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278 results for “COUNTER”
Quality-checked horizontal particle flux data collected using a snow particle counter on board the R/V Akademik Tryoshnikov in the Southern Ocean during the austral summer of 2016/17 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Flux of particles (snow, rain and other particles including sea spray) were recorded passing through a photo-electric snow particle counter installed on board the R/V Akademik Tryoshnikov as part of the Antarctic Circumnavigation Expedition (ACE). Data were recorded from January to March 2017 in the Southern Ocean. Here we present the finalised, quality-checked, horizontal particle flux data where counts have been averaged over a one-minute period.</p> <p><strong>Dataset contents</strong></p> <ul> <li>SPC_HPF_windtrue_1min.csv, data file, comma-separated values</li> <li>SPC_HPF_windtrue_1min.png, metadata, portable network graphics</li> <li>SPC_HPF_windtrue_saveplot.py, script, Python code</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This quality-checked horizontal particle flux dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Intermediate processing stage of horizontal particle flux data collected using a snow particle counter on board the R/V Akademik Tryoshnikov in the Southern Ocean during the austral summer of 2016/17 as part of the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Flux of particles (snow, rain and other particles including sea spray) were recorded passing through a photo-electric snow particle counter installed on board the R/V Akademik Tryoshnikov as part of the Antarctic Circumnavigation Expedition (ACE). Data were recorded from January to March 2017 in the Southern Ocean. Here we present an intermediate step in data processing, with relative horizontal particle flux of particles with a size between 36 – 2000 μm averaged over one-minute periods. Data are presented in daily files.</p> <p><strong>Dataset contents</strong></p> <ul> <li>SPC_HPF_1min_YYYY_MM_DD.csv, data files, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This one-minute averaged horizontal particle flux dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Low-entropy Packed Binary Detection using Hardware Performance Counters
<p><span>Malware analysis faces a critical challenge in accurately identifying packed executables, especially those with low entropy. Existing software-based solutions often fail in detecting packers used by malware, resulting in inaccurate classifications. To address this shortcoming, in this study we introduce a novel method using<br>Hardware Performance Counters (HPCs) to facilitate the classification of binary packers due to HPCs’ minimal access overhead and ability to obviate the necessity for source code. We trained classic machine-learning models by selecting relevant hardware attributes associated with the unpacking procedure for detecting<br>packers used by low-entropy binary programs. Extensive experiments shows the substantial role played by Hardware Performance Counters in detecting binary packing characterized by low entropy,<br>offering a promising avenue for further exploration and refinement of techniques in malware analysis<br><br><br></span></p> <p><span>The following zip files are executables that represent low entropy versions of software packers using byte-padding. The name of the files are the names of the packers which are represened, Acprotect, Armadillo, Aspack, Nspack, Pecompact, Petite, UPX, and Zprotect. These can be used to measure the unpacking process using hardware performance counters in order to test & train machine earning classifiers for accurate classification of low entropy packers.</span></p>
Dataset and models of TMLR 2024 Paper "Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance Analysis"
<p>This is a part of dataset and models of the paper published in TMLR 2024 (Transactions on Machine Learning Research, <a href="https://jmlr.org/tmlr/" target="_blank" rel="noopener">https://jmlr.org/tmlr/</a>).</p> <p>Including training datasets, testing datasets, and models.</p> <p>The example program for using this file will be put on the author's github repo branch: <a href="https://github.com/DSobscure/cgi_drl_platform/tree/game_balance_measures_tmlr" target="_blank" rel="noopener">https://github.com/DSobscure/cgi_drl_platform/tree/game_balance_measures_tmlr</a></p> <p> </p>
The Role of Alkyl Chain Length and Halide Counter Ion in Layered Dion-Jacobson Perovskites with Aromatic Spacers
<p>Structural, optoelectronic and supplementary characterization data for “The Role of Alkyl Chain Length and Halide Counter Ion in Layered Dion-Jacobson Perovskites with Aromatic Spacers”, doi.org/10.1021/acs.jpclett.1c02937.</p> <ul> <li>Figure 2.zip: <ul> <li>2a and 2b (XRD patterns) in *.csv files.</li> <li>2c and 2d raw and processed solid state NMR spectra of (PDEA)PbI4 and (PDEA)I2, comprising 207Pb, 207Pb->1H HETCOR. NMR data is given in the JCAMP-DX 6.0 format including acquisition and processing parameters. The TopSpin software is recommended as this was used to record the NMR data.</li> <li>2e-h GIWAXS images in *.tiff format</li> </ul> </li> <li>Figure 3.zip: <ul> <li>3a and 3c (UV-Vis absorption spectra) in *.csv format</li> <li>3b and 3d (PL emission) in *.xlsx data</li> </ul> </li> <li>Figure 4.zip: <ul> <li>Transient absorption spectroscopy data in *.xlsx format</li> </ul> </li> <li>Figure 5.zip: <ul> <li>5a-f (XRD patterns) in *.csv data</li> </ul> </li> </ul> <ul> <li>Figure S2.zip: <ul> <li>S2a-b (UV-Vis spectra) in *.csv data</li> <li>S2c-d (XRD patterns) in *.csv data</li> </ul> </li> <li>Figure S3.zip: <ul> <li>GIWAXS images in *.tiff format</li> </ul> </li> <li>Figure S4.zip: <ul> <li>S4 Raw and processed solid state NMR spectra of 1H->13C cross polarisation spectra. NMR data is given in the JCAMP-DX 6.0 format including acquisition and processing parameters. The TopSpin software is recommended as this was used to record the NMR data.</li> </ul> </li> <li>Figure S5-S6.zip: <ul> <li>Raw and processed solution NMR spectra of (PDEA)I2 and (PDEA)Br2 in deuterated DMSO, comprising 1H and 13C 1D and 2D HSQC/HMBC spectra. NMR data is given in the JCAMP-DX 6.0 format including acquisition and processing parameters. The TopSpin software is recommended as this was used to record the NMR data.</li> </ul> </li> <li>Figure S7.zip: <ul> <li>PL emission data in *.xlsx format</li> </ul> </li> <li>Figure S8.zip: <ul> <li>Transient absorption spectroscopy data in *.xlsx format</li> </ul> </li> <li>Figure S9.zip: <ul> <li>S9a-f optical absorption data in *.csv format</li> </ul> </li> <li>Figure S10.zip: <ul> <li>PL emission data in *.xlsx format</li> </ul> </li> <li>Figure S11.zip: <ul> <li>*.png image of contact angle measurements</li> </ul> </li> </ul>
set of 100000 TERO counter values
<p>2 files (30_30_D.bin and 62_62_D.bin) = 2 examples of stream of random counter values generated by a TERO TRNG, manufactured in a 28 nanometer STM technology. Used in above mentionned paper in fig 3 and 6 left (for 62_62_D.bin) and fig 3 and 6 right (for 30_30_D.bin)</p>
Data from: Methodological artefacts cause counter-intuitive evolutionary conclusions in a simulation study
<p>In their simulation study, Garcia-Costoya et al. (2023) conclude that evolutionary constraints might aid populations facing climate change. However, we are concerned that this conclusion is largely a consequence of the simulated temperature variation being too small, and, most importantly, that uneven limitations to standing variation disadvantage unconstrained populations.</p>
ARCHIMED-φ simulation files for the simulation of Design A from the article "When architectural plasticity fails to counter the light competition imposed by planting design: an in silico approach using a functional-structural model of oil palm"; in silico Plants journal
<p>Input files for the simulation of Design A in ARCHIMED-φ from the article "When architectural plasticity fails to counter the light competition imposed by planting design: an in silico approach using a functional-structural model of oil palm"; in silico Plants journal.</p> <p>See https://archimed-platform.github.io/archimed-phys-user-doc/ for more details on the model.</p> <p>Make a simulation by opening a terminal at the root of the folder and type: `java -jar .\archimed-phys.jar .\DesignA_MockUpA_seed1_MAP_72.yml`.</p>
April 2019 Darshan counters from the Cori supercomputer
<p><em>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.</em></p> <p> </p> <p><strong>GENERAL INFORMATION</strong></p> <p> </p> <p>1. <em>Title of Dataset</em>: <strong>April 2019 Darshan counters from the Cori supercomputer</strong></p> <p> </p> <p>2. <em>Author Information</em>:</p> <p> </p> <p> A. Principal Investigator Contact Information</p> <p> - Name: Suren Byna</p> <p> - Institution: Lawrence Berkeley National Laboratory</p> <p> - Address: 1 Cyclotron Rd, Berkeley, CA 94720</p> <p> - Email: sbyna@lbl.gov</p> <p> </p> <p> B. Associate or Co-investigator Contact</p> <p> - Name: Jean Luca Bez</p> <p> - Institution: Lawrence Berkeley National Laboratory</p> <p> - Address: 1 Cyclotron Rd, Berkeley, CA 94720</p> <p> - Email: jlbez@lbl.gov</p> <p> </p> <p>3. <em>Date of data collection</em>: 2019-04-01</p> <p> </p> <p>4. <em>Geographic location of data collection</em>: Berkeley, California, USA</p> <p> </p> <p>5. <em>Information about funding sources that supported the collection of the data</em>:</p> <p> </p> <p> 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.</p> <p> </p> <p><strong>SHARING/ACCESS INFORMATION</strong></p> <p> </p> <p>1. <em>Licenses/restrictions placed on the data</em>:</p> <p> </p> <p> - 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>)</p> <p> </p> <p>2. <em>Links to publications that cite or use the data</em>: N/A</p> <p> </p> <p>3. <em>Links to other publicly accessible locations of the data</em>: N/A</p> <p> </p> <p>4. <em>Links/relationships to ancillary data sets</em>: N/A</p> <p> </p> <p>5. <em>Was data derived from another source?</em> YES</p> <p> </p> <p> This dataset is derived from raw Darshan logs collected in Cori during April 2020.</p> <p> </p> <p>6. <em>Recommended citation for this dataset</em>:</p> <p> </p> <p> 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.</p> <p> </p> <p><strong>DATA & FILE OVERVIEW</strong></p> <p> </p> <p>1. <em>File List</em>:</p> <p> </p> <p> 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.</p> <p> </p> <p> 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.</p> <p> </p> <p>2. <em>Additional related data collected that was not included in the current data package</em>:</p> <p> </p> <p> - The original raw Darshan logs are not included in this package.</p> <p> </p> <p>4. <em>Are there multiple versions of the dataset?</em></p> <p> </p> <p> - No, this is the first version of this dataset.</p> <p> </p> <p><strong>DESCRIPTIONS FOR VARIABLES USED IN THE FILES</strong></p> <p> </p> <p>1. <em>Darshan counter dataset</em></p> <p> </p> <p>* `uid`: User identifier that ran this job</p> <p>* `jobid`: Job identifier from the SLURM scheduler</p> <p>* `rank`: Rank identifier</p> <p>* `recordid`: File record identifier</p> <p>* `fstype`: File system type</p> <p>* `POSIX_OPENS`: Number of open calls in POSIX</p> <p>* `POSIX_FILENOS`: Number of POSIX fileno operations</p> <p>* `POSIX_DUPS`: Number of POSIX dup operations</p> <p>* `POSIX_READS`: Number of POSIX read operations</p> <p>* `POSIX_WRITES`: Number of POSIX write operations</p> <p>* `POSIX_SEEKS`: Number of POSIX seek operations</p> <p>* `POSIX_STATS`: Number of POSIX stat operations</p> <p>* `POSIX_MMAPS`: Number of POSIX mmap operations</p> <p>* `POSIX_FSYNCS`: Number of POSIX fsync operations</p> <p>* `POSIX_FDSYNCS`: Number of POSIX fdatasync operations</p> <p>* `POSIX_BYTES_READ`: Total number of bytes that were read from the file</p> <p>* `POSIX_BYTES_WRITTEN`: Total number of bytes written to the file</p> <p>* `POSIX_CONSEC_READS`: Number of consecutive reads (that were immediately adjacent to the previous access)</p> <p>* `POSIX_CONSEC_WRITES`: Number of consecutive writes (that were immediately adjacent to the previous access)</p> <p>* `POSIX_SEQ_READS`: Number of sequential reads (at a higher offset than where the previous access left off)</p> <p>* `POSIX_SEQ_WRITES`: Number of sequential writes (at a higher offset than where the previous access left off)</p> <p>* `POSIX_SIZE_READ_0_100`: Number of read access between 0 and 100 bytes at POSIX level</p> <p>* `POSIX_SIZE_READ_100_1K`: Number of read access between 100 bytes and 1KB at POSIX level</p> <p>* `POSIX_SIZE_READ_1K_10K`: Number of read access between 1KB and 10KB at POSIX level</p> <p>* `POSIX_SIZE_READ_10K_100K`: Number of read access between 10KB and 100KB at POSIX level</p> <p>* `POSIX_SIZE_READ_100K_1M`: Number of read access between 100KB and 1MB at POSIX level</p> <p>* `POSIX_SIZE_READ_1M_4M`: Number of read access between 1MB and 4MB at POSIX level</p> <p>* `POSIX_SIZE_READ_4M_10M`: Number of read access between 4MB and 10MB at POSIX level</p> <p>* `POSIX_SIZE_READ_10M_100M`: Number of read access between 10MB and 100MB at POSIX level</p> <p>* `POSIX_SIZE_READ_100M_1G`: Number of read access between 100MB and 1GB at POSIX level</p> <p>* `POSIX_SIZE_READ_1G_PLUS`: Number of read access greater than 1GB at POSIX level</p> <p>* `POSIX_SIZE_WRITE_0_100`: Number of write access between 0 and 100 bytes at POSIX level</p> <p>* `POSIX_SIZE_WRITE_100_1K`: Number of write access between 100 bytes and 1KB at POSIX level</p> <p>* `POSIX_SIZE_WRITE_1K_10K`: Number of write access between 1KB and 10KB at POSIX level</p> <p>* `POSIX_SIZE_WRITE_10K_100K`: Number of write access between 10KB and 100KB at POSIX level</p> <p>* `POSIX_SIZE_WRITE_100K_1M`: Number of write access between 100KB and 1MB at POSIX level</p> <p>* `POSIX_SIZE_WRITE_1M_4M`: Number of write access between 1MB and 4MB at POSIX level</p> <p>* `POSIX_SIZE_WRITE_4M_10M`: Number of write access between 4MB and 10MB at POSIX level</p> <p>* `POSIX_SIZE_WRITE_10M_100M`: Number of write access between 10MB and 100MB at POSIX level</p> <p>* `POSIX_SIZE_WRITE_100M_1G`: Number of write access between 100MB and 1GB at POSIX level</p> <p>* `POSIX_SIZE_WRITE_1G_PLUS`: Number of write access greater than 1GB at POSIX level</p> <p>* `POSIX_F_OPEN_START_TIMESTAMP`: Timestamp that the first POSIX file open operation began</p> <p>* `POSIX_F_READ_START_TIMESTAMP`: Timestamp that the first POSIX file read operation began</p> <p>* `POSIX_F_WRITE_START_TIMESTAMP`: Timestamp that the first POSIX file write operation began</p> <p>* `POSIX_F_CLOSE_START_TIMESTAMP`: Timestamp that the first POSIX file close operation began</p> <p>* `POSIX_F_OPEN_END_TIMESTAMP`: Timestamp that the last POSIX file open operation ended</p> <p>* `POSIX_F_READ_END_TIMESTAMP`: Timestamp that the last POSIX file read operation ended</p> <p>* `POSIX_F_WRITE_END_TIMESTAMP`: Timestamp that the last POSIX file write operation ended</p> <p>* `POSIX_F_CLOSE_END_TIMESTAMP`: Timestamp that the last POSIX file close operation ended</p> <p>* `POSIX_F_READ_TIME`: Cumulative time spent reading at the POSIX level</p> <p>* `POSIX_F_WRITE_TIME`: Cumulative time spent in write, fsync, and fdatasync at the POSIX level</p> <p>* `POSIX_F_META_TIME`: Cumulative time spent in open, close, stat, and seek at the POSIX level</p> <p>* `MPIIO_INDEP_OPENS`: Number of non-collective MPI opens</p> <p>* `MPIIO_COLL_OPENS`: Number of collective MPI opens</p> <p>* `MPIIO_INDEP_READS`: Number of non-collective MPI reads</p> <p>* `MPIIO_INDEP_WRITES`: Number of non-collective MPI writes</p> <p>* `MPIIO_COLL_READS`: Number of collective MPI reads</p> <p>* `MPIIO_COLL_WRITES`: Number of collective MPI writes</p> <p>* `MPIIO_SPLIT_READS`: Number of MPI split collective reads</p> <p>* `MPIIO_SPLIT_WRITES`: Number of MPI split collective writes</p> <p>* `MPIIO_NB_READS`: Number of MPI non-blocking reads</p> <p>* `MPIIO_NB_WRITES`: Number of MPI non-blocking writes</p> <p>* `MPIIO_SYNCS`: Number of MPI file syncs</p> <p>* `MPIIO_MODE`: MPI mode that the file was last opened in</p> <p>* `MPIIO_BYTES_READ`: Total number of bytes that were read from the file at MPI level</p> <p>* `MPIIO_BYTES_WRITTEN`: Total number of bytes written to the file at MPI level</p> <p>* `MPIIO_RW_SWITCHES`: Number of times that access toggled between read and write in consecutive MPI operations</p> <p>* `MPIIO_SIZE_READ_0_100`: Number of read access between 0 and 100 bytes at MPIIO level</p> <p>* `MPIIO_SIZE_READ_100_1K`: Number of read access between 100 bytes and 1KB at MPIIO level</p> <p>* `MPIIO_SIZE_READ_1K_10K`: Number of read access between 1KB and 10KB at MPIIO level</p> <p>* `MPIIO_SIZE_READ_10K_100K`: Number of read access between 10KB and 100KB at MPIIO level</p> <p>* `MPIIO_SIZE_READ_100K_1M`: Number of read access between 100KB and 1MB at MPIIO level</p> <p>* `MPIIO_SIZE_READ_1M_4M`: Number of read access between 1MB and 4MB at MPIIO level</p> <p>* `MPIIO_SIZE_READ_4M_10M`: Number of read access between 4MB and 10MB at MPIIO level</p> <p>* `MPIIO_SIZE_READ_10M_100M`: Number of read access between 10MB and 100MB at MPIIO level</p> <p>* `MPIIO_SIZE_READ_100M_1G`: Number of read access between 100MB and 1GB at MPIIO level</p> <p>* `MPIIO_SIZE_READ_1G_PLUS`: Number of read access greater than 1GB at MPIIO level</p> <p>* `MPIIO_SIZE_WRITE_0_100`: Number of write access between 0 and 100 bytes at MPIIO level</p> <p>* `MPIIO_SIZE_WRITE_100_1K`: Number of write access between 100 bytes and 1KB at MPIIO level</p> <p>* `MPIIO_SIZE_WRITE_1K_10K`: Number of write access between 1KB and 10KB at MPIIO level</p> <p>* `MPIIO_SIZE_WRITE_10K_100K`: Number of write access between 10KB and 100KB at MPIIO level</p> <p>* `MPIIO_SIZE_WRITE_100K_1M`: Number of write access between 100KB and 1MB at MPIIO level</p> <p>* `MPIIO_SIZE_WRITE_1M_4M`: Number of write access between 1MB and 4MB at MPIIO level</p> <p>* `MPIIO_SIZE_WRITE_4M_10M`: Number of write access between 4MB and 10MB at MPIIO level</p> <p>* `MPIIO_SIZE_WRITE_10M_100M`: Number of write access between 10MB and 100MB at MPIIO level</p> <p>* `MPIIO_SIZE_WRITE_100M_1G`: Number of write access between 100MB and 1GB at MPIIO level</p> <p>* `MPIIO_SIZE_WRITE_1G_PLUS`: Number of write access greater than 1GB at MPIIO level</p> <p>* `MPIIO_F_OPEN_START_TIMESTAMP`: Timestamp that the first MPIIO file open operation began</p> <p>* `MPIIO_F_READ_START_TIMESTAMP`: Timestamp that the first MPIIO file read operation began</p> <p>* `MPIIO_F_WRITE_START_TIMESTAMP`: Timestamp that the first MPIIO file write operation began</p> <p>* `MPIIO_F_CLOSE_START_TIMESTAMP`: Timestamp that the first MPIIO file close operation began</p> <p>* `MPIIO_F_OPEN_END_TIMESTAMP`: Timestamp that the last MPIIO file open operation ended</p> <p>* `MPIIO_F_READ_END_TIMESTAMP`: Timestamp that the last MPIIO file read operation ended</p> <p>* `MPIIO_F_WRITE_END_TIMESTAMP`: Timestamp that the last MPIIO file write operation ended</p> <p>* `MPIIO_F_CLOSE_END_TIMESTAMP`: Timestamp that the last MPIIO file close operation ended</p> <p>* `MPIIO_F_READ_TIME`: Cumulative time spent reading at MPI level</p> <p>* `MPIIO_F_WRITE_TIME`: Cumulative time spent write and sync at MPI level</p> <p>* `MPIIO_F_META_TIME`: Cumulative time spent in open and close at MPI level</p> <p>* `MPIIO_F_VARIANCE_RANK_TIME`: The population variance for MPI I/O time of all the ranks</p> <p>* `MPIIO_F_VARIANCE_RANK_BYTES`: The population variance for bytes transferred of all the ranks at MPI level</p> <p>* `STDIO_OPENS`: Number of stdio file open operations</p> <p>* `STDIO_FDOPENS`: Number of stdio fdopen operations</p> <p>* `STDIO_READS`: Number of stdio read operations</p> <p>* `STDIO_WRITES`: Number of stdio write operations</p> <p>* `STDIO_SEEKS`: Number of stdio seek operations</p> <p>* `STDIO_STATS`: Number of stdio stats operations</p> <p>* `STDIO_FLUSHES`: Number of stdio flush operations</p> <p>* `STDIO_BYTES_READ`: Total number of bytes read from the file using stdio operations</p> <p>* `STDIO_BYTES_WRITTEN`: Total number of bytes written to the file using stdio operations</p> <p>* `STDIO_META_TIME`: Cumulative time spent in stdio open/close/seek operations</p> <p>* `STDIO_READ_TIME`: Cumulative time spent in stdio read operations</p> <p>* `STDIO_WRITE_TIM`: Cumulative time spent in stdio write operations</p> <p> </p> <p>2. <em>Science domain dataset</em></p> <p> </p> <p>* `job_id`: Unique job identifier automatically generated by SLURM upon submission</p> <p>* `date`: Date the job started to run as provided by SLURM</p> <p>* `science_domain`: Science domain of the job based on the information provided upon project allocation</p> <p> </p> <p><strong>RESOURCES</strong></p> <p> </p> <p>Helpful resources in understanding and utilizing this dataset:</p> <p> </p> <p>1. <em>Data collection methodology, understanding of Darshan logs and counters</em></p> <p> </p> <p> - <a href="https://www.mcs.anl.gov/research/projects/darshan/documentation">Darshan</a>: Darshan HPC I/O Characterization Tool Documentation</p> <p> </p> <p>2. <em>General information about the NERSC Cori Supercomputer</em></p> <p> </p> <p> - <a href="https://docs.nersc.gov/systems/cori">CORI</a>: General information about the Cori supercomputer</p> <p> - <a href="https://docs.nersc.gov/tools/performance/darshan">Darshan</a>: Darshan I/O profiler documentation in Cori</p>
Pharmaceutical advertising and the consumption of over the counter (OTC) medicines in users of the Superfar drugstore in Barrios Altos - Cercado de Lima, 2022.
<p>To determine the relationship between pharmaceutical advertising and the consumption of over-the-counter (OTC) medicines in users of the SUPERFAR drugstore in barrios altos -cercado de lima, 2022.</p>
Dataset of Raman spectroscopy responses for over-the-counter drugs in Paraguay, including acetylsalicylic acid, paracetamol, and ibuprofen.
<p>Spectra of three over-the-counter pharmaceuticals—acetylsalicylic acid, paracetamol, and ibuprofen—were collected at the Faculty of Exact and Natural Sciences of the National University of Asuncion, with the aim of creating a dataset that serves as a reference for Raman responses from different drug manufacturers. This dataset will also provide the scientific community with data that can be used for multivariate analysis and model training.</p> <p>In the data collection phase, spectra were obtained using a Raman spectroscopy system (iRaman 785s model from BWTEK) equipped with a 785 nm excitation laser. Samples were collected from diverse sales points such as pharmacies, shopping centers, and street vendors. Each spectrum was captured at 50% laser power with a measurement time of 1 second and an accumulation of 10 spectra over a range of 150 to 3200 cm-1. This method preserved the integrity of the raw data, which includes a common column for Raman shifts and additional columns for intensities and labels, detailing the activation modes in the Raman spectrum.</p> <p>The data is structured into specific xlsx files for each drug, such as "Paracetamol.xlsx", "acetylsalicylic-acid .xlsx", and "Ibuprofen .xlsx", each containing 50 spectra categorized by the type of pharmaceutical but not by brand. Brand-specific categorization is detailed in separate files like "Paracetamol-trademark .xlsx", where samples are classified using codes such as "Par-A" for different brands. This organization aids the scientific community in using clustering methods to analyze the spectral data and differentiate pharmaceutical brands based on their excipients or binders, with consistent codes across different drugs suggesting common manufacturers for various medications.</p> <p> </p>
Localized Topological States beyond Fano Resonances via Counter-Propagating Wave Mode Conversion in Piezoelectric Microelectromechanical Devices
<p>Main numerical and experimental data (Matlab .fig files) of the paper <em>Localized Topological States beyond Fano Resonances via Counter-Propagating Wave Mode Conversion in Piezoelectric Microelectromechanical Devices.</em></p>
Dataset for "On the potential of the Cluster Ion Counter (CIC) to observe local new particle formation, condensation sink and growth rate of newly formed particles"
<p>Data for Kulmala et al. (2024 )"On the potential of the Cluster Ion Counter (CIC) to observe local new particle formation, condensation sink and growth rate of newly formed particles" (https://doi.org/10.5194/ar-2024-14).</p> <p>Included in the file are number concentrations of sub-2 nm ions and 2-2.3 nm ions measured with Cluster Ion Counter (CIC) and Neutral cluster and Air Ion Spectrometer (NAIS) at SMEAR II station in Hyytiälä, Finland. Concentrations of 1-2 nm ions measured with the NAIS are also included. Sub-2 nm (2-2.3 nm) ion concentrations measured with CIC are refered as Channel 1 (Channel 2-Channel 3) in the .csv file.</p> <p>Contact Santeri Tuovinen (santeri.tuovinen@helsinki.fi) for more details.</p>
Dataset for Personalizing Over-the-Counter Hearing Aids using Pairwise Comparisons
<p>This is the initial release of the dataset.</p>
Data from: Methodological artefacts cause counter-intuitive evolutionary conclusions in a simulation study
Open the record for dataset details and reuse information.
Data and input supporting: Evolutionary dynamics of counter-helical magnetic flux ropes
Open the record for dataset details and reuse information.
Measurement of ocular counter-roll using iris images during binocular fixation and head tilt
<p>All images and datas used in the study "Measurement of ocular counter-roll using iris images during binocular fixation and head tilt"</p>
RAD54L2 counters TOP2-DNA adducts to promote genome stability (Etoposide treated RPE1 CRISPR screens in TP53 and RAD53L2 knock outs)
<p>The catalytic cycle of topoisomerase 2 (TOP2) enzymes proceeds via a transient DNA double-strand break (DSB) intermediate termed the TOP2 cleavage complex (TOP2cc), in which the TOP2 protein is covalently bound to DNA. Anti-cancer agents such as etoposide operate by stabilising TOP2ccs, ultimately generating genotoxic TOP2-DNA protein crosslinks that require processing and repair. Here, we identify RAD54-like 2 (RAD54L2) as a factor promoting TOP2cc resolution. We demonstrate that RAD54L2 acts through a novel mechanism together with zinc finger protein associated with TDP2 and TOP2 (ZATT/ZNF451) and independent of tyrosyl-DNA phosphodiesterase 2 (TDP2). Our work suggests a model wherein RAD54L2 recognises sumoylated-TOP2 and, using its ATPase activity, promotes TOP2cc resolution and prevents DSB exposure. These findings suggest RAD54L2-mediated TOP2cc resolution as a potential mechanism for cancer-therapy resistance and highlight RAD54L2 as an attractive candidate for drug discovery.</p>
Synaptic vesicle glycoprotein 2C enhances vesicular storage of dopamine and counters dopaminergic toxicity
<p>Dopaminergic neurons of the substantia nigra exist in a persistent state of vulnerability resulting from high baseline oxidative stress, high energy demand, and broad unmyelinated axonal arborizations. Impairments in the storage of dopamine compound this stress due to cytosolic reactions that transform the vital neurotransmitter into an endogenous neurotoxicant, and this toxicity is thought to contribute to the dopamine neuron degeneration that occurs Parkinson's disease. We have previously identified synaptic vesicle glycoprotein 2C (SV2C) as a modifier of vesicular dopamine function, demonstrating that genetic ablation of SV2C in mice results in decreased dopamine content and evoked dopamine release in the striatum. Here, we adapted a previously published in vitro assay utilizing false fluorescent neurotransmitter 206 (FFN206) to visualize how SV2C regulates vesicular dopamine dynamics and identified that SV2C promotes the uptake and retention of FFN206 within vesicles. In addition, we present data indicating that SV2C enhances the retention of dopamine in the vesicular compartment with radiolabeled dopamine in vesicles isolated from immortalized cells and from mouse brain. Further, we demonstrate that SV2C enhances the ability of vesicles to store the neurotoxicant 1-methyl-4-phenylpyridinium (MPP+) and that genetic ablation of SV2C results in enhanced 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced vulnerability in mice. Together, these findings establish that SV2C functions to enhance storage of dopamine and toxicants and helps maintain the integrity of dopaminergic neurons.</p>
Restored saltmarshes have low beta diversity due to limited topographic variation, but this can be countered by management
<p>1. Spatial heterogeneity of species (beta diversity) is an important attribute of ecological communities, but is less frequently considered when assessing restoration success than other aspects of diversity (gamma and alpha). Differences in beta diversity between restored and natural sites may arise due to differences in environmental heterogeneity.</p> <p>2. We use a nested sampling design to survey plant communities and environmental conditions (elevation, redox potential and metrics of topography) on four pairs of restored and natural saltmarshes. We assess whether there are differences in both alpha and beta diversity between natural and restored sites, and analyse their environmental drivers.</p> <p>3. Topography was an important driver of plant alpha diversity and beta diversity. The effects of topography were partly indirect, mediated though changes in redox potential, but topography also influenced plant communities independently of both elevation and redox.</p> <p>4. Restored saltmarshes were less heterogeneous in topography than natural marshes. This reduced topography was reflected in lower beta diversity; plant communities 1 m apart in natural marshes were as dissimilar as those found 20 m apart in restored marshes.</p> <p>5. Large-scale topographic manipulation carried out at one site a decade after initial restoration successfully increased topographic heterogeneity and increased beta diversity when surveyed three years after manipulation. These changes were still evident when resurveyed after a further two years.</p> <p>6. Synthesis and applications. Increasing environmental heterogeneity can improve restoration outcomes by increasing beta diversity on restored sites. The effect of environmental heterogeneity is likely to be particularly strong within intertidal habitats where small changes in topography can determine whether a species can occur at a given location. Topographic manipulation is a feasible post-restoration technique that can be applied to ensure restored saltmarshes better meet policy targets of biological, physical and functional equivalence with natural marshes.</p> <p>17-Nov-2021</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.