Find research datasets worth reusing

opencc-by-4.0Oct 2023View details →

Predicting p53-Dependent Cell Transitions From Thermodynamic Models

Matlab codes containing Grand Canonical Monte Carlo (GCMC) simulation codes and theoretical codes to study the first-order phase transition behavior of a malignant cell to a normal healthy cell. In this work, we develop thermodynamic models to determine the fate of a malignant cell as governed by the tumor suppressor p53 signaling network.

opencc-by-4.0Oct 2023View details →

Supporting data for Relative increases in CH4 and CO2 emissions from wetlands under global warming dependent on soil carbon substrates

Open the record for dataset details and reuse information.

opencc-by-4.0Jun 2023View details →

Serra et al., "Frequency dependence of ocean kinetic energy and respective changes over the period 1983-2018"

Data needed to reproduce the figures presented in the article "Frequency dependence of ocean kinetic energy and respective changes over the period 1983-2018" by Serra et al.

Parthenolide induces ROS-dependent cell death in human gastric cancer cells

Excel 1 - The original data-Parthenolide induces ROS-dependent cell death in human gastric cancer cellanalysis.Supplemental Table 1 -  Post-hoc Dunn's test for the effect of PN on the cell viability of MGC-803 cell.Supplemental Table 2 - Post-hoc Dunn's test for the effect of PN on the cell colonies of MGC-803 cells.Supplemental Table 3 - Post-hoc Dunn's test for the effect of PN and catalase on ROS Generation of MGC-803 cellsSupplemental Table 4 - Post-hoc Dunn's test for the effect of PN and catalase on cell viability of MGC-803 cellsExcel 2 - gene_sample_count from MGC-803 cells treated with DMSO compared with counterparts treated with PN

Dataset for Utilizing Pathway Dependency and Network Representation of Anticancer Drug Sensitivity for Drug Synergy Prediction

Open the record for dataset details and reuse information.

opennotspecifiedDec 2005View details →

Fig. 1 in The Morphology and Temperature-dependent Development of Mylabris phalerata Pallas (Coleoptera: Meloidae)

Fig. 1. Developmental stages of Mylabris phalerata. A: Egg (2.4 mm); B: L1 (2.6 mm); C: L2 (5.1 mm); D: L3 (13.7 mm); E: L4 (22.4 mm); F: L5 (29.2 mm); G: Pupa (30.1 mm); H: Adult (31.2 mm).

Fig. 2 in Assessing the Old-Growth Dependency of Two Saproxylic Beetle Species in the Southern Appalachian Mountains

Fig. 2. (A) Phellopsis obcordata, dorsal habitus. (B) Fomitopsis ochracea, newly recorded host. (C) Records of P. obcordata and Fomitopsis spp. within Great Smoky Mountains National Park with historic land uses. Outlined areas on the map indicate areas searched during this study in 2019 and 2021. All records outside and some records inside of the areas searched are from iNaturalist and previous literature.

Fig. 1 in Assessing the Old-Growth Dependency of Two Saproxylic Beetle Species in the Southern Appalachian Mountains

Fig. 1. Map of the southern Appalachian Mountains in the southeastern United States, showing collecting areas within National Forests and Great Smoky Mountains National Park (GSMNP). Inset shows the region within the United States.

Fig. 5 in Assessing the Old-Growth Dependency of Two Saproxylic Beetle Species in the Southern Appalachian Mountains

Fig. 5. Probability of occurrence within Great Smoky Mountains National Park of (A) Phellopsis obcordata based on distance from the nearest undisturbed forest (0 indicates inside undisturbed areas), and (B) Megalodacne heros based on elevation.

Fig. 4 in Assessing the Old-Growth Dependency of Two Saproxylic Beetle Species in the Southern Appalachian Mountains

Fig. 4. Boxplots of (A) elevation, (B) tree diameter, and (C) basal area among sampled historic disturbance classes in Great Smoky Mountains National Park.

Fig. 3 in Assessing the Old-Growth Dependency of Two Saproxylic Beetle Species in the Southern Appalachian Mountains

Fig. 3. (A) Megalodacne heros, dorsal habitus. (B) Ganoderma tsugae. (C) Records of M. heros and host Ganoderma spp. within Great Smoky Mountains National Park with historic land uses. Outlined areas on the map indicate areas searched during this study in 2019 and 2021.All records outside and some records inside of the areas searched are from iNaturalist.

Data and scripts used to produce the figures for state-dependent CO2 forcing paper

Data and scripts used to produce the figures for state-dependent CO2 forcing paper (He et al., 2023)

opencc-by-nc-sa-4.0Nov 2023View details →

RRR input and output files corresponding to "Global patterns in river water storage dependent on residence time"

Corresponding peer-reviewed publication This dataset corresponds to all the RRR input and output files that were used in the study reported in: <ul> <li>Collins, E. L., C. H. David, R. Riggs, G. H. Allen, T. M. Pavelsky, P. Lin, M. Pan, D. Yamazaki, R. K. Meentemeyer, and G. M. Sanchez (2024), Global patterns in river water storage dependent on residence time.</li> </ul> When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. Input datasets The dataset herein benefited from and was built upon other datasets: <ul> <li>MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7) available under a CC BY-NC-SA 4.0 license. <a href="https://www.reachhydro.org/home/params/merit-basins">https://www.reachhydro.org/home/params/merit-basins</a></li> <li>GLDAS VIC Land Surface Model L4 monthly 1.0 x 1.0 degree (V2.0) available under a CC0 license. <a href="https://science.nasa.gov/spd-41/">https://science.nasa.gov/spd-41/</a>. DOI: <a href="https://doi.org/10.5067/ZRIHVF29X43C">10.5067/ZRIHVF29X43C</a></li> <li>GLDAS Noah Land Surface Model L4 monthly 1.0 x 1.0 degree (V2.0) available under a CC0 license. <a href="https://science.nasa.gov/spd-41/">https://science.nasa.gov/spd-41/</a>. DOI: <a href="https://doi.org/10.5067/QN80TO7ZHFJZ">10.5067/QN80TO7ZHFJZ</a></li> <li>GLDAS Catchment Land Surface Model L4 monthly 1.0 x 1.0 degree (V2.0) available under a CC0 license. <a href="https://science.nasa.gov/spd-41/">https://science.nasa.gov/spd-41/</a>. DOI: <a href="https://doi.org/10.5067/SGSL3LNKGJWW">10.5067/SGSL3LNKGJWW</a></li> </ul> Known bugs and limitations in this dataset or the associated manuscript In the original files for MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7), as obtained from the pfaf_level_02 zip files, two river reaches do not have a corresponding catchment: COMID=31000001 and COMID=61000003. These two river reaches were removed from our analysis, hence bringing the total number of river reaches used from 2,938,143 to 2,938,141, the same value as the total number of catchments. In the original files for MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7), as obtained from the pfaf_level_02 zip files, the attribute table for the river reach with COMID=67072432 contains only four upstream reaches (COMID=64072433, COMID=64072735, COMID=64073772, and COMID=64073786), while the reach actually has five upstream reaches on the map (those above and COMID=64073793). This was modified herein because all connectivity in this dataset was created by associating upstream and downstream nodes rather than using the attribute table. <div> <div> <div> Our monthly dataset of river gauges with 95% daily data availability for 1980–2009 and with average discharge greater than or equal to 100 m3/s contains 1,148 stations. After mapping gauges to the MERIT Hydro river network using a joint criteria of 1) within a distance of 0.05 degrees and 2) uncorrected 30-yr average simulated discharge of the river reach within one order of magnitude relative to the observed average discharge of the gauge; the number of gauges reduces to 1129. In cases of multiple gauges per reach, we selected the gauge that had the closest observed average discharge to the uncorrected 30-yr averages simulated, resulting in 1,001 gauges. The dataset was further split in calibration gauges (702) and validation gauges (299). Three specific gauges are located in places with 0 accumulation of runoff in their specific sub-basin, they are located at river reaches with ID 41003992, 41001545, and 24005996. These three gauges were removed from the overall analysis at all gauges, resulting in 998 total gauges. All three gauges are part of the 299 validation gauges, not the 702 calibration gauges. They were retained in the analysis of 299 validation gauges.    </div> </div> </div> Files included in this version All monthly discharge observation files for 1148 gauges, in shapefile and CSV formats: <ul> <li>sites_1980-01_2009-12_100cms_095p.zip</li> <li>timeseries_obs_1980-01_2009-12_100cms_095p_monthly.zip</li> </ul> All CSV files corresponding to the hydrography of all pfaf_level_02 regions of MERIT-Hydro v0.7 Basins v1.0 (i.e. the 61 values of ii): <ul> <li>coords_pfaf_ii.zip</li> <li>kfac_pfaf_ii_1km_hour.zip</li> <li>k_pfaf_ii_low.zip</li> <li>k_pfaf_ii_nrm.zip</li> <li>k_pfaf_ii_hig.zip</li> <li>rapid_catchment_pfaf_ii.zip</li> <li>rapid_connect_pfaf_ii.zip</li> <li>rapid_coupling_pfaf_ii_GLDAS.zip</li> <li>riv_bas_id_pfaf_ii_topo.zip</li> <li>sort_pfaf_ii_topo.zip</li> <li>xfac_pfaf_ii_0.1.zip</li> <li>x_pfaf_ii_low.zip</li> <li>x_pfaf_ii_nrm.zip</li> <li>x_pfaf_ii_hig.zip</li> </ul> All netCDF4 files with monthly surface and subsurface runoff that were combined from GLDAS 2.0 and corresponding to the following land surface models for the years 1980 to 2009 (both included): <ul> <li>GLDAS_CLSM_M_1980-01_2009-12_utc.zip</li> <li>GLDAS_NOAH_M_1980-01_2009-12_utc.zip</li> <li>GLDAS_VIC_M_1980-01_2009-12_utc.zip</li> <li>GLDAS_ENS_M_1980-01_2009-12_utc.zip (the 3-model ensemble average based on the three above models).</li> </ul> All netCDF4 files with monthly lateral inflows (based on the GLDAS data above), corresponding to each one of the 61 values of ii, the pfaf_level_02 code: <ul> <li>m3_riv_pfaf_ii_GLDAS_CLSM_M_1980-01_2009-12_utc.zip</li> <li>m3_riv_pfaf_ii_GLDAS_NOAH_M_1980-01_2009-12_utc.zip</li> <li>m3_riv_pfaf_ii_GLDAS_VIC_M_1980-01_2009-12_utc.zip</li> <li>m3_riv_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc.zip</li> </ul> All netCDF4 files with monthly discharge (based on the m3_riv data above), corresponding to each one of the 61 values of ii, the pfaf_level_02 code:  <ul> <li>Qout_pfaf_ii_GLDAS_CLSM_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_ii_GLDAS_NOAH_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_ii_GLDAS_VIC_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc.zip</li> </ul> All netCDF4 files with monthly storage (based on the Qout_pfaf_ii_GLDAS_ENS data above), corresponding to each one of the 61 values of ii, the pfaf_level_02 code, and to each characteristic value of residence time (low, nrm, hig): <ul> <li>V_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc_low.zip</li> <li>V_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc_nrm.zip</li> <li>V_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc_hig.zip</li> </ul> All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) river network on which the 30-year mean of lumped discharge was appended (based on the Qout_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc.zip data above): <ul> <li>riv_pfaf_ii_MERIT_Hydro_v07_Basins_v01_GLDAS_ENS.zip</li> </ul> All monthly discharge observation files for 1148 gauges, in shapefile format, with observed monthly mean appended: <ul> <li>sites_1980-01_2009-12_100cms_095p_meanQ.zip</li> </ul> All monthly discharge observation files for 1001 gauges snapped on the MERIT Hydro (v0.7) Basins (v1.0) river network, in shapefile and CSV formats, corresponding to each one of the 61 values of ii, the pfaf_level_02 code:  <ul> <li>sites_1980-01_2009-12_100cms_095p_meanQ_COR_pfaf_ii.zip</li> <li>sites_1980-01_2009-12_100cms_095p_meanQ_COR.zip</li> <li>obs_tot_id_1980-01_2009-12_100cms_095p_meanQ_COR_pfaf_ii.zip</li> <li>Qobs_1980-01_2009-12_100cms_095p_meanQ_COR_pfaf_ii.zip</li> </ul> All monthly discharge observation files for 1001 gauges snapped on the MERIT Hydro (v0.7) Basins (v1.0) river network, separated into calibration (CAL) and validation (VAL) gauges, in shapefile and CSV formats, corresponding to each one of the 61 values of ii, the pfaf_level_02 code:  <ul> <li>sites_1980-01_2009-12_100cms_095p_meanQ_CAL.zip</li> <li>obs_bas_id_1980-01_2009-12_100cms_095p_meanQ_CAL_pfaf_ii.zip</li> <li>sites_1980-01_2009-12_100cms_095p_meanQ_VAL.zip</li> </ul> All netCDF4 files with monthly lateral inflows (based on the m3_riv_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc.zip data above) corrected using Long-Term Inverse Routing for calibration (CAL) gauges only and associated monthly discharge data, corresponding to each one of the 61 values of ii, the pfaf_level_02 code: <ul> <li>m3_riv_pfaf_ii_GLDAS_CAL_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_ii_GLDAS_CAL_M_1980-01_2009-12_utc.zip</li> </ul> All netCDF4 files with monthly lateral inflows (based on the m3_riv_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc.zip data above) corrected using Long-Term Inverse Routing for all correction (COR) gauges only and associated monthly discharge data, corresponding to each one of the 61 values of ii, the pfaf_level_02 code: <ul> <li>m3_riv_pfaf_ii_GLDAS_COR_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_ii_GLDAS_COR_M_1980-01_2009-12_utc.zip</li> </ul> All netCDF4 files with monthly storage (based on the Qout_pfaf_ii_GLDAS_COR_M_1980-01_2009-12_utc.zip data above), corresponding to each one of the 61 values of ii, the pfaf_level_02 code, and to each characteristic value of residence time (low, nrm, hig): <ul> <li>V_pfaf_ii_GLDAS_COR_M_1980-01_2009-12_utc_low.zip</li> <li>V_pfaf_ii_GLDAS_COR_M_1980-01_2009-12_utc_nrm.zip</li> <li>V_pfaf_ii_GLDAS_COR_M_1980-01_2009-12_utc_hig.zip</li> </ul> All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) river network on which the 30-year mean of lumped discharge was appended (based on the Qout_pfaf_ii_GLDAS_COR_M_1980-01_2009-12_utc.zip data above): <ul> <li>riv_pfaf_ii_MERIT_Hydro_v07_Basins_v01_GLDAS_COR.zip</li> </ul> All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) catchments that have been dissolved and of which the perimeter was extracted, corresponding to each one of the 61 values of ii, the pfaf_level_02 code; as well as global combined files: <ul> <li>cat_pfaf_ii_MERIT_Hydro_v07_Basins_v01_disso.zip</li> <li>cat_ MERIT_Hydro_v07_Basins_v01_disso.zip</li> <li>cat_pfaf_ii_MERIT_Hydro_v07_Basins_v01_perim.zip</li> <li>cat_ MERIT_Hydro_v07_Basins_v01_perim.zip</li> </ul> All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) river network retaining only those reaches that flow to the global coast, corresponding to each one of the 61 values of ii, the pfaf_level_02 code: <ul> <li>riv_pfaf_ii_MERIT_Hydro_v07_Basins_v01_coast.zip</li> </ul>

Code to run the analyses of "Forest storm resilience depends on the interplay between functional composition and climate - insights from European-scale simulations" by Barrere et al. (2024).

Repository containing the code to run the model and statistical analyses of the paper "Forest storm resilience depends on the interplay between functional composition and climate - insights from European-scale simulations" by Julien Barrere, Björn Reineking, Maxime Jeaunatre and Georges Kunstler, accepted by Functional Ecology in 2024. The code requires prior installation of the <a href="https://github.com/gowachin/matreex">matreex</a> R package, developped by Maxime Jeaunatre (INRAE), and of the ```targets``` package. The data folder, required to run the code, can be made available upon request to julienbarrere3@gmail.com Once the packages are installed and the data folder is placer in the main folder, just run ```targets::tar_make()``` from R and the script will download the other packages required and run the analyses. A version of this code is also available in the github <a href="https://github.com/jbarrere3/FunDiv_ipm">repository</a>

opencc-by-4.0Jul 2023View details →

Disordered regions in proteusin peptides guide post-translational modification by a flavin-dependent RiPP brominase

MD simulations for MprE7 alone and MprE7 in complex with SprI

Vibrational Probe at the Electrochemical Interface: Dependence on Plasmon Coupling and Potential on the Lineshape in Two-Dimensional Infrared Spectroscopy

These files contain the data presented in the research article:  Vibrational Probe at the Electrochemical Interface: Dependence on Plasmon Coupling and Potential on the Lineshape in Two-Dimensional Infrared Spectroscopy by Melissa Bodine, Vepa Rozyyev, Jeffrey W. Elam, Andrei Tokmakoff and Nicholas H. C. Lewis J. Phys. Chem. Lett., (2023)

Fig. 4. 2B in High and lowland dependent wing phenotypic variation of the dark blue tiger butterfly, Tirumala septentrionis (Butler, 1874) (Lepidoptera: Nymphalidae) with FE-SEM wing scales nanomorphology

Fig. 4. 2B-PLS analysis of T. septentrionis. (A) Forewing size; (B) Forewing shape; (C) Hindwing size; and (D) Hindwing shape.

Fig. 7 in High and lowland dependent wing phenotypic variation of the dark blue tiger butterfly, Tirumala septentrionis (Butler, 1874) (Lepidoptera: Nymphalidae) with FE-SEM wing scales nanomorphology

Fig. 7. Forewing shape analysis. Mlp female (A) % of variance, (B) PC1, (C) PC2, (D) PC3. Mlp male (E) % of variance, (F) PC1, (G) PC2, (H) PC3. Wyn female (I) % of variance, (J) PC1, (K) PC2, (L) PC3. Wyn male (M) % of variance, (N) PC1, (O) PC2, (P) PC3.

Fig. 10 in High and lowland dependent wing phenotypic variation of the dark blue tiger butterfly, Tirumala septentrionis (Butler, 1874) (Lepidoptera: Nymphalidae) with FE-SEM wing scales nanomorphology

Fig. 10. FE-SEM image analysis of T. septentrionis. Forewing brown regions (A–F), and bluish-white regions (G–J) of T. septentrionis. Hindwing brown regions (K–Q), and bluish-white regions (R–T). FE-SEM image of the male pouch scale of T. septentrionis (U–X). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 1. T in High and lowland dependent wing phenotypic variation of the dark blue tiger butterfly, Tirumala septentrionis (Butler, 1874) (Lepidoptera: Nymphalidae) with FE-SEM wing scales nanomorphology

Fig. 1. T. septentrionis forewing (A) with 25 landmarks; hindwing (B) with 18 landmarks. male pouch represented in white circle.