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opencc-by-4.0Sep 2020View details →

Dataset of Proportion of non-native plants in urban parks correlates with climate, socioeconomic factors and plant traits

Full datasets for the research entitled 'Proportion of non-native plants in urban parks correlates with climate, socioeconomic factors and plant traits'.

opencc-by-4.0Nov 2024View details →

Proportions of Phytoplankton Functional Groups (PFT) retrieved using a Self-Organizing Map in the North Atlantic

Concentrations of diagnostic pigments are retrieved from satellite data (Chl-a + Rrs at 4 wavelengths + SST) using a SOM trained on a global in-situ HPLC dataset (see El Hourany et al. 2019). Pigments are converted to PFTs using empirical coefficients. Three sets of coefficients are used and the results are averaged (see El Hourany et al. 2024). The PFT data is expressed as the proportion of each group in the total community abundance: <code>Proportion_i = (alpha_i * Pig_i) / sum_j(alpha_j * Pig_j)</code> There are seven PFT groups (diatoms, dinoflagellates, haptophytes, green algae, cryptophytes, pelagophytes, prokaryotes). The satellite input Chl-a data is included as well. It was retrieved from the Globcolour portal (<a href="https://hermes.acri.fr/">https://hermes.acri.fr/</a>) in 2022. The CHL-1 product for case 1 waters is used. It uses the AVW merging method: single-sensor level-2 Chl-a data is merged from multiple sensors (SeaWiFS, MERIS, MODIS-Aqua, VIIRS-NPP/JPSS1, OLCI-A/B). The data spans from 2002 to 2020, at a daily resolution. It is available on a regular latitude/longitude grid, at a resolution of 1/24° (approximately 4 km) in a window of bounds 15°N−55°N ; 82°W−40°W. <h2>Storage</h2> All variables are stored in the same daily file: <code>PFT_SOM-DAP_GLOB_4km_daily/[year]/[month]/PFT_[year][month][day]_GLOB_4.nc</code>. The PFT proportions are stored as percentages (ranging between 0 and 100). Files are NetCDF4, and the metadata follow CF conventions. The PFT variables are stored using linear packing (see <a href="https://nco.sourceforge.net/nco.html#Linear-Packing">https://nco.sourceforge.net/nco.html#Linear-Packing</a>) as 16-bits unsigned integers (<code>NC_USHORT</code>), with a scale factor of 1.54e-3. This means values are discretized between 0 and ~100.92 (<code>=(2**16 - 2) * 1.54e-3</code>), with a discretization step of 1.54e-3. As for the PFT variables, these values are in percent points. <h2>References</h2> <ul> <li>El Hourany, R., Abboud-Abi Saab, M., Faour, G., Aumont, O., Crépon, M., Thiria, S. “Estimation of secondary phytoplankton pigments from satellite observations using Self-Organizing Maps (SOMs)”, J. Geophys. Res. Oceans 124, 1357–1378, <a href="https://doi.org/10.1029/2018jc014450">https://doi.org/10.1029/2018jc014450</a>, 2019</li> <li> <div>El Hourany, R, Pierella Karlusich J., Zinger L., Loisel H., Levy M., and Bowler C. “Linking Satellites to Genes with Machine Learning to Estimate Phytoplankton Community Structure from Space”, Ocean Science 20 no. 1, 217−39. <a href="https://doi.org/10.5194/os-20-217-2024">https://doi.org/10.5194/os-20-217-2024</a>, 2024</div> </li> </ul> <h2>Changelog</h2> <h3>v1.2</h3> <ul> <li>[2024-01-24] Finished re-arranging data and checked validity. 553 missing days / source files (about 6% of total data).</li> <li>[2023-12-01] Fix SST projection at PFT generation. Data fully re-generated.</li> </ul> <h3>v1.1</h3> <ul> <li>[2023-08-30] Store PFT data using linear packing. </li> </ul> <h3>v1.0</h3> <ul> <li>[2023-08-03] those data are reorganised, and metadata is added, using the script <a href="https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads">https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads</a></li> <li>[2023-07] data generated with the SOM were stored on `spirit:/data/lollier/PFT`</li> </ul>

opencc-by-4.0Oct 2021View details →

Point and Nonpoint Proportion of Potentially Contaminated Supply (PPCS) for 116 United States cities

Point and nonpoint PPCS metrics (and additional metrics) computed for 116 United States cities using gamut (Geospatial Analytics for Multisectoral Urban Teleconnections)---<a href="https://doi.org/10.5281/zenodo.5590217">https://doi.org/10.5281/zenodo.5590217</a>. These results are described in the following publication: Turner, S.W.D., Rice, J., Nelson, K., Vernon, C., McManamay, R., Dickson, K., and Marston, L. (accepted manuscript) Comparison of potential drinking water source contamination across one hundred U.S. cities. Nature Communications.

opencc-by-4.0Jan 2022View details →

Raw EEG Data for: Learning from Label Proportions in Brain-Computer Interfaces

If you prefer to use the preprocessed and epoched data, please refer to: https://zenodo.org/record/192684 Note that this repository ontains only the visual paradigm with the N=13 subjects recorded at 31 EEG channels, as described in the above link. We copied the relevant section of the description below: <blockquote> This data repository contains raw EEG of an EEG experiment utilizing visual event-related potentials (ERPs) with N=13 healthy subjects. The dataset is used and described in the following journal article: Hübner, D., Verhoeven, T., Schmid, K., Müller, K. R., Tangermann, M., & Kindermans, P. J. (2017). Learning from label proportions in brain-computer interfaces: online unsupervised learning with guarantees. PloS one, 12(4), e0175856. Please cite the above article when using the data. The data set with N=13 subjects is different to ordinary ERP datasets in the sense that the train of stimuli to spell one character (68) is divided into repetitions of two interleaved sequences with length 8 and 18, respectively. We added '#' symbols to the spelling matrix which should never be attended by the subject and hence, are non-targets by definition. The first, shorter sequence, now highlights only ordinary characters, while the second sequence also highlights '#' -- visual blank symbols. By construction, sequence 1 has a higher target ratio than sequence 2. These known, but different target and non-target proportions are then used to reconstruct the target and non-target class means. This approach which does not need explicit class labels is termed Learning from Label Proportions (LLP). It can be used to decode brain signals without prior calibration session. More details can be found in the article. In another study, the above data set was used to simulate a new unsupervised mixture approach which combines the mean estimation of the unsupervised expectation-maximization algorithm by Kindermans et al. (2012, PLoS One) with the means obtained with the LLP approach. This leads to an unsupervised solution for which the performance is as good as in the supervised scenario. Please find more details in the following article: Verhoeven, T., Hübner, D., Tangermann, M., Müller, K. R., Dambre, J., & Kindermans, P. J. (2017). Improving zero-training brain-computer interfaces by mixing model estimators. Journal of neural engineering, 14(3), 036021. </blockquote> The data was recorded with BrainVision recorder. A new file was recorded for every group of 7 characters. The .eeg file contains the RAW EEG data in the format as described in the .vhdr file. Events / stimuli markers are provided in the .vmrk files. Note that there is a wrapper available to use this data in MOABB here: TODO INSERT LINK The subjects had the task to spell a specific sentence with 63 letters. In the online experiment, this was repeated 3 times and each time the online unsupervised classifier was reset at the start of the sentence.

opencc-by-4.0Jun 2022View details →

Data from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional horizontal live load

For each one of the simulations performed from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional horizontal live load, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry buttressed arch. Finally, the .png file presents the collapse mechanism obtained.

opencc-by-4.0Jun 2022View details →

Data from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional concentrated vertical live load applied at mid-span

For each one of the simulations performed from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional concentrated vertical live load applied at mid-span, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry buttressed arch. Finally, the .png file presents the collapse mechanism obtained.

opencc-by-4.0Oct 2023View details →

Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data.

Bulk ATAC-seq data of tumour samples result in an averaged signal across different cell-types (cancer, stromal, vascular and immune cells). We propose a deconvolution framework called EPIC-ATAC (<a href="https://doi.org/10.7554/eLife.94833.1">https://doi.org/10.7554/eLife.94833.1</a>), which relies on newly identified cell-type specific ATAC-Seq marker peaks and reference profiles for all major cancer-relevant cell-types to predict the proportions of each cell-type. To evaluate EPIC-ATAC, we generated a bulk ATAC-Seq dataset from peripheral blood mononuclear cells (PBMCs) samples, from which the number of cells in each cell-type has been estimated using flow cytometry, as ground truth for cell proportions. The data provided in this Zenodo deposit correspond to: - The raw counts matrix for each peak called in this ATAC-Seq dataset: PBMC_counts.txt - The normalized (TPM-like) counts matrix for each peak called in this ATAC-Seq dataset: PBMC_counts_norm.txt - The cell fractions of each cell type in each sample: PBMC_cell_fractions.txt - The peaks called in each sample using MACS2 (*narrow.peaks): *_normalized.narrowPeak - Bed files listing ATAC-Seq fragments for each sample: *.bed We also evaluated EPIC-ATAC on multiple pseudobulks generated from single-cell ATAC-Seq data. We provide rds files containing the pseudobulks data used in our work for the evaluation of EPIC-ATAC. The rds files are located in the zip file "pseudobulks.zip". The file "additional_data.zip" contains additional files used to generate the reference profiles in EPIC-ATAC and to reproduce the main analyses performed in the manuscript: <a href="https://doi.org/10.7554/eLife.94833.1">https://doi.org/10.7554/eLife.94833.1</a>. These files are required to run the code available on the following GitHub repository: GfellerLab/EPIC-ATAC_manuscript.

opencc-by-4.0Jun 2018View details →

Comparatives, Quantifiers, Proportions

The present work investigates whether different quantification mechanisms (set comparison, vague quantification, and proportional estimation) can be jointly learned from visual scenes by a multi-task computational model. The motivation is that, in humans, these processes underlie the same cognitive, non-symbolic ability, which allows an automatic estimation and comparison of set magnitudes. We show that when information about lower complexity tasks is available, the higher-level proportional task becomes more accurate than when performed in isolation. Moreover, the multi-task model is able to generalize to unseen combinations of target/non-target objects. Consistently with behavioral evidence showing the interference of absolute number in the proportional task, the multi-task model no longer works when asked to provide the number of target objects in the scene.

opencc-by-4.0Jul 2021View details →

Data from the parametric analysis of masonry pointed arches with limit analysis subjected to vertical self-weight plus a proportional horizontal live load

For each one of the simulations performed from the parametric analysis of masonry pointed arches with limit analysis, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry panel. Finally, the .png file presents the collapse mechanism obtained.

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Effects of nutrients and organic carbon on the relative proportion of primary producers (microalgae) and heterotrophic decomposers (bacteria and fungi) during aquatic biofilm development in boreal peatland located near Fairbanks Alaska - 2018

1. Producer-decomposer interactions within aquatic biofilms can range from mutualistic associations to competition depending on available resources. The outcomes of such interactions have implications for biogeochemical cycling, and as such, may be especially important in northern peatlands, which are a global carbon sink and are expected to experience changes in resource availability with climate change. The purpose of this study was to evaluate the effects of nutrients and organic carbon on the relative proportion of primary producers (microalgae) and heterotrophic decomposers (bacteria and fungi) during aquatic biofilm development in a boreal peatland. Given that decomposers are often better competitors for nutrients than primary producers in aquatic ecosystems, we predicted that labile carbon subsidies would shift the biofilm composition towards heterotrophy owing to the ability of decomposers to outcompete primary producers for available nutrients in the absence of carbon limitation. 2. We manipulated nutrients (nitrate and phosphate) and organic carbon (glucose) in a full factorial design using nutrient-diffusing substrates in an Alaskan fen. 3. Heterotrophic bacteria were limited by organic carbon and algae were limited by inorganic nutrients. However, the outcomes of competitive interactions depended on background nutrient levels. Heterotrophic bacteria were able to outcompete algae for available nutrients when organic carbon was elevated and nutrient levels remained low, but not when organic carbon and nutrients were both elevated through enrichment. 4. Fungal biomass was significantly lower in the presence of glucose alone, possibly owing to antagonistic interactions with heterotrophic bacteria. In contrast to bacteria, fungi were stimulated along with algae following nutrient enrichment. 5. The decoupling of algae and heterotrophic bacteria in the presence of glucose alone shifted the biofilm trophic status towards heterotrophy. This effect was overturned

openOpenMar 2021View details →

FIG. 15 in New remains of kollpaniine "condylarths" (Panameriungulata) from the early Palaeocene of Bolivia shed light on hypocone origins and molar proportions among ungulate-like placentals

FIG. 15. — Upper molar proportions in Euungulata, "Condylarthra", SANUs, and the kollpaniines from Tiupampa described here. Molar proportions are plotted in the developmental 'morphospace' (Kavanagh et al. 2007; Polly 2007) where the white region is consistent with the IC model; the broken line is the relationship predicted for lower molar of murine rodents (see Material and methods and Table 8). Abbreviations: Kalith., Kalitherium.

FIG. 11 in New remains of kollpaniine "condylarths" (Panameriungulata) from the early Palaeocene of Bolivia shed light on hypocone origins and molar proportions among ungulate-like placentals

FIG. 11. — Pucanodus gagnieri: partial right mandible with m2-3 (MHNC 13869): A, stereophotograph of occlusal view; B, the same in labial view. Scale bar: 5 mm.

FIG. 4 in New remains of kollpaniine "condylarths" (Panameriungulata) from the early Palaeocene of Bolivia shed light on hypocone origins and molar proportions among ungulate-like placentals

FIG. 4. — Molinodus suarezi: partial maxilla (MHNC 13870) with incomplete M1-2 and complete M3. Stereophotograph of occusal view. Scale bar: 5 mm.

FIG. 7 in New remains of kollpaniine "condylarths" (Panameriungulata) from the early Palaeocene of Bolivia shed light on hypocone origins and molar proportions among ungulate-like placentals

FIG. 7. — Simoclaenus sylvaticus: partial right mandible with alveolus of p1, root of p2-3, p4 and m1 (MHNC 13872): A, stereophotographs of occlusal view; B, the same in lateral view; C, the same in medial view. Scale bar: 5 mm.

FIG. 2 in New remains of kollpaniine "condylarths" (Panameriungulata) from the early Palaeocene of Bolivia shed light on hypocone origins and molar proportions among ungulate-like placentals

FIG. 2. — Partial left mandible of Molinodus suarezi (MHNC 13867) bearing p3-m3: A, stereophotographs of the occlusal view; B, lingual view; C, labial view. Scale bar: 5 mm.

FIG. 13. — A, B in New remains of kollpaniine "condylarths" (Panameriungulata) from the early Palaeocene of Bolivia shed light on hypocone origins and molar proportions among ungulate-like placentals

FIG. 13. — A, B, Lamegoia conodonta; C, didolodontidae indet.; A, occlusal view of a left m2 of Lamegoia conodonta (cast of holotype MNRJ 1463-V); B, occlusal view of a right M2 (reversed) of Lamegoia conodonta (cast of MNRJ 1465-V); C, occlusal view of a left M2 (cast of MNRJ 1464-V) of and undetermined didolodont (referred by Paula Couto [1952a] to L. conodonta). Scale bar: 5 mm.

FIG. 5 in New remains of kollpaniine "condylarths" (Panameriungulata) from the early Palaeocene of Bolivia shed light on hypocone origins and molar proportions among ungulate-like placentals

FIG. 5. — Molinodus suarezi: partial maxilla (MHNC 13870): A, occlusal view; B, lingual view. Scale bar: 5 mm.