mec.17373</a> It contains the data obtained with the teleo marker: <ul> <li>fastq files are the raw NGS eDNA sequencing outputs</li> <li>dat file records the adapters names and oligos used for sequencing</li> </ul> Metadata associated to each eDNA sample are also provided.   Methods <blockquote> eDNA extractions were performed in a BSL-2 lab dedicated for eDNA samples following the protocol described in Polanco Fernández et al. (2021). Four PCR amplifications were conducted with different assays covering the whole tree of life. The teleo primer pair (Valentini et al., 2016) targets a 12S mitochondrial DNA marker from teleosts and elasmobranchs; the metazoa primer pair (Kelly et al., 2016) targets a 16S mitochondrial DNA marker from metazoans; the euka2 primer pair (Guardiola et al., 2015) targets a marker from eukaryotes located on the V7 region of the 18S ribosomal RNA; and the bact2 primer pair (Taberlet et al., 2018) targets a marker from prokaryotes located on the V4 region of the 16S ribosomal RNA. The idea of this experimental design is to give a holistic overview of communities, with a nested hierarchy euka2-metazoa-teleo to obtain a finer taxonomic resolution over animal communities, and particularly fish. Twelve PCR replicates per sample were run, with negative extractions and PCR positive and negative controls analyzed in parallel. Unique tags were used for each PCR replicate amplified with the teleo primers only, allowing to differentiate them in the bioinformatic analysis (see after). NGS library preparation and MiSeq paired-end sequencing (2 × 150 bp) were performed at DNA Gensee (Le Bourget-du-Lac, France). </blockquote>   References Guardiola, M., Uriz, M. J., Taberlet, P., Coissac, E., Wangensteen, O. S., & Turon, X. (2015). Deep-Sea, Deep-Sequencing: Metabarcoding Extracellular DNA from Sediments of Marine Canyons. PLOS ONE, 10(10), e0139633. https://doi.org/10.1371/journal.pone.0139633 Kelly, R. P., O’Donnell, J. L., Lowell, N. C., Shelton, A. O., Samhouri, J. F., Hennessey, S. M., Feist, B. E., & Williams, G. D. (2016). Genetic signatures of ecological diversity along an urbanization gradient. PeerJ, 4, e2444. https://doi.org/10.7717/peerj.2444 Polanco Fernández, A., Marques, V., Fopp, F., Juhel, J.-B., Borrero-Pérez, G. H., Cheutin, M.-C., Dejean, T., González Corredor, J. D., Acosta-Chaparro, A., Hocdé, R., Eme, D., Maire, E., Spescha, M., Valentini, A., Manel, S., Mouillot, D., Albouy, C., & Pellissier, L. (2021). Comparing environmental DNA metabarcoding and underwater visual census to monitor tropical reef fishes. Environmental DNA, 3(1), 142–156. https://doi.org/10.1002/edn3.140 Taberlet, P., Bonin, A., Zinger, L., & Coissac, E. (2018). Environmental DNA: For Biodiversity Research and Monitoring. Oxford University Press. Valentini, A., Taberlet, P., Miaud, C., Civade, R., Herder, J., Thomsen, P. F., Bellemain, E., Besnard, A., Coissac, E., Boyer, F., Gaboriaud, C., Jean, P., Poulet, N., Roset, N., Copp, G. H., Geniez, P., Pont, D., Argillier, C., Baudoin, J.-M., … Dejean, T. (2016). Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Molecular Ecology, 25(4), 929–942. https://doi.org/10.1111/mec.13428

opencc-by-4.0Dec 2023View details →

Similar looking sisters: A new sibling species in the Pristimantis danae group from the southwestern Amazon basin (Anura, Strabomantidae)

Supplementary data  Köhler et al. (2024): Similar looking sisters: A new sibling species in the Pristimantis danae group from the southwestern Amazon basin (Anura, Strabomantidae). Zoosystematics and Evolution 100 (2): 565-582. Recordings of anuran advertisement calls: Pristimantis asimus: recorded 29 November 2008 (18:15 h) by Frank Glaw, air temperature not recorded. Locality: Peru: Departamento Huánuco: Provincia Puerto Inca, ACP Panguana, 9.6166°S, 74.9333°W. Call voucher: MUSM 29028 Pristimantis reichlei: recorded 18 December 1998 by Jörn Köhler, air temperature 16.7 °C. Locality: Bolivia: Departamento Cochabamba: Provincia Chapare: "old Chapare road", 17°07'S, 65°34'W. Recording band-pass filtered at 900-3600 Hz.

opencc-by-nc-4.0Nov 2024View details →

Dynamic cortical behavior of plant protoplasts reveals unexpected similarities between plant and animal cells

The raw data presented in this folder corresponds to the publication # Dynamic cortical behavior of plant protoplasts reveals unexpected similarities between plant and animal cells Johanna E. M. Dickmann 1,2, Marjolaine Martin 1,§, Claire Lionnet 1,§, Zoe Nemec-Venza 1, Olivier Hamant 1,2 1 Laboratoire Reproduction et Développement des Plantes, ENS de Lyon, INRAE, CNRS, UCBL1   2 Correspondence: olivier.hamant@ens-lyon.fr, johanna.dickmann@ens-lyon.fr   § Equal contribution   ORCIDs: * Johanna Dickmann: 0000-0002-0861-4440 * Zoe Nemec-Venza: 0000-0002-2346-2596 * Olivier Hamant: 0000-0001-6906-6620 Submitted to bioRxiv in November 2024   This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project number 521501033 to J.D. and the European Research Council (ERC-2021-AdG-101019515 “Musix” to O.H.). ## Data organization The data are organized according to the figure panels in the publication. For large experiments, the folders may contain subfolders for each experimental repeat and/or condition. For explanations on the data and the methods, please refer to the publication. All data presented here are the original raw data output of the microscopes in the CZI format, a proprietary format developed by Zeiss, encapsulating both 4D image data and metadata, i.e. acquisition settings. This format is supported by open-source software such as Fiji and Open Microscopy Environment. Refer to the README files in the subfolders for information on which exact file was used to display in the figure. ## Explanation of the file names ### Arabidopsis experiments The filenames contain the following information, separated by underscores: * an experiment identifier (e.g.”PLA001”, “PRO077”) * the line of the imaged plant material (e.g. “pUBQ10-LTi6B-TdTomato”) * sometimes information on the ecotype of the line (e.g. “Col-0”) * the age of the plants (e.g. “7d” = 7 day old plants) * sometimes information on a stain added (e.g. “FM4-64_0_5ugPml” = FM4-64 dye at a final concentration of 5 ug/ml) * sometimes information on a treatment (e.g. “beforeFDA” = image taken before FDA was added; “FDA2.5ugPml” = after adding FDA at a final concentration of 2.5 ug/ml, sometimes with additional information on the time between adding the FDA and imaging, e.g. “25min”) * sometimes information on the centrifugation speed (e.g. “100g”) * sometimes information on the imaging support (e.g. “bucket” = NOA73 container, “wells” = NOA73 microwells, “coverslip”) * sometimes information on the imaging mode (“z-stack”, “t-series” = time series/timelapse, “6x” = zoom of 6 in Zen software) * the solution the sample was imaged in (“Solution A” or “A” = hyperosmotic buffer with D-mannitol, “AS” = hyperosmotic buffer with D-sorbitol) * sometimes information on experimental setup (“ON” = overnight timelapse imaging) * increasing numbers at the end of the file name indicate subsequent fields of view or positions imaged with the same settings * for Fig. 1A: information about the length of the plasmolysis (“50 min”) * for Fig. 4c,d: information on which solution the protoplasts are and have been imaged in: “A” = hyperosmotic buffer solution with 600 mM D-mannitol. “B” =  hyperosmotic buffer solution with 280 mM D-mannitol. Times indicate time between addition of new buffer and onset of imaging of the position list. * for Fig. 4e-f: the concentration of the hyperosmotic buffer solution is indicated. For the control, the number of additions of hyperosmotic buffer solution with 600 mM D-mannitol is indicated. ### Physcomitrium patens experiments The filenames contain the following information, separated by underscores: * an experiment identifier (e.g. "PyP001")   * “Physco_wt” referring to Physcomitrium patens wild type * the age of the moss tissue used for protoplasting (e.g. “6d” = 6 days) * information on the stain added (e.g. “ Fm4-64_2ugPml” = FM4-64 dye at a final concentration of 2 ug/ml) * information on the imaging support (e.g. “bucket” = NOA73 container, “coverslip”) ### Maize experiments The filenames contain the following information, separated by underscores: * the date on which the experiment was performed (yyyymmdd) * the plant species (“Maize”) * sometimes information in the solution used for digestion ("A+E" = hyperosmotic buffer solution with D-mannitol) ### Bead experiments The filenames contain the following information, separated by underscores: * an experiment identifier (e.g. “beads008”) * a description of the beads (“fluoresbrite1micron” = Fluoresbrite beads of a diameter of 1 um) * sometimes a short description of the protocol (e.g. “SolAwashed-2-3mLsolA” = NOA73 microwells were washed in hyperosmotic buffer with 600 mM D-mannitol 2x prior to imaging, beads were imaged in 3 ml hyperosmotic buffer solution with 600 mM D-mannitol.) * an information on the size of the field of view (e.g. “small FOV” = small field of view, i.e. one microwell with beads) * information at which approx. height of the microwell the image was taken (“TopOfWells” = close to the opening of the wells on the top) * sometimes information on the zoom of the Zen software (e.g. “7x”)

opencc-by-4.0Sep 2024View details →

SimRE: A Requirements Similarity Tool for Software Product Lines - Dataset

The RequirementsPairs file consists of 34 pairs of software requirements related to GIS (Geographic Information Systems), each paired with a corresponding similarity degree. It was introduced in the paper "SimRE: A Requirements Similarity Tool for Software Product Lines." GISv1 and GISv2 refer to version 1 and version 2 of the GIS dataset, respectively. These datasets are composed of 173 requirements that represent the functionality of a web-based GIS product line.

opencc-by-4.0Apr 2023View details →

Challenges and limitations of applying the flux variance similarity (FVS) method to partition evapotranspiration in a montane cloud forest

Dataset <table> <tbody> <tr> <td>Name</td> <td>Description</td> </tr> <tr> <td>FVS_ori.zip</td> <td>the output from FVS method</td> </tr> <tr> <td>ModFVS.zip</td> <td>the output from ModFVS method</td> </tr> <tr> <td>CLM.zip</td> <td>the output from CLM </td> </tr> <tr> <td>Chilan_30min_sap_velocity_20200601_20211120_QC.csv</td> <td>the sap flow data in Chi-Lan</td> </tr> <tr> <td>*_clim.csv</td> <td>the observation data in Chi-Lan and Lien-Hua-Chih</td> </tr> </tbody> </table>   Codes for Analysis <table> <tbody> <tr> <td>Name</td> <td>Description</td> </tr> <tr> <td>*.ipynb</td> <td>the python code used for analyzing output</td> </tr> <tr> <td>*_FVS_process.py</td> <td>the python code used for process ModFVS method</td> </tr> </tbody> </table>   ModFVS method (fluxpart-0.2.10+rhtest-py3-none-any.whl) <ul> <li>use "pip install fluxpart-0.2.10+rhtest-py3-none-any.whl" to install the package</li> <li> To specify a maximum allowable relative humidity when calculating WUE, set a value for "max_rh" in "wue_options". For example, to set the max RH to 95%, you would change your example code to this: wue_options = {"meas_ht": 23.7,"canopy_ht":10, "ppath": "C3","ci_mod":ci_mod, "max_rh":95} </li> <li> Note that this code is a fork of (https://github.com/usda-ars-ussl/fluxpart) </li> </ul>

opencc-zeroNov 2021View details →

Populations restored using regional seed are genetically diverse and similar to natural populations in the region

Ecological restoration and plant re-introductions aim to create plant populations that are genetically similar to natural populations to preserve the regional gene pool, yet genetically diverse to allow adaptation to a changing environment. For this purpose, seeds for restoration are increasingly sourced from multiple populations in the target region. However, it has only rarely been tested whether using regional seed indeed leads to genetically diverse restored populations which are genetically similar to natural populations. We used single nucleotide polymorphism (SNP) markers to investigate genetic diversity within and differentiation among populations of Centaurea jacea and Betonica officinalis on restored and natural meadows in the White Carpathians, Czech Republic. The restoration took place 20 years ago using regional seeds propagated from a mix of multiple regional source populations. We included original regional seeds in our analysis to compare the restored populations with their origin (only in C. jacea). Additionally, we analysed conventional seeds without certified origin because these would have constituted a common alternative for restoration seeding in the absence of regional seeds. The differentiation between restored and natural populations (mean pairwise FST = 0.018 in Centaurea and 0.021 in Betonica) was similar to the differentiation among natural populations (FST = 0.023 and 0.021), and the restored populations were slightly more genetically diverse than the natural populations. In addition, restored populations were relatively similar to their origin, the regional seeds (FST = 0.015). In contrast, conventional seeds were strongly differentiated from all regional populations (FST = 0.100 and 0.059, in Centaurea and Betonica, respectively) and harboured substantially lower genetic diversity. We also found signs of gene flow via pollen or seed dispersal from natural to restored populations but not vice versa. Policy implications. Regionally sourced seeds can produce genetically diverse populations at natural levels of genetic differentiation.

opencc-by-4.0Dec 2021View details →

PyVOLCANS: A Python package to flexibly explore similarities and differences between volcanic systems

Python tool to identify analogue volcanoes via <a href="https://doi.org/10.1007/s00445-019-1336-3">VOLCANS</a>. The main goal of PyVOLCANS is to help alleviate data-scarcity issues in volcanology, and contribute to developments in a range of topics, including (but not limited to): quantitative volcanic hazard assessment at local to global scales, investigation of magmatic and volcanic processes, and even teaching and scientific outreach. We hope that future users of PyVOLCANS will include any volcano scientist or enthusiast with an interest in exploring the similarities and differences between volcanic systems worldwide. Please visit our <a href="https://github.com/BritishGeologicalSurvey/pyvolcans/wiki">wiki pages</a> for more information.

opencc-by-4.0Feb 2022View details →

Isolation-by-distance and genetic parentage analysis provide similar larval dispersal estimates

An R studio project that includes original SNP data files used to quantify dispersal in Elacatinus lori via the isolation-by-distance (IBD) method. Associated R-code used to generate IBD regression slopes, calculate sigma, and construct dispersal kernels. Includes output from NeEstimator,  estimating effective population size.  Folders 1-3 contain the code/data needed to obtain the slope of the IBD relationship, effective population size, and the standard deviation (sigma) of the dispersal distribution, respectively. Folder 4 contains the R code needed to construct Laplacian dispersal kernels.

opencc-zeroMay 2022View details →

Data from: Flowering overlap and floral trait similarity help explain the structure of pollination network

Co-flowering communities are usually characterized by high plant generalization but knowledge of the underlying factors leading to high levels of generalization and pollinator sharing, and how these may contribute to network structure is still limited. Flowering phenology and floral trait similarity are considered among the most important factors determining plant generalization and pollinator sharing. However, these have been evaluated independently even though they can act in concert with each other. Moreover, the importance of flowering phenology and floral similarity, via their effects on plant generalization, in the structure of plant–pollinator networks have been scarcely studied. Here, we aim to evaluate the effect of flowering phenology and floral similarity in mediating the degree of pollinator sharing and plant generalization in two coastal communities and uncover their importance as drivers of plant–pollinator network structure. We recorded flower production per species, as well as the identity and frequency of floral visitors along the entire flowering season. We estimated the degree of flowering overlap, the degree of floral similarity (using floral traits associated with size and color), and the degree of pollinator sharing among plant species within both communities. Structural equation models (SEM) showed a positive effect of flowering overlap on pollinator sharing and plant generalization. Pollinator sharing and plant generalization positively affected network nestedness. Furthermore, SEM showed a direct positive effect of flowering overlap on network modularity. The SEM analyses also revealed a significant interaction effect of floral similarity and flowering overlap on pollinator sharing, with consequences for network nestedness in one community. Our results highlight the importance of integrating multiple axes of differentiation such as flowering phenology and floral similarity into our understanding of the drivers of plant–pollinator network structure.