Find research datasets worth reusing

opennotspecifiedJul 2016View details →

Distribution. SW Brazil, known only from two sites, the type locality in Rondonia and Juruena (Mato Grosso State)Descriptive notes Head-body ¢.230 mm, tail ¢.80 mm. No specific data are available for body weight. Rondon's Tuco-tuco is medium-sized. Dorsal hairs are pale at bases and sepia at tips. Head and venterare slightly rufous, and tail is uniform brown. Skull is robust and depressed. Inter-maxillaries are also robust, with lateral protruding expansion; maxillaries are narrow; and mandible is strong and wide. Supraorbital process protrudes, and traverse occipital-temporal crest is straight. Bullae are inflated. in Ctenomyidae

Distribution. SW Brazil, known only from two sites, the type locality in Rondonia and Juruena (Mato Grosso State)Descriptive notes Head-body ¢.230 mm, tail ¢.80 mm. No specific data are available for body weight. Rondon's Tuco-tuco is medium-sized. Dorsal hairs are pale at bases and sepia at tips. Head and venterare slightly rufous, and tail is uniform brown. Skull is robust and depressed. Inter-maxillaries are also robust, with lateral protruding expansion; maxillaries are narrow; and mandible is strong and wide. Supraorbital process protrudes, and traverse occipital-temporal crest is straight. Bullae are inflated.

Script and data of "Role of Frictional Processes in Mesoscale Eddy Available Potential Energy Budget in the Global Ocean"

% File description: 1. Cal_conversions.m: a set of functions calculating the EAPE-EKE and EAPE-EKE conversion terms with CESM output data in B-grid 2. smooth2a.m: function of boxcar filtering 3. CONV_u100_2d.mat: data of the global distribution of upper 100 m averaged conversion terms used in Figure 2 of the manuscript % Variables inside the file:     CONVa_H_u100: MAPE-EAPE conversion driven by frictional process     CONVo_H_u100: MAPE-EAPE conversion driven by non-frictional process     CONVa_V_u100: EAPE-EKE conversion driven by frictional process     CONVo_V_u100: EAPE-EKE conversion driven by non-frictional process 4. CONV_profile.mat: data of the vertical profiles of global and regional averaged EAPE-EKE conversion terms used in Figure 3 of the manuscript % Variables inside the file:     % Vertical profiles of quasi-global-averaged EAPE-EKE conversion      CONVa_V_GLO_profile: driven by frictional process     CONVo_V_GLO_profile: driven by non-frictional process     CONVttw_V_GLO_profile: reproduced by TTW balance           % Vertical profiles of EAPE-EKE conversion averaged in western boundary current regions     CONVa_V_WBCE_profile: driven by frictional process     CONVo_V_WBCE_profile: driven by non-frictional process     CONVttw_V_WBCE_profile: reproduced by TTW balance      % Vertical profiles of EAPE-EKE conversion averaged in subtropical gyres     CONVa_V_STG_profile: driven by frictional process     CONVo_V_STG_profile: driven by non-frictional process     CONVttw_V_STG_profile: reproduced by TTW balance           % Vertical profiles of EAPE-EKE conversion averaged in subpolar gyres     CONVa_V_SPG_profile: driven by frictional process     CONVo_V_SPG_profile: driven by non-frictional process     CONVttw_V_SPG_profile: reproduced by TTW balance      % Vertical profiles of EAPE-EKE conversion averaged in the Southern Ocean     CONVa_V_SO_profile: driven by frictional process     CONVo_V_SO_profile: driven by non-frictional process     CONVttw_V_SO_profile: reproduced by TTW balance  5. CONV_SeasDiff.mat: data of the seasonal difference (winter minus summer) of global and regional averaged conversion terms used in Figure 3 of the manuscript % Variables inside the file:     % Vertical profiles of the seasonal difference of quasi-global-averaged EAPE-EKE conversion      CONVa_V_GLO_SeasDiff: driven by frictional process     CONVo_V_GLO_SeasDiff: driven by non-frictional process     CONVttw_V_GLO_SeasDiff: reproduced by TTW balance           % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in western boundary current regions     CONVa_V_WBCE_SeasDiff: driven by frictional process     CONVo_V_WBCE_SeasDiff: driven by non-frictional process     CONVttw_V_WBCE_SeasDiff: reproduced by TTW balance      % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in subtropical gyres     CONVa_V_STG_SeasDiff: driven by frictional process     CONVo_V_STG_SeasDiff: driven by non-frictional process     CONVttw_V_STG_SeasDiff: reproduced by TTW balance           % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in subpolar gyres     CONVa_V_SPG_SeasDiff: driven by frictional process     CONVo_V_SPG_SeasDiff: driven by non-frictional process     CONVttw_V_SPG_SeasDiff: reproduced by TTW balance      % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in the Southern Ocean     CONVa_V_SO_SeasDiff: driven by frictional process     CONVo_V_SO_SeasDiff: driven by non-frictional process     CONVttw_V_SO_SeasDiff: reproduced by TTW balance  6. Coord_lon_lat_zw.mat: coordinate information for the variables in "CONV_u100_2d.mat", "CONV_profile.mat"and "CONV_SeasDiff.mat"   % Variables inside the file:     lon: longitude for the global distributions of the conversion terms     lat: latitude for the global distributions of the conversion terms     z_w: depth of each vertical level for vertical profiles of conversion terms

openother-openJun 2021View details →

Data bundle for egon-data: A transparent and reproducible data processing pipeline for energy system modeling

egon-data provides a transparent and reproducible open data based data processing pipeline for generating data models suitable for energy system modeling. The data is customized for the requirements of the research project eGon. The research project aims to develop tools for an open and cross-sectoral planning of transmission and distribution grids. For further information please visit the eGon <a href="https://ego-n.org/">project website</a> or its <a href="https://github.com/openego/eGon-data">Github repository.</a> egon-data retrieves and processes data from several different external input sources. As not all data dependencies can be downloaded automatically from external sources we provide a data bundle to be downloaded by egon-data. The following data sets are part of the available data bundle: <ol> <li>climate_zones_germany <ul> <li>Climate zones in Germany</li> <li>source: Own representation based on DWD TRY climate zones</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>emobility <ul> <li>Data on eMobility mit_trip_data: motorized individual travel - individual trips of electric vehicles (EV) generated with a modified version of simBEV v0.1.3 (https://github.com/rl-institut/simbev/tree/1f87c716d14ccc4a658b8d2b01fd12b88a4334d5). simBEV generates driving profiles for BEVs and PHEVs based upon MID data (BMVI) per RegioStaR7 region type (BBSR).</li> <li>Reiner Lemoine Institut, June 2022</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>geothermal_potential <ul> <li>Spatial distribution of deep geothermal potentials in Germany</li> <li>source: <a href="https://doi.org/10.3390/en11020332">Assessment and Public Reporting of Geothermal Resources in Germany: Review and Outlook</a></li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>household_electricity_demand_profiles <ul> <li>Annual profiles in hourly resolution of electricity demand of private households for different household types (singles, couples, other) with varying number of elderly and children. The profiles were created using a bottom-up load profile generator by Fraunhofer IEE developed in the Bachelor's thesis "Auswirkungen verschiedener Haushaltslastprofile auf PV-Batterie-Systeme" by Jonas Haack, Fachhochschule Flensburg, December 2012. The columns are named as follows: "<HH_TYPE_PREFIX>a<PROFILE_ID>", e.g. P2a0000 is the first profile of a couple's household with 2 children. See publication below for the list of prefixes. Values are given in Wh. A related conference paper can be obtained here: http://publica.fraunhofer.de/documents/N-374761.html</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>household_heat_demand_profiles <ul> <li>Sample heat time series including hot water and space heating for single- and multi-familiy houses. The profiles were created using the loadprofile generator by Fraunhofer IEE developed in the Master's thesis "Synthesis of a heat and electrical load profile for single and multi-family houses used for subsequent performance tests of a multi-component energy system", Simon Ruben Drauz, RWTH Aachen University, March 2016</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>hydrogen_storage_potential_saltstructures <ul> <li>The data are taken from figure 7.1 in Donadei, S., et al., (2020), p. 7-5..</li> <li>Source: Flach lagernde Salze, (c) BGR Hannover, 2021. Datenquelle: InSpEE-Salzstrukturen, (c) BGR, Hannover, 2015. & Donadei, S., Horváth, B., Horváth, P.-L., Keppliner, J., Schneider, G.-S., & Zander-Schiebenhöfer, D. (2020). Teilprojekt Bewertungskriterien und Potenzialabschätzung. BGR. Informationssystem Salz: Planungsgrundlagen, Auswahlkriterien und Potenzialabschätzung für die Errichtung von Salzkavernen zur Speicherung von Erneuerbaren Energien (Wasserstoff und Druckluft) – Doppelsalinare und flach lagernde Salzschichten: InSpEE-DS. Sachbericht. Hannover: BGR.</li> <li>License: The original data are licensed under the GeoNutzV, see https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf</li> </ul> </li> <li>industrial_sites <ul> <li>Information about industrial sites with DSM-potential in Germany from a Master's thesis by Danielle Schmidt. The data set includes own information on the coordinates of every industrial site.</li> <li>source: Schmidt, Danielle. (2019). Supplementary material to the masters thesis: NUTS-3 Regionalization of Industrial Load Shifting Potential in Germany using a Time-Resolved Model [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3613767</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>nep2035_version2021 <ul> <li>Data extracted from the German grid development plan - power</li> <li>source: Netzentwicklungsplan Strom 2035 (2021), erster Entwurf | Übertragungsnetzbetreiber (M) CC-BY-4.0</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>pipeline_classification_gas <ul> <li>Parameters for the classification of gas pipelines</li> <li>source: Single parameters extracted from <a href="https://www.econstor.eu/bitstream/10419/173388/1/1011162628.pdf">Electricity, Heat and Gas Sector Data for Modelling the German System</a></li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>pypsa_eur_sec <ul> <li>Preliminary results from scenario generator pypsa-eur-sec</li> <li>source: own calculation using pypsa-eur-sec fork (https://github.com/openego/pypsa-eur-sec)</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>regions_dynamic_line_rating <ul> <li>German regions suitable to model dynamic line rating</li> <li>source: Own representation based on <a href="https://www.transnetbw.de/files/pdf/netzentwicklung/netzplanungsgrundsaetze/UENB_PlGrS_Juli2020.pdf">Grundsätze für die Ausbauplanung des Deutschen Übertragungsnetze (2020)</a></li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>re_potential_areas <ul> <li>Eligible areas for wind turbines and ground-mounted PV systems.</li> <li>Reiner Lemoine Institut, January 2022</li> <li>License: Attribution 4.0 International (CC BY 4.0)</li> </ul> </li> <li>WZ_definition <ul> <li>Definitions of industrial and commercial branches</li> <li>source: <a href="https://www.destatis.de/static/DE/dokumente/klassifikation-wz-2008-3100100089004.pdf">Klassifikation der Wirtschaftszweige (WZ 2008)</a></li> <li>Extract from Terms of Use: © Statistisches Bundesamt, Wiesbaden 2008 Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellenangabe gestattet.</li> </ul> </li> <li>zensus_households <ul> <li>Dataset describing the amount of people living by a certain types of family-types, age-classes,sex and size of household in Germany in state-resolution.</li> <li>source: Data retrieved from <a href="https://ergebnisse2011.zensus2022.de/datenbank/online">Zensus Datenbank</a> by performing these steps: <ul> <li>Search for: "1000A-2029"</li> <li>or choose topic: "Bevölkerung kompakt"</li> <li>Choose table code: "1000A-2029" with title "Personen: Alter (11 Altersklassen)/Geschlecht/Größe desprivaten Haushalts - Typ des privaten Haushalts (nach Familien/Lebensform)"</li> <li>Change setting "GEOLK1" to "Bundesländer (16)" higher resolution "Landkreise und kreisfreie Städte (412)" only accessible after registration.</li> </ul> </li> <li>Extract from Terms of Use: © Statistische Ämter des Bundes und der Länder 2021, Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellennachweis gestattet.</li> </ul> </li> </ol>

Data related to "Near-bed sediment transport processes during onshore bar migration in large-scale experiments. Comparison with offshore bar migration."

Abstract: This paper presents novel insights into nearshore sediment transport processes during bar migration on the basis of large-scale laboratory experiments with bichromatic wave groups on a relatively steep initial beach slope (1:15). Insights are based on detailed measurements of velocity and sand concentration near the bed from shoaling up to the outer breaking zone including suspended sediment and sheet flow transport. The analysis focuses on onshore migration under an accretive wave condition but comparison to an erosive condition highlights important differences. Decomposition shows that total net transport mainly results from a balance of short wave-related, bedload net onshore transport and current-related, suspended net offshore transport. When comparing the accretive to the more energetic erosive condition, the balance shifts towards net onshore transport, and onshore migration, because the short wave-related transport does not decrease as much as the current-related transport. This is related to the effects of skewness and asymmetry combined with larger sediment entrainment and undertow magnitude under the erosive condition. Net transports from streaming in the wave boundary layer and from infragravity waves are noticeable but only play a subordinate role. Identified priorities for numerical model development include parametrization of wave nonlinearity effects and better description of wave breaking and its influences on sediment suspension. The present data, unique in their combination of high measurement detail with fully-evolving accretive beach profiles, help to improve numerical modeling of long-term morphological evolution. About the data: The folder “Beach Profiles” contains the measurements from the mechanical profiler before and after each test. To save time, only the morphologically active section of the profiles was measured. Additionally, the folder contains the initial profiles at the start of each sequence (after application of the benchmark waves). Here the full profile was measured. The structure “MobFrame” contains the absolute cross-shore position of the mobile frame (from which detailed measurements were taken) in the considered tests. The folder “ACVP” contains structures with ensemble-averaged velocity and concentration measurements in vertical reference to the undisturbed bed level or a few bins below it (zeta0-coordinate system as described in the paper). For better interpretation of the measurements, it also features the ensemble-averaged intrawave instantaneous bed elevation (erosion depth) and the upper limit of the sheet flow layer. The folder “ADV” contains structures with the ensemble-averaged ADV data of each test. Apart from the velocity components of each ADV they contain the vertical elevation of each ADV with respect to the ACVP transceiver. The ADV measurements were not subject to the same vertical referencing procedure that was described in the paper for the near-bed ACVP measurements. The folder “OBS” contains structures with the ensemble-averaged OBS data of each test. Apart from the concentration measurements in each OBS sensor they contain the vertical elevation of each OBS with respect to the ACVP transceiver. The folder “ETA” contains structures with the ensemble-averaged surface elevation data of each test (from different instruments as described in the paper). The location of each instrument is given in absolute cross-shore coordinates x.   For visualizing the near-bed concentration data, which may not be as trivial as visualizing the rest of the data, an example of MATLAB code is given: %S=ACVP_xx; %to choose which ACVP file you want to look into con=S.c; con(con<1)=1; %to cater for the cells where the logarithm is not defined xphase=linspace(0,1,length(S.solbed)).*ones(size(S.c,2),size(S.c,1)); figure; hold on; box on; [C,h]=contourf(xphase,S.z,log10(transpose(con)),[0:0.1:3]); cbh=colorbar; caxis([0 3]); set(h,'edgecolor','none'); tt=get(cbh,'Title'); set(tt,'String','$log_{10}(c)$ $[kg/m^3]$','Interpreter','Latex'); plot(xphase(1,:),S.solbed,'k','Linewidth',1.5); plot(xphase(1,:),S.solflo,'r','Linewidth',1.5); xlabel('$t/T_r$','Interpreter','Latex') ylabel('$\zeta_0$ $[m]$','Interpreter','Latex') set(gca,'Fontsize',18)

Source data for the paper A laser-assisted chlorination process for reversible writing of doping patterns in graphene

Source Data supporting the plots within the paper "A laser-assisted chlorination process for reversible writing of doping patterns in graphene."  Including Source Data for Figures 1-4 in the main text, and Supplementary Figures S1, S3-12, S14, S17.

Source data for the paper A laser-assisted chlorination process for reversible writing of doping patterns in graphene

Processed Seurat Object of scRNAseq data from wildtype and CaMKK2 KO immune infiltrate of CT2a preclinical murine glioma

This repository contains the processed Seurat objects generated from the raw data deposited at the Gene Expression Omnibus (GEO) under GSE197879. Details about the experiment and sequencing are available under GSE197879. Information on how the Seurat objects were created can be found in this GitHub repository https://github.com/wht10/CT2A_scRNAseq_CaMKK2KOvWT . Notable metadata within each Seurat object: 1. Processed_CD45_Live_Fig2b.rds <ul> <li>Genotype - whether the cell is from a WT or CaMKK2 KO mouse</li> <li>HTO_maxID - The biological replicate that the cell came from (4 biological replicates per genotype)</li> <li>MouseID - A concatenation between the genotype and HTO_maxID, providing a unique identifier for each biological replicate</li> <li>Cell.Type - The cell type annotations for each cell. Can be assigned to "Idents()" to change the name of the cell identities.</li> <li>Geno.Ident - A concatenation between Genotype and Cell.Type. By re-assigning this to "Idents()"  "FindMarkers()" can be used to investigate differentially expressed genes within a cell-type between genotypes. </li> </ul> 2. Reclustered_TILs_Fig3a.rds <ul> <li>Genotype - whether the cell is from a WT or CaMKK2 KO mouse</li> <li>HTO_maxID - The biological replicate that the cell came from (4 biological replicates per genotype)</li> <li>MouseID - A concatenation between the genotype and HTO_maxID, providing a unique identifier for each biological replicate</li> <li>Celltype - The cell type annotations for each cell. Can be assigned to "Idents()" to change the name of the cell identities.</li> <li>Geno_Ident - A concatenation between Genotype and cell-type. By re-assigning this to "Idents()"  "FindMarkers()" can be used to investigate differentially expressed genes within a cell-type between genotypes. </li> </ul>

Source data for the paper "A laser-assisted chlorination process for reversible writing of doping patterns in graphene"

opencc-by-4.0Dec 2021View details →

Text simplification in second language: process and product data

This folder contains data on text simplification collected from an experimental study with second-language university students. We adopted a pre-test and post-test design, and randomly divided participants into experimental and control group. In the pre-test, participants were given an extract of a corporate report dealing with sustainability and were asked to revise it to make it easier to read for a lay customer. Subsequently, they took part in training. The experimental group received training on both plain language and sustainability, while the control group received training exclusively on the topic of sustainability. In the post-test session (2-3 days after the pre-test), all participants were assigned a second extract of a corporate report dealing with sustainability, and were asked again to make it easier to read for a lay customer by applying what they had learned from their respective training. This design allowed us to examine the impact of plain language training on text simplification (revision) tasks. The texts were in English while the participants were native speakers of other languages (mainly Dutch), so the text simplification took place in their second language. This project (PLanTra) has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 888918. Please see "readme" file for additional information.

dryad32/100

Data from: Regeneration processes on coarse woody debris in mixed forests: do tree germinants and seedlings have species-specific responses when grown on coarse woody debris?

Tree regeneration on coarse woody debris (CWD) is considered to be one the most ecologically valuable aspects of CWD in forest systems. However, most studies have focused solely on uncovering the differences in establishment and growth on CWD (regarded as a homogeneous substrate) in comparison with the forest floor. Our study concentrates on the underlying mechanisms of germinant and seedling colonization patterns and demographic responses relative to various properties of CWD. We analysed the effects of CWD properties (decay class, form, species and diameter) on: (i) germinant and seedling annual counts; (ii) annual germinant and seedling survival; and (iii) seedling growth and height. Our study comprised three species (beech, fir and spruce) over 7-year span in two old-growth stands in the Western Carpathians, and employed generalized linear models and mixed models to test for differences between species. CWD properties affected regeneration at the germination stage. There were some demographic differences between species relative to CWD properties. Decay class had the most pronounced effect on beech, not only on its establishment but also survival and growth. Beech and spruce established and survived in higher densities on beech CWD, while their height growth was enhanced on conifer-derived CWD, particularly on spruce CWD. Stumps enhanced establishment of all species and survival of conifer seedlings. Synthesis. Our study shows that CWD properties do influence seedling establishment, growth, height distribution and survival. Moreover, there may be trade-offs between seedling growth and survival among tree species growing on different species of CWD. This highlights the need to include CWD heterogeneity as a factor that can affect the role of CWD in regeneration in forest ecosystems.

opencc-zeroDec 2015View details →

dryad32/100

Data from : Soil pH determines bacterial distribution and assembly processes in natural mountain forests of eastern China

Aim: There have been numerous studies of forest-soil microbial biogeography, but an integrated view of edaphic factors, plant, climatic factors, and geographic distance in determining the variation of bacterial community and assembly processes remains unclear at large spatial scales. Here, we analyzed the factors affecting the biogeographic pattern and assembly processes of soil bacterial communities under 58 tree species in five natural mountain forests. Location: Eastern China. Major taxa studied: Bacterial communities. Methods: Hierarchical partitioning analysis and distance decay models were performed to evaluate the relative contributions of plant phylogeny, environmental, and spatial variables to the composition of bacterial communities. We applied the Nearest Taxon Index (NTI), β-Nearest Taxon Index (βNTI), and the modified Raup-Crick metric to reveal the mechanisms of bacterial assembly processes. Results: We found that plant phylogeny accounted for a significant, but minor, fraction (0.7%) of the variation in composition of bacterial communities. In contrast, soil pH was the primary determinant of bacterial diversity and community composition, independently explaining 68.6% and 69.9% of the variation, respectively. Based on the NTI analysis, bacterial community assembly was more phylogenetically clustered with increasing soil pH. Variable selection was the predominant process explaining bacterial community assembly when differences in soil pH were ≥ 0.83, whereas homogenizing dispersal dominated when differences in soil pH were < 0.83. However, there was no significant relationship between plant phylogenetic distance and βNTI. Main conclusions: Our findings provide strong evidence that soil pH predominantly determines bacterial distribution and mediates the relative impact of stochasticity and determinism in soil bacterial community assembly. This suggests that climate-change associated forest soil acidification could have a dramatic impact on soil bacterial diversity, composition, and function.

opencc-zeroAug 2022View details →

opencc-by-4.0Sep 2022View details →

Processed receiver function data, dispersion measurement, and shear velocity model (Dharwar)

Processed receiver function data at 1 sample per second, dispersion measurement, and shear velocity model (Dharwar).

opencc-by-4.0Sep 2022View details →

Boundary Data set : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features

Boundary data set used to evaluate the continuity predictions for different models in boundary zones. It is composed of labelled and unlabelled pixels for a boundary size of 100m and 200m. For further details see section VI-A-1 of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>. To compute the predictions in the boundary zones with different models (GP, RF, MLP, LTAE), the code is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_gp">open source repository</a>.

Processed data for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"

This is the data used to reproduce the results from "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".

Scatter plots for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"

This file contains the test-score-vs-metric plots generated by the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".

Generalization metrics for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"

This file contains all the generalization metrics that can be used to reproduce the results of "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".

Rank correlation results for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"

This file contains the rank correlation results from the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".

opencc-by-4.0Apr 2024View details →

Oliveira et al. 2024 Processed Data

This is the collection of processed data for the manuscript "<a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=CAqdlvcAAAAJ&citation_for_view=CAqdlvcAAAAJ:0EnyYjriUFMC">Epigenetic heritability of cell plasticity drives cancer drug resistance through one-to-many genotype to phenotype mapping</a>", the accompanyng code is hosted on GitHub at this <a href="https://github.com/sottorivalab/epigenetic_heritability_and_cell_plasticity_reproducibility">link</a>, this data is sufficient to regenerate all the figures. There are 3 cohorts of organoids analyzed in the paper (more details in the manuscript): MSI, MSS batch 1 and MSS batch 2. All the subdirectories are divided accordingly  The directory is structured in 6 main branches: <ul> <li>archetype_params: this folder contains results of the archetypal analysis decomposition for RNA and ATAC of each cohort, they are either in tabular format or as MIDAA objects (more info <a href="https://github.com/sottorivalab/midaa">here</a>) </li> <li>archr_objects: this folder contains the zipped archR projects for each of the cohort, beware that archR projects have absolute paths, so you might have to change the internal links to make them work, I know the authors of archR are trying to put the possibilities of relative paths. In case you know of a better way to share archR objects please let me know.</li> <li>barcode_tables: this stores the barcode quantification tables for each cohort: in the MSI and MSS batch 2 case both floating and cellular barcodes are together in the same table (novaseq...etc), while for MSS batch 1 they are dividend into cell and floating tables</li> <li>copy_number_data: This folder contains the segmentation and the absolute copy number values obtained from lpWGS for each organoid line and treatment combination, and scDNA-seq results for MSI and MSS batch 2 for Parental and CENPE+MPS1 inh </li> <li>other_data: this folder contains various processed data.  <blockquote>congas_fit_final.rds: contains the final fit of <a href="https://github.com/caravagnalab/rcongas">CONGAS</a>  on MSS batch1 multiome medicc_input_congas_final_tree.new: this contains the tree inferred by <a href="https://bitbucket.org/schwarzlab/medicc2">MEDICC2</a> on CONGAS output palette_MSI.rds: this contains the color palette for MSI organoid palette_enriched_100.rds: this contains the color paletter for MSS organoid</blockquote> <blockquote>AKT_mutect2.ann.all_plugins.VEP.tsv: this contains the mutect calls for SNVs in the AKT Trametinib resistant vs Parental Population of MSS batch 1 deep WGS AZD_TRAM_vs_AKT_blood.cnvs.txt: this contains the ASCAT inferred CNV in the AKT Trametinib resistant vs Parental Population of MSS batch 1 deep WGS</blockquote> </li> <li>seurat_objects: This folder contains the seurat objects for scRNA-seq data</li> </ul>

opencc-by-4.0Apr 2024View details →

MD Simulation data, results and scripts for "Simulating the Skin Permeation Process of Ionizable Molecules"

Input and output data from MD simulations in the "Simulating the Skin Permeation Process of Ionizable Molecules" article. The data of the uncharged permeants were already published in Lundborg et al.  Skin permeability prediction with MD simulation sampling spatial and alchemical reaction coordinates. Biophys. J. 2022, 121, 3837-3849. Also includes scripts to rerun the analyses.