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722
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ShareScore release 0.9.0
Dataset results
722 results for “IDS”
NOAA Monthly Mean Sea Level Summary Data for the Key West Water Level Station (NOAA/NOS Co-OPS ID 8724580), Florida, USA, January 1913 - ongoing
Monthly Mean Sea Level Summary Data for the Key West, Florida, Water Level Station (NOAA/NOS CO-OPS ID 8724580). Data is in meters relative to the STND-Key West Station Datum.
NOAA Daily Surface Meteorologic Data at NCDC Flamingo Ranger Station (ID-083020) (FCE), South Florida, USA, January 1951 - January 2021
The National Climatic Data Center's (NOAA) daily mean, maximum, and minimum air temperatures and daily precipitation collected at Flamingo Station (Coop ID- 083020). This site was terminated in January 2021.
NOAA Daily Surface Meteorologic Data at NCDC Miami International Airport Station (ID-085663), South Florida, USA, January 1948 - ongoing
The National Climatic Data Center's (NOAA) daily mean, maximum, and minimum air temperatures and daily precipitation collected at Miami International Airport Station (Coop ID- 085663).
NOAA Daily Surface Meteorologic Data at NCDC Royal Palm Ranger Station (ID-087760)(FCE LTER), South Florida, USA, May 1949 - ongoing
The National Climatic Data Center's (NOAA) daily mean, maximum, and minimum air temperatures and daily precipitation collected at Royal Palm Ranger Station (Coop ID- 087760).
SIRAH-CoV2 initiative: RNA binding domain of nucleocapsid phosphoprotein (PDB id:6VYO)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 RNA binding domain of the nucleocapsid phosphoprotein in its APO form with Zn ions bound (PDB id:6VYO, Bioassembly 1). Simulations were performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Zinc ions were parameterized as reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00160">Klein et al. 2020</a>.</p> <p>The file 6VYO_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com. Additionally, the file 6VYO_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6VYO_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6VYO_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6VYO_SIRAHcg_prot_10us_skip10ns.prmtop 6VYO_SIRAHcg_prot_10us_skip10ns.ncrst 6VYO_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Florencia Klein (fklein@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: Main Protease (PDB id:6LU7)
<p>This dataset contains the trajectory of a 15 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 Main protease in its APO form (PDB id: 6LU7, Bioassembly 1). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. </p> <p>The files 6LU7_SIRAHcg_rawdata1.tar, 6LU7_SIRAHcg_rawdata2.tar, and 6LU7_SIRAHcg_rawdata3.tar, contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6LU7_SIRAHcg_15us_prot.tar contains only the protein coordinates, while 6LU7_SIRAHcg_15us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6LU7_SIRAHcg_15us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6LU7_SIRAHcg_prot.prmtop 6LU7_SIRAHcg_prot.ncrst 6LU7_SIRAHcg_prot_15us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: Nucleocapsid protein N-terminal RNA binding domain (PDB id:6M3M)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 Nucleocapsid protein N-terminal RNA binding domain (PDB id:6M3M). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. </p> <p>The files 6M3M_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6M3M_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6M3M_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6M3M_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6W4B_SIRAHcg_prot.prmtop 6W4B_SIRAHcg_prot.ncrst 6W4B_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Florencia Klein (fklein@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: Papain-like Protease (PDB id:6W9C)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 Papain-like protease in its APO form with Zn ions bound (PDB id: 6W9C). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Zinc ions were parameterized as reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00160">Klein et al. 2020</a>.</p> <p>The files 6W9C_SIRAHcg_rawdata_0-5us.tar, 6W9C_SIRAHcg_rawdata_5-10us.tar, contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6W9C_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6W9C_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6W9C_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6W9C_SIRAHcg_prot.prmtop 6W9C_SIRAHcg_prot.ncrst 6W9C_SIRAHcg_10us_prot_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Florencia Klein (fklein@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: NSP16 - NSP10 Complex (PDB id:6W4H)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 NSP16 - NSP10 Complex with Zn ions bound (PDB id: 6W4H). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Zinc ions were parameterized as reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00160">Klein et al. 2020</a>.</p> <p>The file 6W4H_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6W4H_SIRAHcg_10us_prot_Zn.tar contains only the protein coordinates, while 6W4H_SIRAHcg_10us_prot_Zn_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6W4H_SIRAHcg_10us_prot_Zn_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6w4h_SIRAHcg_prot.prmtop 6w4h_SIRAHcg_prot.ncrst 6w4h_SIRAHcg_prot_Zn_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Martín Soñora (msonora@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p> <p> </p>
SIRAH-CoV2 initiative: ORF7A enconded accessory protein (PDB id:6W37)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 ORF7a encoded accessory protein (PDB id: 6W37). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. </p> <p>The file 6W37_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually, continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6W37_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6W37_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6W37_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6w37_SIRAHcg_prot.prmtop 6w37_SIRAHcg_prot.ncrst 6w37_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Martín Soñora (msonora@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: RNA-dependent RNA polymerase in complex with cofactors Nsp7 and Nsp8 (PDB id:7BTF)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 RNA-dependent RNA polymerase in complex with cofactors Nsp7 and Nsp8 and Zinc (PDB id: 7BTF). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Zinc ions were parameterized as reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00160">Klein et al. 2020</a>.</p> <p>The files 7BTF_SIRAHcg_rawdata_0-2us.tar, 7BTF_SIRAHcg_rawdata_2-6us.tar, and 7BTF_SIRAHcg_rawdata_6-10us.tar, contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 7BTF_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 7BTF_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 7BTF_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD 1.9.3 using the command line:</p> <p>vmd 7BTF_SIRAHcg_prot.prmtop 7BTF_SIRAHcg_prot.ncrst 7BTF_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Martin Soñora (msonora@pasteur.edu.uy) Sergio Pantano (spantano@pasteur.edu.uy).</p>
SHAPE-ID Literature Review dataset: bibliography on IDR/TDR
<p><strong>Background and methodology:</strong></p> <p>The dataset consists of 5040 records of publication metadata (author, abstract, title, keywords, tags etc.), produced for the purposes of the <a href="https://doi.org/10.5281/zenodo.3760417">systematic literature review</a> in the framework of the <a href="https://www.shapeid.eu/">SHAPE-ID</a> project. </p> <p>In the course of the review Project team queried Web of Science (WoS), Scopus and JSTOR databases for records on interdisciplinarity and transdisciplinarity (IDR/TDR). In the case of WoS and Scopus, <a href="https://doi.org/10.5281/zenodo.4034333">complex search strings</a> were created to reflect the main research questions of the Literature Review: different understandings of IDR/TDR and factors and indicators of success or failure of integration of IDR/TDR in research and research policy. JSTOR database offers less advanced data-analytical tools, but the project team decided to include items that have interdisciplinarity or transdisciplinarity in the title, to counterbalance the reported biases against Arts, Humanities and Social Sciences in Scopus and WoS. These three data sources were complemented with bibliographies prepared during the preliminary scoping analysis of IDR/TDR literature. The query results were compiled in reference managers Zotero and Endnote. During data processing the records were normalized and duplicates were removed. </p> <p>Based on systematic review, a sample of the literature had been selected for qualitative analysis. At the same time, the bibliographic metadata was analysed with computationally assisted quantitative methods.</p> <p><strong>Description of the file:</strong></p> <p>This is a csv file exported from the Zotero database, and formatted according to the <a href="https://www.zotero.org/support/kb/item_types_and_fields">Zotero metadata model</a>. It contains a collection of 5040 bibliographic records compiled for the purpose of the SHAPE-ID Literature Review.</p>
SHAPE-ID Literature Review dataset: journal occurrences with ASJC codes
<p><strong>Background and methodology:</strong></p> <p>The dataset consists of a list of 2202 journal titles represented in the <a href="https://doi.org/10.5281/zenodo.4034507">SHAPE-ID Literature Review bibliography</a>, prepared for the purposes of quantitative analysis.</p> <p>The list of journals is based on 3955 journal articles in the bibliography dataset that had an International Standard Serial Number (ISSN). To each journal title the project team attributed:</p> <p>- a weight factor based on how many articles from the given journal featured in bibliography dataset</p> <p>- at least one <a href="https://service.elsevier.com/app/answers/detail/a_id/15181/supporthub/scopus/">All Science Journal Classification</a> (ASJC) code, representing different scientific disciplines</p> <p>- a country of publication. </p> <p>In case of 1853 of those journal titles, the attribution was automatised (we matched the ISSNs of journal titles in our sample against the Scopus Sources list from February 2019). In case of the remaining 349 titles the attribution was accomplished manually, based on the information available in SCOPUS, Web of Science, JSTOR, Information Matrix for the Analysis of Journals (MIAR) and ISSN databases.</p> <p><strong>Description of the file:</strong></p> <p>This is a csv file containing a list of 2202 journal titles represented in the SHAPE-ID Literature Review bibliography, with country of publication and ASJC codes assigned. </p> <p>The file is formatted as follows:</p> <p>Column A: ISSN of the journal</p> <p>Column B: information on how country and ASJC codes were attributed. Value “N” indicates automatic attribution based on match with Scopus list of sources. Other values indicate manual attribution. Values WOS, SCOPUS, JSTOR indicate source of information. Valu “Y” indicates that information was compiled based on multiple sources. </p> <p>Column C: numeric values correspond to the weight factor, i.e. number of time articles from each journal featured in the SHAP-ID Literature Review bibliography. </p> <p>Column D: SHAPE-ID Zotero bibliography identifier.</p> <p>Column E: Journal title</p> <p>Column F: The country of publication</p> <p>Columns G-AD: ASJC codes (numeric and word values) associated with journal entries. </p>
Võro IDS wordlist by Leon Cosgrove and Sulev Iva
<p>Cite the source of the dataset as:</p> <blockquote> <p>Leon Cosgrove and Sulev Iva. 2020. Võro dictionary. In: Key, Mary Ritchie & Comrie, Bernard (eds.) The Intercontinental Dictionary Series. Leipzig: Max Planck Institute for Evolutionary Anthropology. (Available online at http://ids.clld.org/)</p> </blockquote>
SIRAH-CoV2 initiative: Spike D614G mutation (introduced on PDB id:6XR8)
<p>This dataset contains the raw data of a coarse-grained molecular dynamics simulation of the D614G mutant of Spike protein from SARS-CoV-2. The initial coordinates correspond to the PDB structure 6XR8, where the coordinates of the side chain of Aspartate 614 were deleted to produce a Glycine. Missing loops were introduced with Swiss-Model (https://swissmodel.expasy.org). Simulations were performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Glycan parameters correspond to those reported by <a href="http://doi.org/10.1101/2020.12.18.423446">Garay et at.</a>2020. </p> <p>The files spike_D614G_SIRAHcg_rawdata_0-2us.tar, spike_D614G_SIRAHcg_rawdata_2-4us.tar, spike_D614G_SIRAHcg_rawdata_4-6us.tar, contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file spike_D614G_SIRAHcg_prot_10us.tar contains only the protein coordinates, while spike_D614G_SIRAHcg_prot_10us_skip10ns.tar contains one frame every 10ns. Notice that these two files contain a 10-microsecond trajectory, while the raw data is limited to 6 microseconds because of size restrictions on the database. Raw data from microseconds 6 to 10 is available upon request (see below).</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file spike_D614G_SIRAHcg_prot_10us_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd spike_D614G_SIRAHcg_prot.prmtop spike_D614G_SIRAHcg_prot.ncrst spike_D614G_SIRAHcg_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV-2 initiative.</p> <p>For further details, please contact Pablo Garay (pgaray@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>
SIRAH-CoV2 initiative: nsp7-nsp8 complex (PDB id:6YHU)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 nsp7-nsp8 complex (PDB id: 6YHU, Bioassembly 1). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. </p> <p>The file 6YHU_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6YHU_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6YHU_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6YHU_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6yhu_SIRAHcg_prot.prmtop 6yhu_SIRAHcg_prot.ncrst 6yhu_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Martín Soñora (msonora@pasteur.edu.uy) Sergio Pantano (spantano@pasteur.edu.uy).</p>
Wikipedia: wikipedia-id (Indonesian)
Wikipedia is a multilingual, web-based, free-content encyclopedia project supported by the Wikimedia Foundation and based on a model of openly editable content. EOL harvests articles from wikipedia that are indexed as species or higher taxa.<p></p>Wikipedia bahasa Indonesia disediakan secara gratis oleh Wikimedia Foundation, sebuah organisasi nirlaba. Selain dalam bahasa Indonesia, Wikipedia tersedia dalam bahasa daerah berikut: Aceh, Bali, Banjar, Banyumasan, Bugis, Gorontalo, Jawa, Melayu, Minangkabau, Sunda, dan Tetun. <p></p>https://id.wikipedia.org/wiki/Halaman_Utama
Maltese IDS wordlist by Michael Spagnol and Bernard Comrie
<p>Cite the source of the dataset as:</p> <blockquote> <p>Michael Spagnol and Bernard Comrie. 2020. Maltese dictionary. In: Key, Mary Ritchie & Comrie, Bernard (eds.) The Intercontinental Dictionary Series. Leipzig: Max Planck Institute for Evolutionary Anthropology. (Available online at http://ids.clld.org/)</p> </blockquote>
ID market related datasets for HLU9 of the CROSSBOW project
<p>Datasets for HLU9 of the CROSSBOW project organised by countries individually. What is included in the dataset?</p> <ol> <li><strong>Cross-Zonal Capacities</strong> - unformatted dataset published as received from the TSOs <ul> <li>CZC data for the years 2018 & 2019 were gathered in HLU9. The CZC values were for the timeframe before the ID market took place. Parameters included: Date & Time, CZC value to and from the delivering LFC area. CZC data was collected for borders of TSOs from the CROSSBOW project consortium<strong>.</strong></li> </ul> </li> <li><strong>ID market data - </strong>unformatted dataset published as received from the TSOs <ul> <li>ID related data for the years 2018 & 2019 were gathered in HLU9. The parameters that were included in the data were: Date & Time, Volume Weighted Average Price, Traded volume, Location… ID market related data was collected for countries of CROSSBOW TSOs, where such data was available.</li> </ul> </li> </ol>
Forschungsdaten und Fragebogen zur "Umfrage zum Bedarf und Nutzung von Organisations-IDs an Hochschulen und außeruniversitären Forschungseinrichtungen in Deutschland"
<p>*English description below*</p> <p>Um mehr über den Status quo der Nutzung und Verbreitung von Organisations-IDs in Deutschland zu erfahren, wurde im Rahmen des DFG-geförderten Projekts ORCID DE im Zeitraum vom 13.07.2020 bis zum 04.12.2020 eine „Umfrage zum Bedarf und Nutzung von Organisations-IDs an Hochschulen und außeruniversitären Forschungseinrichtungen in Deutschland“ unter 548 wissenschaftlichen Einrichtungen in Deutschland durchgeführt. An der bislang größten Befragung zu Organisations-IDs in Deutschland beteiligten sich 183 Einrichtungen. Bestandteil der Studie waren unter anderem Fragen zur Kenntnis, Verbreitung und Nutzung von Organisations-IDs an wissenschaftlichen Einrichtungen. Darüber hinaus wurden Anforderungen an Organisations-IDs bzw. ihre Metadaten (z. B. Relationen und Granularität) erfragt. Die vorliegende quantitative Befragung wurde als Online-Umfrage durchgeführt. Technisch wurde die Befragung mit der Software LimeSurvey auf einem Server der Universität Bielefeld umgesetzt. </p> <p>Die vorliegende CSV-Datei enthält die aus Lime-Survey exportierten, prozessierten und anonymisierten Antworten der 183 teilnehmenden Einrichtungen auf die 22 gestellten Fragen. Der Fragebogen wurde besseren Nachvollziehbarkeit der Dokumentation sowie zur Reproduzierbarkeit ebenfalls als PDF-Datei bereitgestellt. Sowohl die Forschungsdaten als auch der Fragebogen sind auf Deutsch.</p> <p>In order to learn more about the status quo of the use and dissemination of organizational identifiers in Germany, a "Survey on the Need for and Use of Organizational Identifiers at Universities and Non-University Research Institutions in Germany" was conducted among 548 research institutions in Germany in the period from July 13, 2020 to December 4, 2020 as part of the DFG-funded ORCID DE project. This survey constitutes the largest survey on organizational identifiers in Germany to date; it counts 183 participating institutions. Among other things, the survey included questions on the knowledge, distribution, and use of organizational identifiers at research institutions.</p> <p>Technically, the survey was implemented with the software LimeSurvey on a server at Bielefeld University. </p> <p>This CSV file contains the processed and anonymised answers of the 183 participating institutions to the 22 questions exported from LimeSurvey. The questionnaire was also made available as a PDF file for better traceability of the documentation and for reproducibility. Both the research data and the questionnaire are in German.</p>
ScienceDex guides
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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)
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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.