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opencc-by-4.0Apr 2022View details →

April 2019 Darshan counters from the Cori supercomputer

Copyright (c) 2022, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.   GENERAL INFORMATION   1. Title of Dataset: April 2019 Darshan counters from the Cori supercomputer   2. Author Information:       A. Principal Investigator Contact Information         - Name: Suren Byna         - Institution: Lawrence Berkeley National Laboratory         - Address: 1 Cyclotron Rd, Berkeley, CA 94720         - Email: sbyna@lbl.gov       B. Associate or Co-investigator Contact         - Name: Jean Luca Bez         - Institution: Lawrence Berkeley National Laboratory         - Address: 1 Cyclotron Rd, Berkeley, CA 94720         - Email: jlbez@lbl.gov   3. Date of data collection: 2019-04-01   4. Geographic location of data collection: Berkeley, California, USA   5. Information about funding sources that supported the collection of the data:       This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231.   SHARING/ACCESS INFORMATION   1. Licenses/restrictions placed on the data:       - Creative Commons - Attribution-NonCommercial-NoDerivatives 4.0 International (<a href="https://creativecommons.org/licenses/by-nc-nd/4.0">CC BY-NC-ND 4.0</a>)   2. Links to publications that cite or use the data:  N/A   3. Links to other publicly accessible locations of the data: N/A   4. Links/relationships to ancillary data sets: N/A   5. Was data derived from another source? YES       This dataset is derived from raw Darshan logs collected in Cori during April 2020.   6. Recommended citation for this dataset:       Jean Luca Bez, Ahmad Maroof Karimi, Arnab K. Paul, Bing Xie, Suren Byna, Philip Carns, Sarp Oral, Feiyi Wang, and Jesse Hanley. 2022. Access Patterns and Performance Behaviors of Multi-layer Supercomputer I/O Subsystems under Production Load. In Proceedings of the 31st International Symposium on High-Performance Parallel and Distributed Computing (HPDC '22). Association for Computing Machinery, New York, NY, USA.   DATA & FILE OVERVIEW   1. File List:       1. `darshan`: Dataset derived from raw Darshan logs collected in Cori during April 2020. Months are organized as directories with a `Darshan.csv` file in each day containing all the counters collected and relevant to our investigation.       2. `domains`: Dataset maps job identification to their primary associated science domain as reported by NERSC upon project registration. A single CSV comprises all jobs in April 2020 covered or not by Darshan.   2. Additional related data collected that was not included in the current data package:       - The original raw Darshan logs are not included in this package.   4. Are there multiple versions of the dataset?       - No, this is the first version of this dataset.   DESCRIPTIONS FOR VARIABLES USED IN THE FILES   1. Darshan counter dataset   * `uid`: User identifier that ran this job * `jobid`: Job identifier from the SLURM scheduler * `rank`: Rank identifier * `recordid`: File record identifier * `fstype`: File system type * `POSIX_OPENS`: Number of open calls in POSIX * `POSIX_FILENOS`: Number of POSIX fileno operations * `POSIX_DUPS`: Number of POSIX dup operations * `POSIX_READS`: Number of POSIX read operations * `POSIX_WRITES`: Number of POSIX write operations * `POSIX_SEEKS`: Number of POSIX seek operations * `POSIX_STATS`: Number of POSIX stat operations * `POSIX_MMAPS`: Number of POSIX mmap operations * `POSIX_FSYNCS`: Number of POSIX fsync operations * `POSIX_FDSYNCS`: Number of POSIX fdatasync operations * `POSIX_BYTES_READ`: Total number of bytes that were read from the file * `POSIX_BYTES_WRITTEN`: Total number of bytes written to the file * `POSIX_CONSEC_READS`: Number of consecutive reads (that were immediately adjacent to the previous access) * `POSIX_CONSEC_WRITES`: Number of consecutive writes (that were immediately adjacent to the previous access) * `POSIX_SEQ_READS`: Number of sequential reads (at a higher offset than where the previous access left off) * `POSIX_SEQ_WRITES`: Number of sequential writes (at a higher offset than where the previous access left off) * `POSIX_SIZE_READ_0_100`: Number of read access between 0 and 100 bytes at POSIX level * `POSIX_SIZE_READ_100_1K`: Number of read access between 100 bytes and 1KB at POSIX level * `POSIX_SIZE_READ_1K_10K`: Number of read access between 1KB and 10KB at POSIX level * `POSIX_SIZE_READ_10K_100K`: Number of read access between 10KB and 100KB at POSIX level * `POSIX_SIZE_READ_100K_1M`: Number of read access between 100KB and 1MB at POSIX level * `POSIX_SIZE_READ_1M_4M`: Number of read access between 1MB and 4MB at POSIX level * `POSIX_SIZE_READ_4M_10M`: Number of read access between 4MB and 10MB at POSIX level * `POSIX_SIZE_READ_10M_100M`: Number of read access between 10MB and 100MB at POSIX level * `POSIX_SIZE_READ_100M_1G`: Number of read access between 100MB and 1GB at POSIX level * `POSIX_SIZE_READ_1G_PLUS`: Number of read access greater than 1GB at POSIX level * `POSIX_SIZE_WRITE_0_100`: Number of write access between 0 and 100 bytes at POSIX level * `POSIX_SIZE_WRITE_100_1K`: Number of write access between 100 bytes and 1KB at POSIX level * `POSIX_SIZE_WRITE_1K_10K`: Number of write access between 1KB and 10KB at POSIX level * `POSIX_SIZE_WRITE_10K_100K`: Number of write access between 10KB and 100KB at POSIX level * `POSIX_SIZE_WRITE_100K_1M`: Number of write access between 100KB and 1MB at POSIX level * `POSIX_SIZE_WRITE_1M_4M`: Number of write access between 1MB and 4MB at POSIX level * `POSIX_SIZE_WRITE_4M_10M`: Number of write access between 4MB and 10MB at POSIX level * `POSIX_SIZE_WRITE_10M_100M`: Number of write access between 10MB and 100MB at POSIX level * `POSIX_SIZE_WRITE_100M_1G`: Number of write access between 100MB and 1GB at POSIX level * `POSIX_SIZE_WRITE_1G_PLUS`: Number of write access greater than 1GB at POSIX level * `POSIX_F_OPEN_START_TIMESTAMP`: Timestamp that the first POSIX file open operation began * `POSIX_F_READ_START_TIMESTAMP`: Timestamp that the first POSIX file read operation began * `POSIX_F_WRITE_START_TIMESTAMP`: Timestamp that the first POSIX file write operation began * `POSIX_F_CLOSE_START_TIMESTAMP`: Timestamp that the first POSIX file close operation began * `POSIX_F_OPEN_END_TIMESTAMP`: Timestamp that the last POSIX file open operation ended * `POSIX_F_READ_END_TIMESTAMP`: Timestamp that the last POSIX file read operation ended * `POSIX_F_WRITE_END_TIMESTAMP`: Timestamp that the last POSIX file write operation ended * `POSIX_F_CLOSE_END_TIMESTAMP`: Timestamp that the last POSIX file close operation ended * `POSIX_F_READ_TIME`: Cumulative time spent reading at the POSIX level * `POSIX_F_WRITE_TIME`: Cumulative time spent in write, fsync, and fdatasync at the POSIX level * `POSIX_F_META_TIME`: Cumulative time spent in open, close, stat, and seek at the POSIX level * `MPIIO_INDEP_OPENS`: Number of non-collective MPI opens * `MPIIO_COLL_OPENS`: Number of collective MPI opens * `MPIIO_INDEP_READS`: Number of non-collective MPI reads * `MPIIO_INDEP_WRITES`: Number of non-collective MPI writes * `MPIIO_COLL_READS`: Number of collective MPI reads * `MPIIO_COLL_WRITES`: Number of collective MPI writes * `MPIIO_SPLIT_READS`: Number of MPI split collective reads * `MPIIO_SPLIT_WRITES`: Number of MPI split collective writes * `MPIIO_NB_READS`: Number of MPI non-blocking reads * `MPIIO_NB_WRITES`: Number of MPI non-blocking writes * `MPIIO_SYNCS`: Number of MPI file syncs * `MPIIO_MODE`: MPI mode that the file was last opened in * `MPIIO_BYTES_READ`: Total number of bytes that were read from the file at MPI level * `MPIIO_BYTES_WRITTEN`: Total number of bytes written to the file at MPI level * `MPIIO_RW_SWITCHES`: Number of times that access toggled between read and write in consecutive MPI operations * `MPIIO_SIZE_READ_0_100`: Number of read access between 0 and 100 bytes at MPIIO level * `MPIIO_SIZE_READ_100_1K`: Number of read access between 100 bytes and 1KB at MPIIO level * `MPIIO_SIZE_READ_1K_10K`: Number of read access between 1KB and 10KB at MPIIO level * `MPIIO_SIZE_READ_10K_100K`: Number of read access between 10KB and 100KB at MPIIO level * `MPIIO_SIZE_READ_100K_1M`: Number of read access between 100KB and 1MB at MPIIO level * `MPIIO_SIZE_READ_1M_4M`: Number of read access between 1MB and 4MB at MPIIO level * `MPIIO_SIZE_READ_4M_10M`: Number of read access between 4MB and 10MB at MPIIO level * `MPIIO_SIZE_READ_10M_100M`: Number of read access between 10MB and 100MB at MPIIO level * `MPIIO_SIZE_READ_100M_1G`: Number of read access between 100MB and 1GB at MPIIO level * `MPIIO_SIZE_READ_1G_PLUS`: Number of read access greater than 1GB at MPIIO level * `MPIIO_SIZE_WRITE_0_100`: Number of write access between 0 and 100 bytes at MPIIO level * `MPIIO_SIZE_WRITE_100_1K`: Number of write access between 100 bytes and 1KB at MPIIO level * `MPIIO_SIZE_WRITE_1K_10K`: Number of write access between 1KB and 10KB at MPIIO level * `MPIIO_SIZE_WRITE_10K_100K`: Number of write access between 10KB and 100KB at MPIIO level * `MPIIO_SIZE_WRITE_100K_1M`: Number of write access between 100KB and 1MB at MPIIO level * `MPIIO_SIZE_WRITE_1M_4M`: Number of write access between 1MB and 4MB at MPIIO level * `MPIIO_SIZE_WRITE_4M_10M`: Number of write access between 4MB and 10MB at MPIIO level * `MPIIO_SIZE_WRITE_10M_100M`: Number of write access between 10MB and 100MB at MPIIO level * `MPIIO_SIZE_WRITE_100M_1G`: Number of write access between 100MB and 1GB at MPIIO level * `MPIIO_SIZE_WRITE_1G_PLUS`: Number of write access greater than 1GB at MPIIO level * `MPIIO_F_OPEN_START_TIMESTAMP`: Timestamp that the first MPIIO file open operation began * `MPIIO_F_READ_START_TIMESTAMP`: Timestamp that the first MPIIO file read operation began * `MPIIO_F_WRITE_START_TIMESTAMP`: Timestamp that the first MPIIO file write operation began * `MPIIO_F_CLOSE_START_TIMESTAMP`: Timestamp that the first MPIIO file close operation began * `MPIIO_F_OPEN_END_TIMESTAMP`: Timestamp that the last MPIIO file open operation ended * `MPIIO_F_READ_END_TIMESTAMP`: Timestamp that the last MPIIO file read operation ended * `MPIIO_F_WRITE_END_TIMESTAMP`: Timestamp that the last MPIIO file write operation ended * `MPIIO_F_CLOSE_END_TIMESTAMP`: Timestamp that the last MPIIO file close operation ended * `MPIIO_F_READ_TIME`: Cumulative time spent reading at MPI level * `MPIIO_F_WRITE_TIME`: Cumulative time spent write and sync at MPI level * `MPIIO_F_META_TIME`: Cumulative time spent in open and close at MPI level * `MPIIO_F_VARIANCE_RANK_TIME`: The population variance for MPI I/O time of all the ranks * `MPIIO_F_VARIANCE_RANK_BYTES`: The population variance for bytes transferred of all the ranks at MPI level * `STDIO_OPENS`: Number of stdio file open operations * `STDIO_FDOPENS`: Number of stdio fdopen operations * `STDIO_READS`: Number of stdio read operations * `STDIO_WRITES`: Number of stdio write operations * `STDIO_SEEKS`: Number of stdio seek operations * `STDIO_STATS`: Number of stdio stats operations * `STDIO_FLUSHES`: Number of stdio flush operations * `STDIO_BYTES_READ`: Total number of bytes read from the file using stdio operations * `STDIO_BYTES_WRITTEN`: Total number of bytes written to the file using stdio operations * `STDIO_META_TIME`: Cumulative time spent in stdio open/close/seek operations * `STDIO_READ_TIME`: Cumulative time spent in stdio read operations * `STDIO_WRITE_TIM`: Cumulative time spent in stdio write operations   2. Science domain dataset   * `job_id`: Unique job identifier automatically generated by SLURM upon submission * `date`: Date the job started to run as provided by SLURM * `science_domain`: Science domain of the job based on the information provided upon project allocation   RESOURCES   Helpful resources in understanding and utilizing this dataset:   1. Data collection methodology, understanding of Darshan logs and counters       - <a href="https://www.mcs.anl.gov/research/projects/darshan/documentation">Darshan</a>: Darshan HPC I/O Characterization Tool Documentation   2. General information about the NERSC Cori Supercomputer       - <a href="https://docs.nersc.gov/systems/cori">CORI</a>: General information about the Cori supercomputer     - <a href="https://docs.nersc.gov/tools/performance/darshan">Darshan</a>: Darshan I/O profiler documentation in Cori

opencc-by-4.0May 2022View details →

Variabel trilogi ketahanan pangan Kab Lebak dan Pandeglang 2019

Data ketersediaan, akses, dan pemanfaatan bahan pangan kecamatan-kecamatan di Kabupaten Lebak dan Pandeglang 2019.

opencc-by-nc-4.0May 2022View details →

Svalbox-DOM_2019-0023

Model description: Overview of the lower section of Janusfjellet, exposing outcrops and sequences from the Jurassic to the Cretaceous. Data availability: All uploaded archives are to be extracted to the same directory for a functional working environment, i.e., at the very least consisting of a project folder with the data and metashape subdirectories. The included processing report specifies the processing parameters that were used as input for the processing software (Agisoft Metashape, v1.7.2.12040). Exported model data use CRS epsg:32633. Show and visualise on <a href="http://svalbox.no/?page_id=4172">Svalbox.no</a>: Through Svalbox we aim to take geoscientific research and education to the next level – with the simple motto of placing all key information and data sets in their correct spatial position. We bring modern technology, in particular cost-effective digital outcrops, to complement more than 200 years of geoscientific research on Svalbard. Finally, Svalbox provides a sustainable platform for sharing results, workflows and teaching material with the wider geoscientific community. For more information, contact the Svalbox team at svalbox[at]gmail.com.

opencc-by-4.0Aug 2021View details →

A temporally consistent 8-day 0.05° gap-free snow cover extent dataset over the Northern Hemisphere for the period 1981–2019

Northern Hemisphere (NH) snow cover extent (SCE) is one of the most important indicator of climate change for its unique surface property. However, short temporal coverage, coarse spatial resolution, and different snow discrimination approach among published SCE products hampers its detailed studies. Using the Advanced Very High Resolution Radiometer Surface Reflectance (AVHRR-SR) Climate Data Record (CDR) and several ancillary datasets, this study generated a temporally consistent 8-day 0.05° gap-free NH terrestrial SCE product for the period 1981–2019 as part of the Global LAnd Surface Satellite dataset (GLASS) product suite. This process consistent of five steps. First, a decision tree algorithm with multiple threshold tests was applied to detect SCE from daily AVHRR-SR CDR. Second, we merge two existing daily SCE products to take advantage of their spatial coverage. Third, an aggregation process was used to detect the maximum SCE in each 8-day periods. Forth, the GLASS SCE was generated with the help of snow cover probability climatology. Fifth, the validation process was carried out to evaluate the quality of GLASS SCE. Validation results by using 562 Global Historical Climatology Network stations during 1981–2017 (r=0.61, p<0.05) and MOD10C2 during 2001–2019 (r=0.97, p<0.01) proved that the GLASS SCE product is credible in snow cover frequency monitoring. Moreover, cross-comparison between GLASS SCE and surface albedo during 1982–2018 further confirmed its values in climate changes studies. The GLASS SCE data set provides binary maps of snow cover for the Northern Hemisphere from September 1981 to the December 2019. The data are organized by year and provided in GeoTIFF formats. The gridcells were flagged as “0” if classified as "Non-snow", "1" if retrieved from AVHRR satellite observations, and "2" if  filled by IMS snow climatology. Spatial Coverage: N: 90, S: 0, E: 180, W: -180 Spatial Resolution: 0.05 deg x 0.05 deg Samples = 7200 Lines = 1800 Temporal Coverage: September 1981 to December 2019 Temporal Resolution: 8-day

opencc-by-4.0Jun 2022View details →

BeerDEcoded Nanopore Sequencing workshop of la trappe Dez. 2019

The BeerDEcoded workshops are a project of the Street Science Community. https://streetscience.community/projects/beerdecoded/ We are extracting DNA from different Beers and sequence them using the Nanopore sequencing technique. The data contains the reads extracted from the beer of the brand la trappe and was performed on 08.12.2019.   The sequencing was performed on a MinION using a Flow Cell and the MinKNOW software. The Software collects sequencing data in real-time and has a base-calling integrated to convert the fast5 files into fastq files.

opencc-by-4.0Jun 2022View details →

Sensing images of Chutou gully during 2019 to 2021

We release three sets of sensing images, which were obtained on 7  February 2019, 26 February 2020, and 12 March 2021, in Chutou gully, , Miansi Town, Wenchuan County, Sichuan Province, Southwestern China. These UAV images both can be viewed and edited in ENVI software.

opencc-by-4.0Mar 2022View details →

Historic data of the national electricity system transitions in Europe in 1990–2019 for retrospective evaluation of models [dataset]

This data package supports empirical analysis of national electricity system transitions and retrospective evaluation of electricity system models in 1990–2019 in 31 European countries, including the EU27, Switzerland, Iceland, Norway, and the United Kingdom. The data package covers two types of content. Firstly, we provide an annotated list of 528 original data sources and references relevant for retrospective electricity system modeling with emphasis on open-access sources. Secondly, we provide a total of 1359 processed and harmonized data files in a format that is suitable as inputs to electricity system models. Four types of data files are included for each country: (i) a country file documenting national demand and economic data, (ii) technology files describing techno-economic data for each major generation technology in the country's electricity mix, (iii) resource files describing fuel prices and CO2 emissions for each fuel, and (iv) load profiles describing 24-hour national load curves for each available year. We provide these data files as comma-separated files to enable their wider reuse for retrospective evaluation of models as well as for empirical analyses of the European electricity system transitions.

openother-openJun 2022View details →

epfl-mobots/plos_one_experiments: pone_bidirectional_2019

This is the release containing the data from the paper entitled "Bidirectional interactions facilitate the integration of a robot into a shoal of zebrafish Danio rerio". Cite the paper: <pre><code>@article{papaspyros2019bidirectional, title={Bidirectional interactions facilitate the integration of a robot into a shoal of zebrafish Danio rerio}, author={Papaspyros, Vaios and Bonnet, Frank and Collignon, Bertrand and Mondada, Francesco}, journal={PloS one}, volume={14}, number={8}, pages={e0220559}, year={2019}, publisher={Public Library of Science San Francisco, CA USA} } </code></pre>

opencc-by-4.0Jul 2022View details →

Data for Daudin, Frank & Rapoport « Can internal migration foster the convergence in regional fertility rates? Evidence from 19th century France », Economic Journal (2019), n°620, May, p.1618-1692.

Data for Guillaume Daudin, Raphaël Franck and Hillel Rapoport « Can internal migration foster the convergence in regional fertility rates? Evidence from 19th century France » avec Raphaël Franck et Hillel Rapoport, Economic Journal (2019), n°620, May, p.1618-1692. Downloading the data will provide you with a .zip file. To understand what is going on, please refer to the paper, its git depository and especially this file : https://github.com/gdaudin/migrations/blob/master/R%C3%A9capitulatif%20des%20do%20-%20Migration.pdf All the migration "matrices" are in Stata format (.dta). They are presented as a list of directed pairs of departments (x gender (variable sexe) and x year (variable annee)). "Matrice_complete_1901+1911.dta" are the migration matrices in the source. "Matrices_TRA_Recens.dta" are the main estimated migration matrices.

opencc-by-4.0Jul 2022View details →

Data used in a manuscript entitled "Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model" submitted to Geophysical Research Letters

This include a dataset used in a manuscript entitled “Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model” by Yamada and co-authors, which is submitted to Geophysical Research Letters. Contact: Yohei Yamada (yoheiy@jamstec.go.jp)

opencc-by-4.0Jul 2022View details →

FLEXPART–ERA-Interim simulations with 3 million parcels globally (1979–2019)

Description This data set contains FLEXPART simulations driven with the reanalysis ERA-Interim from 1 February 1979 to 30 August 2019. The simulations cover the entire globe (90°S to 90°N and 180°E to 179°W) with 3 million homogeneously distributed air parcels representing the entire mass in the atmosphere.  Software The following software versions were used:  <ul> <li>flex_extract v.7.1.2-dev (<a href="https://www.flexpart.eu/browser/flex_extract.git/?rev=3ca4c3ef0fa2a11ccf5a1c69e336b0893e2cb6a3">this version</a>); see Tipka et al. (2020) for details. </li> <li>FLEXPART v10.4 (<a href="https://www.flexpart.eu/browser/flexpart.git/?rev=3d7eebf7c4909f59db5ec32c524f88fb846e9fe5">this version</a>); see Pisso et al. (2019) for details. </li> </ul> Forcing data FLEXPART was driven with global reanalysis from ERA-Interim (Dee et al., 2011). The ERA-Interim reanalysis was downloaded and pre-processed using flex_extract v.7.1.2 (Tipka et al., 2020) using 6-hourly reanalyses at 00, 06, 12, and 18 UTC supplemented with 3- and 9-hourly forecasts (03 UTC and 09 UTC are taken from the forecasts initialised at 00UTC; and 15 UTC and 21 UTC are taken from the forecasts at 12 UTC). The settings are detailed in CONTROL_EI_1deg.global. The output of flex_extract were EIgYYYYMMDDHH files for each time step from 1 February 1979 to 30 August 2019 on a global 1° grid and contain the following variables: u, v, etadot, t, q, qc, sp, sshf, ewss, nsss, ssr, lsp, cp, sd, msl, tcc, 10u, 10v, 2t, 2d, z, lsm, sdor, cvl, cvh, sr; where the first 6 variables are available on 60 model layers.  Simulations Simulations with FLEXPART v10.4 were performed in forward mode and subdivided into annual chunks; i.e. simulations for each year were started on December 1st 00 UTC of the previous year and run through to January 5th 18 UTC of the following year in order to run all years in parallel. For example, simulations for the year 2000 were started on December 1st 1999 at 00 UTC and run through to January 5th 2001 18 UTC. The overlap in December guarantees a coherent evaluation of each year with trajectories expanding up to 30 days into the past.  Table 1 shows the main settings of the FLEXPART simulations as set in options/COMMAND and options/RELEASES. <table align="left"> <caption>Table 1: Main simulation settings (set in options/COMMAND; flags not listed here are set to 'off' [0] and in options/RELEASES; note that dates have been omitted for comprehension).</caption> <thead> <tr> <th scope="col">options/COMMAND</th> </tr> </thead> <tbody> <tr> <th scope="row">LDIRECT</th> <td>1</td> <td> Simulation direction in time; 1 (forward) </td> </tr> <tr> <th scope="row"> LOUTSTEP </th> <td>10800</td> <td>  Interval of model output (s) </td> </tr> <tr> <th scope="row"> LOUTSAMPLE </th> <td>900</td> <td> Interval of output sampling  (s), higher stat. accuracy with shorter intervals </td> </tr> <tr> <th scope="row"> LSYNCTIME </th> <td>900</td> <td>  All processes are synchronized to this time interval (s) </td> </tr> <tr> <th scope="row">CTL</th> <td>2</td> <td> CTL>1, ABL time step = (Lagrangian timescale (TL))/CTL, uses LSYNCTIME if CTL<0 </td> </tr> <tr> <th scope="row"> IFINE </th> <td>4</td> <td> Reduction for time step in vertical transport, used only if CTL>1 </td> </tr> <tr> <th scope="row">IOUT</th> <td>1</td> <td> Output type: [1]mass 2]pptv 3]1&2 4]plume 5]1&4, +8 for NetCDF output </td> </tr> <tr> <th scope="row"> IPOUT </th> <td>1</td> <td> Particle position output: 0] no 1] every output 2] only at end 3] time averaged </td> </tr> <tr> <th scope="row"> LSUBGRID </th> <td>1</td> <td> Increase of ABL heights due to sub-grid scale orographic variations; 1] on </td> </tr> <tr> <th scope="row"> LCONVECTION </th> <td>1</td> <td> Switch for convection parameterization; 1] on </td> </tr> <tr> <th scope="row"> MDOMAINFILL </th> <td>1</td> <td> Switch for domain-filling, if limited-area particles generated at boundary </td> </tr> <tr> <th scope="row">options/RELEASES</th> </tr> <tr> <th scope="row"> NSPEC </th> <td>1</td> <td> Total number of species </td> </tr> <tr> <th scope="row"> SPECNUM_REL </th> <td>1</td> <td> Species numbers in directory SPECIES </td> </tr> <tr> <th scope="row"> LON1 </th> <td>-179.00</td> <td> Left longitude of release box -180 < LON1 <180 </td> </tr> <tr> <th scope="row"> LON2 </th> <td>180.00</td> <td> Right longitude of release box, same as LON1 </td> </tr> <tr> <th scope="row">LAT1</th> <td>-90.00</td> <td> Lower latitude of release box, -90 < LAT1 < 90 </td> </tr> <tr> <th scope="row">LAT2</th> <td>90.00</td> <td> Upper latitude of release box same format as LAT1 </td> </tr> <tr> <th scope="row">Z1</th> <td>0.00</td> <td> Lower height of release box meters/hPa above reference level </td> </tr> <tr> <th scope="row">Z2</th> <td>10000.00</td> <td> Upper height of release box meters/hPa above reference level </td> </tr> <tr> <th scope="row">ZKIND</th> <td>1</td> <td> Reference level 1=above ground, 2=above sea level, 3 for pressure in hPa </td> </tr> <tr> <th scope="row"> PARTS </th> <td> 3000000 </td> <td> Total number of particles to be released </td> </tr> <tr> <th scope="row">COMMENT</th> <td> "globalRELEASE" </td> <td> Comment, written in the outputfile   </td> </tr> </tbody> </table>   Stored outputs Parcel positions and characteristics were stored at each 3-hourly time step in binary files names as partposit_YYYYMMDDHH0000. These files contain the following variables for each of the 3 million parcels: <ul> <li>parcel id (a unique parcel identity number)</li> <li>longitude</li> <li>latitude</li> <li>height</li> <li>time step</li> <li>orography</li> <li>potential vorticity</li> <li>specific humidity</li> <li>density</li> <li>height of the surrounding boundary layer</li> <li>height of the surrounding troposphere</li> <li>temperature</li> <li>mass.</li> </ul> One partposit_YYYYMMDDHH0000 file is 172 MB. All files for a month are compressed in monthly archives (as fp-eraint-global_v104_rYYYY_partposit_YYYYMM.tar.gz) and sum up to 393 GB for each annual simulation. The output of the entire simulation spanning 1979–2019 sums up to 17 TB.  Metadata  The metadata of each annual simulation is stored in a compressed fp-eraint-global_v104_rYYYY_meta.tar.gz archive. Metadata archives contain all namelists (all files from the options folder), invariant simulation outputs (such as header), and job outputs. For easy reference, the atmospheric mass at the time of initialisation is stored in a separate text file atm_mass.txt). The average atmospheric mass is 5.09205935e18 kg; each parcel thus represents approximately 1.697353e12 kg of air.  Post-processing The simulations were setup and their outputs were stored in a way so that post-processing with the Heat- And MoiSture Tracking framEwoRk (HAMSTER, Keune et al., 2022) is possible without further changes. HAMSTER constructs multi-day backward trajectories for a region of interest and facilitates the identification of, e.g., precipitation origins.  Contact and data access Only metadata is uploaded to this repository due to space constraints; access to the full data set can be obtained by contacting the corresponding author Jessica Keune via jessica.keune[at]ugent.be.  References Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, et al. (2011). The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656), 553-597. Keune, J., Schumacher, D. L., & Miralles, D. G. (2022). A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models. Geoscientific Model Development, 15(5), 1875-1898. Pisso, I., Sollum, E., Grythe, H., Kristiansen, N. I., Cassiani, M., Eckhardt, S., et al. (2019). The Lagrangian particle dispersion model FLEXPART version 10.4. Geoscientific Model Development, 12(12), 4955-4997. Tipka, A., Haimberger, L., & Seibert, P. (2020). Flex_extract v7. 1.2–a software package to retrieve and prepare ECMWF data for use in FLEXPART. Geoscientific Model Development, 13(11), 5277-5310.

Crystal size and texture data of sea ice from the Antarctic Marginal Ice Zone collected in Winter 2019

This dataset details the crystal size and texture data from the sea ice cores collected during the SCALE Winter 2019 voyage to the Antarctic Marginal Ice Zone. Each sample collected has a series of accompanying photograph figures from which the crystal size and texture are based.

Crystal size and texture data of sea ice from the Antarctic Marginal Ice Zone collected in Spring 2019

This dataset details the crystal size and texture data from the sea ice cores collected during the SCALE Spring 2019 voyage to the Antarctic Marginal Ice Zone. Each sample collected has a series of accompanying photograph figures from which the crystal size and texture are based.

Results of ultrasound measurements of sea-ice cores sampled during the Southern oCean seAsonal Experiment (SCALE) winter cruise in 2019

This dataset details the results of testing sea ice cores collected during the SCALE 2019 Winter Cruise using ultrasound techniques.

opencc-by-4.0Jun 2022View details →

BeerDEcoded Nanopore Sequencing Run of Chimay Now. 2019

The BeerDEcoded workshops are a project of the Street Science Community. https://streetscience.community/projects/beerdecoded/ We are extracting DNA from different Beers and sequence them using the Nanopore sequencing technique. The data contains the reads extracted from the beer of the brand Chimay and was performed on 26.11.2019.   The Sequencing was performed on a MinION using a Flow Cell and the MinKNOW software. The Software collects sequencing data in real-time and has a base-calling integrated to convert the fast5 files into fastq files.

Svalbox-DOM_2019-0021_Kampesteindalen

Model description: Digital outcrop model of the cliffs surrounding the stream leading into Kampesteindalen. Data availability: All uploaded archives are to be extracted to the same directory for a functional working environment, i.e., at the very least consisting of a project folder with the data and metashape subdirectories. The included processing report specifies the processing parameters that were used as input for the processing software (Agisoft Metashape, v1.7.2.12040). Exported model data use CRS epsg:32633. Show and visualise on <a href="http://svalbox.no/?page_id=4093">Svalbox.no</a>: Through Svalbox we aim to take geoscientific research and education to the next level – with the simple motto of placing all key information and data sets in their correct spatial position. We bring modern technology, in particular cost-effective digital outcrops, to complement more than 200 years of geoscientific research on Svalbard. Finally, Svalbox provides a sustainable platform for sharing results, workflows and teaching material with the wider geoscientific community. For more information, contact the Svalbox team at svalbox[at]gmail.com.

SuperDARN meteor wind data for January 2019

SuperDARN meteor wind data *.m.* - meridional *.z.*  - zonal X/Y are in radar coordinates - most users can disregard.    Supported by NSF #1934973 Collaborative Research: Super Dual Auroral Radar Network (SuperDARN) Operations, Research and Community Support     SuperDARN is an international collaboration operating high frequency (HF) radars deployed in the northern and southern hemispheres to measure ionospheric plasma circulation. Each partner institution secures funding and manages operations for their own facilities. The continued availability of SuperDARN data depends on the proper acknowledgment of data by its users. Guidelines for data acknowledgment are as follows: When data from an individual radar or radars are used, users must contact the principal investigator(s) of those radar(s) to obtain the appropriate acknowledgement information and to offer collaboration, where appropriate. Contact information is available in the README file for this collection. For all usage of SuperDARN data, users are asked to include the following standard acknowledgment text: “The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.” While SuperDARN has an open data use policy, i.e., prior permission to access and analyse the data is not required, the data user is strongly encouraged to establish early contact with any Principal Investigator whose data are involved in the project to discuss the intended usage and collaboration. Data can be subject to limitations that are not immediately evident to users. In addition, some data are embargoed for use by designated Principal Investigators for a period of one year. SuperDARN and the organizations that contributed data must be acknowledged in all reports and publications that use SuperDARN data. The SuperDARN Executive Council (see list in the README) must be notified before data are redistributed through another database. The data are not to be used for commercial purposes. If you have any questions about appropriate use of these data, contact any SuperDARN Principal Investigator.

Svalbox-DOM_2019-0015_Rudmosepynten

Model description: Virtual Outcrop Model of beach outcrop model at Rudmosepynten. The eastern section exhibits westward dipping carbonate benches of the Minkinfjellet Formation whereas the western part shows paleokarst breccias of the Minkinfjellet Formation. Transition from carbonate benches to paleokarst breccias is characterized as a sharp dip parallel boundary characteristic for the base of evaoprite paleokarst. Data availability: All uploaded archives are to be extracted to the same directory for a functional working environment, i.e., at the very least consisting of a project folder with the data and metashape subdirectories. The included processing report specifies the processing parameters that were used as input for the processing software (Agisoft Metashape, v1.7.1.11797). Exported model data use CRS epsg:32633. Show and visualise on <a href="http://svalbox.no/?page_id=3767">Svalbox.no</a>: Through Svalbox we aim to take geoscientific research and education to the next level – with the simple motto of placing all key information and data sets in their correct spatial position. We bring modern technology, in particular cost-effective digital outcrops, to complement more than 200 years of geoscientific research on Svalbard. Finally, Svalbox provides a sustainable platform for sharing results, workflows and teaching material with the wider geoscientific community. For more information, contact the Svalbox team at svalbox[at]gmail.com.

Svalbox-DOM_2019-0019_Criocerasdalen

Model description: Outcrop model of the faulted caprock shales of the Janusfjellet Subgroup in Criocerasdalen. The faulted, mudstone-dominated sequences form the caprock to the proposed Longyearbyen CO2 Lab reservoir. Obs: Images contain anomalous GPS/geospatial coordinates, with the resulting model being wrongly oriented. Data availability: All uploaded archives are to be extracted to the same directory for a functional working environment, i.e., at the very least consisting of a project folder with the data and metashape subdirectories. The included processing report specifies the processing parameters that were used as input for the processing software (Agisoft Metashape, v1.7.2.12040). Exported model data use CRS epsg:32633. Show and visualise on <a href="http://svalbox.no/?page_id=4087">Svalbox.no</a>: Through Svalbox we aim to take geoscientific research and education to the next level – with the simple motto of placing all key information and data sets in their correct spatial position. We bring modern technology, in particular cost-effective digital outcrops, to complement more than 200 years of geoscientific research on Svalbard. Finally, Svalbox provides a sustainable platform for sharing results, workflows and teaching material with the wider geoscientific community. For more information, contact the Svalbox team at svalbox[at]gmail.com.

Svalbox-DOM_2019-0020_Deltaneset

Model description: Digital outcrop model of the cliffs along the first beach section to the east of Deltaneset, i.e., in the direction of Diabasodden. These form the reservoir units for the proposed Longyearbyen CO2 Lab reservoir. Data availability: All uploaded archives are to be extracted to the same directory for a functional working environment, i.e., at the very least consisting of a project folder with the data and metashape subdirectories. The included processing report specifies the processing parameters that were used as input for the processing software (Agisoft Metashape, v1.7.2.12040). Exported model data use CRS epsg:32633. Show and visualise on <a href="http://svalbox.no/?page_id=4089">Svalbox.no</a>: Through Svalbox we aim to take geoscientific research and education to the next level – with the simple motto of placing all key information and data sets in their correct spatial position. We bring modern technology, in particular cost-effective digital outcrops, to complement more than 200 years of geoscientific research on Svalbard. Finally, Svalbox provides a sustainable platform for sharing results, workflows and teaching material with the wider geoscientific community. For more information, contact the Svalbox team at svalbox[at]gmail.com.