Summary:

The data files are in NASA Ames Format.

A full description of the data set has been published in the journal
Earth System Science Data at http://www.earth-syst-sci-data.net/5/385.

 

Summary:

Matlab data for both temperature and oxygen time series between December 1, 2014, 0:00 (EST) and April 26, 2015, 0:00 (EST). Temperature profiles are sampled every 20 seconds. Dissolved oxygen are sampled every 30 minutes. The depth of each time series are written on the file name.

Summary: Communities-at-sea are peer-groups of vessels which share a gear type and are associated with a particular port (e.g., vessels from New Bedford, MA that use gillnets). For vessels using trawl gear, small and large trawlers are considered separate communities according to vessel length (<> 65 feet). We used Vessel Trip Report (VTR) data for commercial fishing trips from 1996 to 2014, as reported by vessel captains, to determine the at-sea "servicesheds" or customary fishing grounds of communities. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/765477

Summary:

The COVID-19 pandemic has disrupted the world. The virus will not be the last pandemic to wreak havoc on humanity if we continue to ignore links between infectious diseases and the destruction of the natural world. Global health and economies are both at serious risk without proper containment and mitigation measures in each country. Moreover, panic and xenophobia are already on the rise, both of which are being intensified by misinformation and “fake news”. In order to mitigate the transmission of the virus and to intervene in the course of this pandemic, the world needs to take rapid, synchronised international action. It is crucial that governments consider the best science available to make informed decisions that are internationally coordinated and supported by local evidence. The Global Young Academy (GYA) comprises 200 members and 258 alumni, all excellent and socially-committed young researchers from 86 countries with multidisciplinary expertise, and is connected to more than 40 National Young Academies worldwide. These young academies are well placed to bridge the gap between international science and policymakers, as well as to disseminate and translate knowledge to society. This GYA Statement delivers specific recommendations for governments, the public, and young researchers.

Summary:

A global issue atlas of climate change adaptation

This project presents the results of the EU research project EMAPS, as well as its process: an experiment to use computation and visualization to harness the increasing availability of digital data and mobilize it for public debate. To do so, EMAPS gathered a team of social and data scientists, climate experts and information designers. It also reached out beyond the walls of Academia and engaged with the actors of the climate debate.

Summary: This dataset describes changes in groundfish fishing communities in the northeast US from 1997-2014 as captured in the vessel trip report (VTR) data collected by the National Oceanic and Atmospheric Administration National Marine Fisheries Service Northeast Fisheries Science Center (NOAA-NMFS-NEFSC). For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/752624

Summary: Total catches and estimated revenue by species for communities-at-sea based on landings reported on Vessel Trip Reports (VTRs). Landings data were compiled from VTRs and summed over the available years of data for each community. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/765560

Summary: These data are from a set of 2 experiments quantifying the ingestion by copepods of marine snow with different food distributions (layer vs. homogenous). Experiments were conducted in July 2018 in the Prairie research lab at the University of San Diego, California, USA. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/860674

Summary: Seawater was sampled from Niskin bottles associated with CTD casts on each cruise. Water for these dissolved gas isotope samples was the first to be sampled from a given Niskin bottle. When possible, bottles from the same cast were sampled, but depth profiles often came from separate casts at the same site. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/753594

Summary: Projected changes in habitat suitability for 33 marine species on the Northeast US shelf. Changes in habitat suitability are calculated based on species distribution models fit to bottom trawl survey data from the NOAA Northeast Fisheries Science Center. Positive values indicate an increase in habitat suitability by 2040-2050 relative to historical (1963-2005). The spatial resolution of projections is 0.25 x 0.25 degrees. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/765386

Summary:

This is the dataset (ver. 2017.02.13) for the manuscript "Soil fluxes of carbonyl sulfide (COS), carbon monoxide, and carbon dioxide in a boreal forest in southern Finland" submitted to the journal Atmospheric Chemistry and Physics.

Summary:

splitvent: A model for Single Ventilator/Dual Patient Ventilation

This repository is the work corresponding to the paper titled: A Simulated Single Ventilator/Dual Patient Ventilation Strategy for Acute Respiratory Distress Syndrome during the COVID-19 Pandemic.

Requirements. This repository includes two Simscape (Simulink 4.8) models and several Matlab 2020a functions and scripts, therefore the Simulink models will not run on any lower version of Matlab.

Disclaimer

The study done through this code does not encourage, endorse or support the use of a single ventilator to support two patients, but to quantify the potential risks and provide a quantitative test of a potential solution. We recognise the adoption of one ventilator to support two patients as a last resort requires several key developments still.

Summary:

Each file contains data for phytochemical combination experiments with one of the Crithidia bombi strains referenced in the paper:

IL13.2, VT1, C1.1, or S08

Conc.E: Eugenol concentration (ppm)

Conc.T: Thymol concentration (ppm)

max_rate: Maximum growth rate estimated from model-free spline using grofit

integral: Growth integral estimated from model-free spline

lag: Lag to onset of log phase, estimated from model-free spline     

maximum_predictedOD: Maximum OD, estimated from model-free spline

Summary:

X-ray reflectivity data (reflectivity vs Q) for the oxidation of insoluble organic material at the air-water interface. The organic material was extracted from atmospheric aerosol and sea water samples. The organic material was reacted with gas-phase ozone and aqueous phase hydroxyl radicals. The reflectivity data was collected at the Diamond Light source on I07 in May 2013 (SI8744) and April 2014 (SI9632) funded by STFC and NERC. The data supports a publication in Atmospheric Environment entitled “Are organic films from atmospheric aerosol and sea water inert to oxidation by ozone at the air-water interface?"

Summary:

This dataset arises from the READ project (Horizon 2020).

The dataset consists of a subset of documents from the Ratsprotokolle collection composed of minutes of the council meetings held from 1470 to 1805 (about 30.000 pages), which will be used in the READ project. This dataset is written in Early Modern German. The number of writers is unknown. Handwriting in this collection is complex enough to challenge the HTR software.

The training dataset is composed of 400 pages; most of the pages consist of a single block with many difficulties for line detection and extraction. The ground-truth in this set is in PAGE format and it is provided annotated at line level in the PAGE files.

Summary:

Background The newly identified coronavirus known as 2019-nCoV has posed a serious global health threat. According to the latest report (18-February-2020), it has infected more than 72,000 people globally and led to deaths of more than 1,016 people in China.

Methods The 2019 novel coronavirus proteome was aligned to a curated database of viral immunogenic peptides. The immunogenicity of detected peptides and their binding potential to HLA alleles was predicted by immunogenicity predictive models and NetMHCpan 4.0.

Results We report in silico identification of a comprehensive list of immunogenic peptides that can be used as potential targets for 2019 novel coronavirus (2019-nCoV) vaccine development. First, we found 28 nCoV peptides identical to Severe acute respiratory syndrome-related coronavirus (SARS CoV) that have previously been characterized immunogenic by T cell assays. Second, we identified 48 nCoV peptides having a high degree of similarity with immunogenic peptides deposited in The Immune Epitope Database (IEDB). Lastly, we conducted a de novo search of 2019-nCoV 9-mer peptides that i) bind to common HLA alleles in Chinese and European population and ii) have T Cell Receptor (TCR) recognition potential by positional weight matrices and a recently developed immunogenicity algorithm, iPred, and identified in total 63 peptides with a high immunogenicity potential.

Conclusions Given the limited time and resources to develop vaccine and treatments for 2019-nCoV, our work provides a shortlist of candidates for experimental validation and thus can accelerate development pipeline.

Summary:

Background The newly identified coronavirus known as 2019-nCoV has posed a serious global health threat. According to the latest report (18-February-2020), it has infected more than 72,000 people globally and led to deaths of more than 1,016 people in China.

Methods The 2019 novel coronavirus proteome was aligned to a curated database of viral immunogenic peptides. The immunogenicity of detected peptides and their binding potential to HLA alleles was predicted by immunogenicity predictive models and NetMHCpan 4.0.

Results We report in silico identification of a comprehensive list of immunogenic peptides that can be used as potential targets for 2019 novel coronavirus (2019-nCoV) vaccine development. First, we found 28 nCoV peptides identical to Severe acute respiratory syndrome-related coronavirus (SARS CoV) that have previously been characterized immunogenic by T cell assays. Second, we identified 48 nCoV peptides having a high degree of similarity with immunogenic peptides deposited in The Immune Epitope Database (IEDB). Lastly, we conducted a de novo search of 2019-nCoV 9-mer peptides that i) bind to common HLA alleles in Chinese and European population and ii) have T Cell Receptor (TCR) recognition potential by positional weight matrices and a recently developed immunogenicity algorithm, iPred, and identified in total 63 peptides with a high immunogenicity potential.

Conclusions Given the limited time and resources to develop vaccine and treatments for 2019-nCoV, our work provides a shortlist of candidates for experimental validation and thus can accelerate development pipeline.

Summary:

Calculating free energy differences between levels of theory (i.e., \(\Delta A^{low \to high}\)) is integral to performing indirect (S)QM/MM free energy simulations. However, connecting levels of theory via free energy simulations has proved difficult due to (1) bond/angle degrees of freedom, (2) dihedral degrees of freedom, and (3) solvent arrangement differences between levels of theory, largely due to partial charge differences between levels of theory. In order to improve calculation of (S)QM/MM free energy simulations, the free energy simulation community should begin to compare methods based on convergence success relative to overall computational time and resource requirements. We have begun to compile such a dataset by calculating \(\Delta A^{MM \to SCC-DFTB}\) in gas phase for 22 drug-like molecules, as seen in our recent publication, Kearns, et al. 2018, Molecules, Submitted, and we hope that future practitioners will do the same. With this work we hope to provide a standard for comparison for future FES methodologies; additionally, in the near future we hope to continue to add to this dataset including results in more complicated environments such as in solution and in enzyme. All data can be found in our publication and in the accompanying Supporting Information; raw data (such as simulation trajectories and raw data files) can be made available upon request. The purpose of this dataset publication is to make available all starting coordinates, topologies, parameter sets, and input files necessary to replicating the results published in our work.

Summary:

Corresponding peer-reviewed publication

This dataset corresponds to all the RAPID input and output files that were used in the study reported in:

• David, Cédric H., Florence Habets, David R. Maidment and Zong-Liang Yang (2011), RAPID applied to the SIM-France model, Hydrological Processes, 25(22), 3412-3425. DOI: 10.1002/hyp.8070. 

 

When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. 

 

Time format

The times reported in this description all follow the ISO 8601 format.  For example 2000-01-01T16:00-06:00 represents 4:00 PM (16:00) on Jan 1^st 2000 (2000-01-01), Central Standard Time (-06:00).  Additionally, when time ranges with inner time steps are reported, the first time corresponds to the beginning of the first time step, and the second time corresponds to the end of the last time step.  For example, the 3-hourly time range from 2000-01-01T03:00+00:00 to 2000-01-01T09:00+00:00 contains two 3-hourly time steps.  The first one starts at 3:00 AM and finishes at 6:00AM on Jan 1^st 2000, Universal Time; the second one starts at 6:00 AM and finishes at 9:00AM on Jan 1^st 2000, Universal Time.

 

Data sources

The following sources were used to produce files in this dataset:

• The hydrographic network of SIM-France, as published in Habets, F., A. Boone, J. L. Champeaux, P. Etchevers, L. Franchistéguy, E. Leblois, E. Ledoux, P. Le Moigne, E. Martin, S. Morel, J. Noilhan, P. Quintana Seguí, F. Rousset-Regimbeau, and P. Viennot (2008), The SAFRAN-ISBA-MODCOU hydrometeorological model applied over France, Journal of Geophysical Research: Atmospheres, 113(D6), DOI: 10.1029/2007JD008548.
• The observed flows are from Banque HYDRO, Service Central d’Hydrométéorologie et d’Appui à la Prévision des Inondations. Available at http://www.hydro.eaufrance.fr/index.php.
• Outputs from a simulation using SIM-France (Habets et al. 2008).  The simulation was run by Florence Habets, and produced 3-hourly time steps from 1995-08-01T00:00+02:00 to 2005-07-31T21:02+00:00.  Further details on the inputs and options used for this simulation are provided in David et al. (2011).

 

Software

The following software were used to produce files in this dataset:

• The Routing Application for Parallel computation of Discharge (RAPID, David et al. 2011, http://rapid-hub.org), Version 1.1.0.  Further details on the inputs and options used for this series of simulations are provided below and in David et al. (2011).
• ESRI ArcGIS (http://www.arcgis.com). 
• Microsoft Excel (https://products.office.com/en-us/excel). 
• The GNU Compiler Collection (https://gcc.gnu.org) and the Intel compilers (https://software.intel.com/en-us/intel-compilers). 

 

Study domain

The files in this dataset correspond to one study domain:

• The river network of SIM-France is made of 24264 river reaches.  The temporal range corresponding to this domain is from 1995-08-01T00:00+02:00 to 2005-07-31 T21:00+02:00.

 

Description of files

All files below were prepared by Cédric H. David, using the data sources and software mentioned above. 

• rapid_connect_France.csv.  This CSV file contains the river network connectivity information and is based on the unique IDs of the SIM-France river reaches (the IDs).  For each river reach, this file specifies: the ID of the reach, the ID of the unique downstream reach, the number of upstream reaches with a maximum of four reaches, and the IDs of all upstream reaches.  A value of zero is used in place of NoData.  The river reaches are sorted in increasing value of ID.  The values were computed based on the SIM-France FICVID file.  This file was prepared using a Fortran program.
• m3_riv_France_1995_2005_ksat_201101_c_zvol_ext.nc.  This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) from surface and subsurface runoff into the upstream point of each river reach. The river reaches have the same IDs and are sorted similarly to rapid_connect_France.csv.  The time range for this file is from 1995-08-01T00:00+02:00 to 2005/07/31T21:00+02:00.  The values were computed using the outputs of SIM-France.  This file was prepared using a Fortran program.
• kfac_modcou_1km_hour.csv.  This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches.  The river reaches have the same IDs and are sorted similarly to rapid_connect_France.csv.  The values were computed based on the following information: ID, size of the side of the grid cell, Equation (5) in David et al. (2011), and using a wave celerity of 1 km/h.  This file was prepared using a Fortran program.
• kfac_modcou_ttra_length.csv.  This CSV file contains a second guess of Muskingum k values (in seconds) for all river reaches.  The river reaches have the same IDs and are sorted similarly to rapid_connect_France.csv.  The values were computed based on the following information: ID, size of the side of the grid cell, travel time, and Equation (9) in David et al. (2011).

• k_modcou_0.csv.  This CSV file contains Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011).  This file was prepared using a Fortran program.

• k_modcou_1.csv.  This CSV file contains Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011).  This file was prepared using a Fortran program.
• k_modcou_2.csv.  This CSV file contains Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011).  This file was prepared using a Fortran program.
• k_modcou_3.csv.  This CSV file contains Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011).  This file was prepared using a Fortran program.
• k_modcou_4.csv.  This CSV file contains Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011).  This file was prepared using a Fortran program.
• k_modcou_a.csv.  This CSV file contains Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011).  This file was prepared using a Fortran program.
• k_modcou_b.csv.  This CSV file contains Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011).  This file was prepared using a Fortran program.
• k_modcou_c.csv.  This CSV file contains Muskingum k values (in seconds) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011).  This file was prepared using a Fortran program.
• x_modcou_0.csv.  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on Table (2) in David et al. (2011).  This file was prepared using a Fortran program.
• x_modcou_1.csv.  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on Table (2) in David et al. (2011).  This file was prepared using a Fortran program.
• x_modcou_2.csv.  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on Table (2) in David et al. (2011).  This file was prepared using a Fortran program.
• x_modcou_3.csv.  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on Table (2) in David et al. (2011).  This file was prepared using a Fortran program.
• x_modcou_4.csv.  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on Table (2) in David et al. (2011).  This file was prepared using a Fortran program.
• x_modcou_a.csv.  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on Table (2) in David et al. (2011).  This file was prepared using a Fortran program.
• x_modcou_b.csv.  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on Table (2) in David et al. (2011).  This file was prepared using a Fortran program.
• x_modcou_c.csv.  This CSV file contains Muskingum x values (dimensionless) for all river reaches.  The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv.  The values were computed based on Table (2) in David et al. (2011).  This file was prepared using a Fortran program.
• rivsurf_France.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the SIM-France domain.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_adour.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Adour River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_allier.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Allier River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_ardeche.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Ardeche River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_dordogne.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Dordogne River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_garonne.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Garonne River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_garonne_reste.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Garonne River Basin, downstream.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_garonneariege.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Garonne and Ariege River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_herault.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Herault River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_loir.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Loir River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_loire.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Loire River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_loire_amont_nevers.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Loire River Basin, upstream.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_loire_reste.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Loire River Basin, downstream.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_lot.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Lot River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_meuse.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Meuse River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_oise.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Oise River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_rhone.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Rhone River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_rhone_reste.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Rhone River Basin, downstream.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_rhone_suisse.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Rhone River Basin, upstream.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_saone.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Saone River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_seine.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Seine River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_seine_amont.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Seine River Basin, upstream.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_seine_reste.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Seine River Basin, downstream.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_tarn.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Tarn River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• rivsurf_vienne.csv.  This CSV file contains the list of unique IDs of SIM-France river reaches in the Vienne River Basin.  The river reaches are sorted from upstream to downstream.  The values were computed using the ID field.  This file was prepared using Excel.
• Qout_France_201101_c_zvol_ext_3653days_p1_dtR1800s.nc.  This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.  The river reaches have the same IDs and are sorted similarly to rivsurf_France.csv.  The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011).  This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.
• Qout_France_201101_c_zvol_ext_3653days_p2_dtR1800s.nc.  This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach.  The river reaches have the same IDs and are sorted similarly to rivsurf_France.csv.  The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011).  This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.
• Qout_France_201101_c_zvol_ext_3653days_p3_dtR1800s.nc.  This netCDF file cont

Summary:

Equilibrated POPC lipid bilayer simulation ran with Gromacs 4.5, Lipid14 force field (http://dx.doi.org/10.1021/ct4010307), 50ns, T=303K, 72 POPC molecules, 2234 water molecules. This data is ran for the nmrlipids.blospot.fi project. More details from nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi. If data is used, please cite nmrlipids.blogspot.fi project.