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Supplementary data (CC BY-NC-SA 4.0): A reactive neural network framework for water-loaded acidic zeolites
<p><strong>Content (Creative Commons Attribution Non Commercial Share Alike 4.0 International):</strong></p><p>This dataset provides supplementary data to "A reactive neural network framework for water-loaded acidic zeolites". It contains trained Neural Network Potentials (NNP and ΔNNP model), scripts, and all energy and force data used in this work at the (Δ)NNP, ReaxFF, and DFT (SCAN+D3(BJ) and ωB97X-D3(BJ)) level. Energy and forces are stored as ASE trajectory files (traj), readable by the <a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment </a>(ASE). In addition, this repository contains the generated training database with DFT (SCAN+D3(BJ)) energies and forces as SchNetPack1.0 database (SiAlOH.db) file readable by ASE and <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>.</p><ol><li>"aimd_simulations.zip" - VASP INCAR file, XDATCAR and traj file for 10 ps AIMD run (Supplementary Figure 6) and NNP level (re-)calculated energies/forces ("aimd_nnp_recalc.traj")</li><li>"biased_dynamics.zip" - VASP/Plumed input and output files for DFT (SCAN+D3(BJ)) and NNP level biased dynamics including traj files (Supplementary Figure 12)</li><li>"database_input.zip" - structure (cif) files of the initial structures used for database generation (Supplementary Table 1)</li><li>"delta_nnp.zip" - (pytorch) ΔNNP model (compatible with <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>) together with example scripts </li><li>"error_stats.zip" - traj files of all generalization tests (Figure 1 and Supplementary Figure 4) storing energies/forces at the SCAN+D3(BJ), ReaxFF, and NNP level as well as traj files with ΔNNP and ωB97X-D3(BJ) energies/forces for a subset taken from biased dynamics runs (Supplementary Figure 11)</li><li>"md_simulations.zip" - NNP level MD trajectories of all generalization test (Figure 1 and Supplementary Figure 4) runs including an example script for an MD run</li><li>"neb_calculations.zip" - traj files and example scripts for NEB calculations at the (Δ)NNP along with the corresponding DFT energy/force data (SCAN+D3(BJ) and ωB97X-D3(BJ))</li><li>"nnps.zip" - (pytorch) NNP model files (compatible with <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>)</li><li>"silica_database.zip" - output files of the single-point (SP) and optimization test runs (Supplementary Figure 1) of pure silica structures together with an example structure optimization script </li><li>"SiAlOH.db" - DFT (SCAN+D3(BJ)) training database as SchNetPack1.0 database file readable by ASE and <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a></li></ol>
Data for: Temperature-controlled Molecular Bonding Hysteresis: Interphase Dynamics of a Nanoparticle-modified Polymer Network
<p>The data is supplementary to the publication "Temperature-controlled Molecular Bonding Hysteresis: Interphase Dynamics of a Nanoparticle-modified Polymer Network", DOI: <a title="DOI URL" href="https://doi.org/10.1021/acs.jpclett.4c00406">10.1021/acs.jpclett.4c00406</a></p> <p>Key words: Thermal volume expansion, Interphase dynamics, Temperature-modulated optical refractometry, Nanoparticles, Optical Remanence, Hysteresis, Refractive index</p> <p>The data sets contain measured and processed data on the interphase dynamics of a nanoparticle modified epoxy resin collected via Temperature-modulated optical refractometry (TMOR).</p> <p>Material details:</p> <ul> <li>Cycloaliphatic epoxy resin + Anhydride curing agent + 1-methylimidazole</li> <li>Core-shell rubber nanoparticles, 100 nm, dispersed in a cycloaliphatic epoxy carrier resin</li> </ul> <p>Funding received from:</p> <ul> <li>German Research Foundation (DFG), project number: 521902629.</li> </ul>
Code and Data for "Anticoncentration and state design of random tensor networks"
<p>We investigate quantum random tensor network states where the bond dimensions scale polynomially with the system size, N. Specifically, we examine the delocalization properties of random Matrix Product States (RMPS) in the computational basis by deriving an exact analytical expression for the Inverse Participation Ratio (IPR) of any degree, applicable to both open and closed boundary conditions. For bond dimensions χ∼γN, we determine the leading order of the associated overlaps probability distribution and demonstrate its convergence to the Porter-Thomas distribution, characteristic of Haar-random states, as γ increases. Additionally, we provide numerical evidence for the frame potential, measuring the 2-distance from the Haar ensemble, which confirms the convergence of random MPS to Haar-like behavior for χ≫\sqrt{N}. We extend this analysis to two-dimensional systems using random Projected Entangled Pair States (PEPS), where we similarly observe the convergence of IPRs to their Haar values for χ≫\sqrt{N}. These findings demonstrate that random tensor networks with bond dimensions scaling polynomially in the system size are fully Haar-anticoncentrated and approximate unitary designs, regardless of the spatial dimension.</p>
Data belonging to: Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013.
<p>Data belonging to the paper Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013.</p> <p>Data includes analysis scripts (R Language) and all used data files. Data is composed of location information of the different sites, environmental conditions on site and vegetation composition.</p>
Data for paper "Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts"
<p>The forecasts and observation datasets are used in the paper "Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts". https://doi.org/10.1016/j.jhydrol.2021.127301</p> <p>The forecast data is a subset of the "ensemble for machine learning dataset (ENS4ML)" from ECMWF. </p> <p>The Python codes are stored in Github: https://github.com/wentao-bnu/LeNet_CSG_Precip</p>
Supplementary data: Accurate large-scale simulations of siliceous zeolites by neural network potentials
<p><strong>Content</strong></p> <p><em>1. Zeolite databases</em></p> <ul> <li>Deem database containing 331170 hypothetical zeolite frameworks [Deem09, Pophale11] geometrically optimized at the NNPscan level (note, the first row of the database is alpha-quartz): "DEEM_NNPscan.db"</li> <li>Database of 236 exiting zeolite frameworks of the <a href="http://www.iza-structure.org/databases/">International Zeolite Association (IZA) </a>optimized at the NNPscan level: "IZA_NNPscan.db"</li> <li>Both databases are <a href="https://wiki.fysik.dtu.dk/ase/ase/db/db.html">ASE SQLite database files</a> of the <a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment</a> containing the ASE <a href="https://wiki.fysik.dtu.dk/ase/ase/atoms.html">Atoms objects</a> with energies and forces (NNPscan level); readable with ASE's <a href="https://wiki.fysik.dtu.dk/ase/ase/io/io.html">I/O module</a></li> <li>Additionally, relevant quantities can be extracted with, e.g., the following queries (further information: ase db --help):</li> </ul> <pre><code class="language-bash">ase db DEEM_NNPscan.db -c id,formula,natoms,volume,mass,density,energy_per_tsite,n_tsites,relative_energy # Output id|formula|natoms| volume| mass|density|energy_per_tsite|n_tsites|relative_energy 1|O6Si3 | 9|111.161|180.249| 26.988| -31.796| 3| 0.000 2|O16Si8 | 24|433.858|480.664| 18.439| -31.638| 8| 15.265 3|O16Si8 | 24|421.114|480.664| 18.997| -31.596| 8| 19.359 4|O16Si8 | 24|426.557|480.664| 18.755| -31.614| 8| 17.613 5|O16Si8 | 24|412.410|480.664| 19.398| -31.613| 8| 17.677 6|O16Si8 | 24|393.544|480.664| 20.328| -31.594| 8| 19.546 7|O16Si8 | 24|422.400|480.664| 18.939| -31.657| 8| 13.476 8|O16Si8 | 24|394.405|480.664| 20.284| -31.581| 8| 20.797 9|O12Si6 | 18|265.201|360.498| 22.624| -31.611| 6| 17.868 10|O16Si8 | 24|357.047|480.664| 22.406| -31.581| 8| 20.785 11|O16Si8 | 24|434.894|480.664| 18.395| -31.621| 8| 16.911 12|O16Si8 | 24|384.158|480.664| 20.825| -31.657| 8| 13.448 13|O12Si6 | 18|258.977|360.498| 23.168| -31.679| 6| 11.278 14|O16Si8 | 24|466.429|480.664| 17.152| -31.593| 8| 19.588 15|O16Si8 | 24|423.469|480.664| 18.892| -31.639| 8| 15.179 16|O16Si8 | 24|450.716|480.664| 17.750| -31.628| 8| 16.219 17|O16Si8 | 24|331.528|480.664| 24.131| -31.642| 8| 14.857 18|O16Si8 | 24|458.573|480.664| 17.445| -31.635| 8| 15.572 19|O16Si8 | 24|359.298|480.664| 22.266| -31.655| 8| 13.636 20|O16Si8 | 24|464.264|480.664| 17.232| -31.612| 8| 17.750 Rows: 331171 (showing first 20) Keys: density, energy_per_tsite, n_tsites, relative_energy ase db IZA_NNPscan.db -c id,formula,natoms,volume,mass,density,energy_per_tsite,n_tsites,relative_energy,iza_code # Output id|formula |natoms| volume| mass|density|energy_per_tsite|n_tsites|relative_energy|iza_code 1|O16Si8 | 24| 435.488| 480.664| 18.370| -31.676| 8| 11.594|ABW 2|O32Si16 | 48| 961.419| 961.328| 16.642| -31.645| 16| 14.612|ACO 3|O96Si48 | 144|3154.579|2883.984| 15.216| -31.664| 48| 12.810|AEI 4|O80Si40 | 120|2102.921|2403.320| 19.021| -31.703| 40| 9.021|AEL 5|O96Si48 | 144|2417.286|2883.984| 19.857| -31.666| 48| 12.586|AEN 6|O144Si72| 216|4075.300|4325.976| 17.667| -31.674| 72| 11.831|AET 7|O96Si48 | 144|2786.810|2883.984| 17.224| -31.675| 48| 11.716|AFG 8|O48Si24 | 72|1400.247|1441.992| 17.140| -31.690| 24| 10.268|AFI 9|O64Si32 | 96|1764.823|1922.656| 18.132| -31.653| 32| 13.809|AFN 10|O80Si40 | 120|2080.330|2403.320| 19.228| -31.707| 40| 8.632|AFO 11|O64Si32 | 96|2097.384|1922.656| 15.257| -31.655| 32| 13.622|AFR 12|O112Si56| 168|3820.116|3364.648| 14.659| -31.650| 56| 14.150|AFS 13|O144Si72| 216|4732.720|4325.976| 15.213| -31.664| 72| 12.793|AFT 14|O60Si30 | 90|1897.074|1802.490| 15.814| -31.659| 30| 13.268|AFV 15|O96Si48 | 144|3154.885|2883.984| 15.214| -31.664| 48| 12.776|AFX 16|O32Si16 | 48|1137.335| 961.328| 14.068| -31.591| 16| 19.790|AFY 17|O48Si24 | 72|1283.812|1441.992| 18.694| -31.620| 24| 17.034|AHT 18|O96Si48 | 144|2479.287|2883.984| 19.360| -31.681| 48| 11.155|ANA 19|O64Si32 | 96|1797.086|1922.656| 17.807| -31.662| 32| 12.924|APC 20|O64Si32 | 96|1751.393|1922.656| 18.271| -31.678| 32| 11.422|APD Rows: 236 (showing first 20) Keys: density, energy_per_tsite, iza_code, n_tsites, relative_energy # Filtering of the database, e.g., for structures with relative energies < 10 kJ/(mol Si) ase db IZA_NNPscan.db relative_energy\<10 -c density,energy_per_tsite,n_tsites,relative_energy,iza_code # Output density|energy_per_tsite|n_tsites|relative_energy|iza_code 19.021| -31.703| 40| 9.021|AEL 19.228| -31.707| 40| 8.632|AFO 19.385| -31.695| 24| 9.802|ATV 18.778| -31.702| 34| 9.061|DOH 19.570| -31.693| 24| 9.959|EWO 18.401| -31.698| 32| 9.451|GON 18.551| -31.695| 112| 9.807|IHW 17.778| -31.693| 288| 9.972|IMF 19.154| -31.695| 6| 9.762|JBW 18.187| -31.695| 96| 9.734|MFI 19.278| -31.709| 48| 8.443|MRE 18.035| -31.698| 90| 9.481|MSO 20.417| -31.724| 44| 7.003|MTF 19.227| -31.704| 136| 8.898|MTN 18.542| -31.693| 28| 9.966|MTW 19.137| -31.695| 60| 9.798|PCR 20.037| -31.709| 144| 8.464|PSI 18.843| -31.703| 64| 9.004|SAF 18.371| -31.703| 112| 8.975|STO 19.894| -31.706| 17| 8.671|VET Rows: 20 (showing first 20) Keys: density, energy_per_tsite, iza_code, n_tsites, relative_energy</code></pre> <ul> <li>The quantities shown above are available with the keys (besides standard ASE database keys):</li> </ul> <table> <thead> <tr> <th scope="col">Key</th> <th scope="col">Quantity</th> <th scope="col">Unit</th> </tr> </thead> <tbody> <tr> <td>id</td> <td>Identifier</td> <td> </td> </tr> <tr> <td>formula</td> <td>Chemical formula of the unit cell</td> <td> </td> </tr> <tr> <td>natoms</td> <td>Number of atoms</td> <td> </td> </tr> <tr> <td>volume</td> <td>Unti cell volume</td> <td>Å<sup>3</sup></td> </tr> <tr> <td>mass</td> <td>Atomic mass of the unit cell</td> <td>amu</td> </tr> <tr> <td>density</td> <td>Framework density</td> <td>Si/nm<sup>3</sup></td> </tr> <tr> <td>energy_per_tsite</td> <td>NNPscan energy</td> <td>eV</td> </tr> <tr> <td>n_tsites</td> <td>Number of T-sites</td> <td> </td> </tr> <tr> <td>relative_energy</td> <td>Energy with respect to quartz</td> <td>kJ/(mol Si)</td> </tr> <tr> <td>iza_code</td> <td>only for 'IZA_NNPscan.db'</td> <td> </td> </tr> </tbody> </table> <ul> <li> Comma separated csv files for the quantities listed above: "DEEM_NNPscan.csv" and "IZA_NNPscan.csv"</li> </ul> <p><em>2. Neural network potentials (NNP) for silica</em></p> <ul> <li>SchNet [Schütt18,Schütt19] NNP files trained on DFT data at the PBE+D3 (NNPpbe) and SCAN+D3 level (NNPscan)</li> <li>Simulations can be performed using <a href="https://schnetpack.readthedocs.io/en/stable/getstarted/getstarted.html#references">SchNetPack</a> with its ASE calculator</li> <li>This example shows a simple single-point calculation</li> </ul> <pre><code class="language-python">import ase.io import torch from schnetpack.interfaces import SpkCalculator from schnetpack.environment import AseEnvironmentProvider # check if GPU(s) are available if torch.cuda.is_available(): device = "cuda" else: device = "cpu" # load the NNP model model = torch.load('SiOscan1', map_location=device) # read some structure atoms = ase.io.read( ... ) # define SchNetPack calculator calc = SpkCalculator(model=model, device=device, energy='energy', forces='forces', environment_provider=AseEnvironmentProvider(6.) ) # attach calculator to atoms object atoms.set_calculator(calc) # perform simulations, e.g., single-point calculation energy = atoms.get_potential_energy() print(energy)</code></pre> <p><em>3. Test set used for accuracy evaluation (ASE database: test_set_NNPscan.db)</em></p>
Citation network data sets for 'Oxytocin – a social peptide? Deconstructing the evidence'
<p><strong>Introduction</strong></p> <p>This note describes the data sets used for all analyses contained in the manuscript 'Oxytocin - a social peptide?’<a href="#_ftn1">[1]</a> </p> <p><strong>Data Collection</strong></p> <p>The datasets described here were originally retrieved from Web of Science (WoS) Core Collection via the University of Edinburgh’s library subscription <a href="#_ftn2">[2]</a>. The aim of the original study for which these data were gathered was to survey peer-reviewed primary studies on oxytocin and social behaviour. To capture relevant papers, we used the following query:</p> <p><em>TI = (“oxytocin” OR “pitocin” OR “syntocinon”) AND TS = (“social*” OR “pro$social” OR “anti$social”)</em></p> <p>The final search was performed on the 13 September 2021. This returned a total of 2,747 records, of which 2,049 were classified by WoS as ‘articles’. Given our interest in primary studies <em>only</em> – articles reporting original data – we excluded all other document types. We further excluded all articles sub-classified as ‘book chapters’ or as ‘proceeding papers’ in order to limit our analysis to primary studies published in peer-reviewed academic journals. This reduced the set to 1,977 articles. All of these were published in the English language, and no further language refinements were unnecessary.</p> <p>All available metadata on these 1,977 articles was exported as plain text ‘flat’ format files in four batches, which we later merged together via Notepad++. Upon manually examination, we discovered examples of papers classified as ‘articles’ by WoS that were, in fact, reviews. To further filter our results, we searched all available PMIDs in PubMed (1,903 had associated PMIDs - ~96% of set). We then filtered results to identify all records classified as ‘review’, ‘systematic review’, or ‘meta-analysis’, identifying 75 records <a href="#_ftn3">[3]</a> (thus, ~4% of records classified by WoS were classified as reviews in PubMed). After examining a sample and agreeing with the PubMed classification, these were removed these from our dataset - leaving a total of 1,902 articles.</p> <p>From these data, we constructed two datasets via parsing out relevant reference data via the Sci2 Tool <a href="#_ftn4">[4]</a>. First, we constructed a ‘node-attribute-list’ by first linking unique reference strings (‘Cite Me As’ column in WoS data files) to unique identifiers, we then parsed into this dataset information on the identify of a paper, including the title of the article, all authors, journal publication, year of publication, total citations as recorded from WoS, and WoS accession number. Second, we constructed an ‘edge-list’ that records the citations from a <em>citing paper</em> in the ‘Source’ column and identifies the <em>cited paper</em> in the ‘Target’ column, using the unique identifies as described previously to link these data to the node-attribute-list.</p> <p>We then constructed a network in which papers are nodes, and citation links between nodes are directed edges between nodes. We used Gephi Version 0.9.2 <a href="#_ftn5">[5]</a> to manually clean these data by merging duplicate references that are caused by different reference formats or by referencing errors. To do this, we needed to retain both all retrieved records (1,902) as well as including <em>all</em> of their references to papers whether these were included in our original search or not. In total, this produced a network of 46,633 nodes (unique reference strings) and 112,520 edges (citation links). Thus, the average reference list size of these articles is ~59 references. The mean indegree (within network citations) is 2.4 (median is 1) for the entire network reflecting a great diversity in referencing choices among our 1,902 articles.</p> <p>After merging duplicates, we then restricted the network to include <em>only</em> articles fully retrieved (1,902), and retrained <em>only</em> those that were connected together by citations links in a large interconnected network (i.e. the largest component). In total, 1,892 (99.5%) of our initial set were connected together via citation links, meaning a total of ten papers were removed from the following analysis – and these were neither connected to the largest component, nor did they form connections with one another (i.e. these were ‘isolates’).</p> <p>This left us with a network of 1,892 nodes connected together by 26,019 edges. <strong><em>It is this network that is described by the ‘node-attribute-list’ and ‘edge-list’ provided here</em></strong>. This network has a mean in-degree of 13.76 (median in-degree of 4). By restricting our analysis in this way, we lose 44,741 unique references (96%) and 86,501 citations (77%) from the full network, but retain a set of articles tightly knitted together, all of which have been fully retrieved due to possessing certain terms related to oxytocin AND social behaviour in their title, abstract, or associated keywords.</p> <p>Before moving on, we calculated indegree for all nodes in this network – this counts the number of citations to a given paper from other papers within this network – and have included this in the <em>node-attribute-list</em>. We further clustered this network via modularity maximisation via the Leiden algorithm <a href="#_ftn6">[6]</a>. We set the algorithm to resolution 1, and allowed the algorithm to run over 100 iterations and 100 restarts. This gave <em>Q</em>=0.43 and identified seven clusters, which we describe in detail within the body of the paper. We have included cluster membership as an attribute in the node-attribute-list.</p> <p>For additional analysis, we also analysed the full reference list data to examine the most commonly cited references between 2016 and 2021 - the results of this are described in OTSOC_Cited_2016-2021.csv. This takes the reference lists of all retrieved papers within the network and examines their full reference lists (including references to other papers not contained within the network). These data were cleaned by matching DOIs and manual cleansing. </p> <p><strong>Data description</strong></p> <p>We include here two network datasets: (i) ‘OTSOC-node-attribute-list.csv’ consists of the attributes of 1,892 primary articles retrieved from WoS that include terms indicating a focus on oxytocin and social behaviour; (ii) ‘OTSOC-edge-list.csv’ records the citations between these papers. Together, these can be imported into a range of different software for network analysis; however, we have formatted these for ease of upload into Gephi 0.9.2. Finally, we include (iii) 'OTSOC_Cited_2016-2021' that lists all papers cited by >10 papers in the OTSOC network following any analysis of the bibliographies of retrieved papers. Below, we detail their contents:</p> <p><strong>1. ‘OTSOC-node-attribute-list.csv’</strong> is a comma-separate values file that contains all node attributes for the citation network (n=1,892) analysed in the paper. The columns refer to:</p> <p><em>Id</em>, the unique identifier</p> <p><em>Label</em>, the reference string of the paper to which the attributes in this row correspond. This is taken from the ‘Cite Me As’ column from the original WoS download. The reference string is in the following format: last name of first author, publication year, journal, volume, start page, and DOI (if available). </p> <p><em>Wos_id</em>, unique Web of Science (WoS) accession number. These can be used to query WoS to find further data on all papers via the ‘UT= ’ field tag.</p> <p><em>Title</em>, paper title.</p> <p><em>Authors</em>, all named authors.</p> <p><em>Journal, </em>journal of publication.</p> <p><em>Pub_year</em>, year of publication.</p> <p><em>Wos_citations</em>, total number of citations recorded by WoS Core Collection to a given paper as of 13 September 2021</p> <p><em>Indegree</em>, the number of within network citations to a given paper, calculated for the network shown in Figure 1 of the manuscript.</p> <p><em>Cluster</em>, provides the cluster membership number as discussed within the manuscript (Figure 1). This was established via modularity maximisation via the Leiden algorithm (Res 1; Q=0.43|7 clusters)</p> <p><strong>2. ‘OTSOC-edge -list.csv’</strong> is a comma-separated values file that contains all citation links between the 1,892 articles (n=26,019). The columns refer to:</p> <p><em>Source</em>, the unique identifier of the citing paper.</p> <p><em>Target, </em>the unique identifier of the cited paper.</p> <p><em>Type, </em>edges are ‘Directed’, and this column tells Gephi to regard all edges as such.</p> <p><em>Syr_date, </em>this contains the date of publication of the citing paper.</p> <p><em>Tyr_date, </em>this contains the date of publication of the cited paper.</p> <p><strong>3. 'OTSOC_Cited_2016-2021.csv'</strong> is a comma-separated values file that contain citations to all cited references that were cited by at least 10 of the retrieved papers within the OTSOC network published from 2016 onwards. The columns refer to: </p> <p><em>Reference, </em>the cited reference string extracted from the bibliographies of retrieved papers.</p> <p><em>Publication year, </em>the publication year of the cited reference.</p> <p><em>DOI</em>, the DOI of the cited reference. </p> <p><em>indegree_2016, </em>the total number of citations to a cited reference from papers published in 2016 and contained within the OTSOC network. </p> <p><em>indegree_2017, </em>the total number of citations to a cited reference from papers published in 2017 and contained within the OTSOC network. </p> <p><em>indegree_2018, </em>the total number of citations to a cited reference from papers published in 2018 and contained within the OTSOC network. </p> <p><em>indegree_2019, </em>the total number of citations to a cited reference from papers published in 2019 and contained within the OTSOC network. </p> <p><em>indegree_2020, </em>the total number of citations to a cited reference from papers published in 2020 and contained within the OTSOC network. </p> <p><em>indegree_2021, </em>the total number of citations to a cited reference from papers published in 2021 and contained within the OTSOC network. </p> <p><em>total indegree 2016-21</em>, the total number of citation to a cited reference from papers published between 2016-2021 and contained within the OTSOC network. </p> <p><strong>Software recommended for analysis</strong></p> <p>Gephi version 0.9.2 was used for the visualisations within the manuscript, and both files can be read and into Gephi without modification.</p> <p><strong>Notes</strong></p> <p><a href="#_ftnref1">[1]</a> Leng, G., Leng, R. I., Ludwig, M. (Submitted). Oxytocin – a social peptide? Deconstructing the evidence.</p> <p><a href="#_ftnref2">[2]</a> Edinburgh University’s subscription to Web of Science covers the following databases: (i) Science Citation Index Expanded, 1900-present; (ii) Social Sciences Citation Index, 1900-present; (iii) Arts & Humanities Citation Index, 1975-present; (iv) Conference Proceedings Citation Index- Science, 1990-present; (v) Conference Proceedings Citation Index- Social Science & Humanities, 1990-present; (vi) Book Citation Index– Science, 2005-present; (vii) Book Citation Index– Social Sciences & Humanities, 2005-present; (viii) Emerging Sources Citation Index, 2015-present.</p> <p><a href="#_ftnref3">[3]</a> For those interested, the following PMIDs were identified as ‘articles’ by WoS, but as ‘reviews’ by PubMed: ‘34502097’ ‘33400920’ ‘32060678’ ‘31925983’ ‘31734142’ ‘30496762’ ‘30253045’ ‘29660735’ ‘29518698’ ‘29065361’ ‘29048602’ ‘28867943’ ‘28586471’ ‘28301323’ ‘27974283’ ‘27626613’ ‘27603523’ ‘27603327’ ‘27513442’ ‘27273834’ ‘27071789’ ‘26940141’ ‘26932552’ ‘26895254’ ‘26869847’ ‘26788924’ ‘26581735’ ‘26548910’ ‘26317636’ ‘26121678’ ‘26094200’ ‘25997760’ ‘25631363’ ‘25526824’ ‘25446893’ ‘25153535’ ‘25092245’ ‘25086828’ ‘24946432’ ‘24637261’ ‘24588761’ ‘24508579’ ‘24486356’ ‘24462936’ ‘24239932’ ‘24239931’ ‘24231551’ ‘24216134’ ‘23955310’ ‘23856187’ ‘23686025’ ‘23589638’ ‘23575742’ ‘23469841’ ‘23055480’ ‘22981649’ ‘22406388’ ‘22373652’ ‘22141469’ ‘21960250’ ‘21881219’ ‘21802859’ ‘21714746’ ‘21618004’ ‘21150165’ ‘20435805’ ‘20173685’ ‘19840865’ ‘19546570’ ‘19309413’ ‘15288368’ ‘12359512’ ‘9401603’ ‘9213136’ ‘7630585’</p> <p><a href="#_ftnref4">[4]</a> Sci2 Team. (2009). Science of Science (Sci2) Tool. Indiana University and SciTech Strategies. Stable URL: <a href="https://sci2.cns.iu.edu">https://sci2.cns.iu.edu</a></p> <p><a href="#_ftnref5">[5]</a> Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media. Gephi is available via <a href="https://gephi.org/">https://gephi.org/</a></p> <p><a href="#_ftnref6">[6]</a> Traag, V. A., Waltman, L., & van Eck, N. J. (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific reports, 9(1), 5233. <a href="https://doi.org/10.1038/s41598-019-41695-z">https://doi.org/10.1038/s41598-019-41695-z</a></p>
Data for the paper 'Reducing networks of ethnographic codes co-occurrence in anthropology'
<p>Pseudonymized data supporting the paper "Reducing networks of ethnographic codes co-occurrence in anthropology", published in "Advances in Quantitative Ethnography. Fourth International Conference on Quantitative Ethnography (ICQE 2022), Copenhagen, Denmark, October 15–19, 2022, Proceedings", and edited by Amanda Barany and Crina Damsa. The paper is part of the POPREBEL project. The data were gathered in the spring and summer of 2021, as a part of a larger research project on populism in Central and Eastern Europe, to be completed by the end of 2022. They consist of 17 semi-structured interviews with Polish-speaking Internet users, who used social media to seek and share information about health against the backdrop of the COVID-19 pandemic. Research participants were asked about their opinion on the current state of affairs in their respective countries, and their political choices over the years and at present.</p> <p><a href="https://edgeryders.eu/t/long-term-ssna-data-storage-documentation-manual/12786">Data export and documentation process</a> (contains links to the code used to export the data).</p>
Data from Automated plankton image analysis using convolutional neural networks
<p>Datasets and code from Luo et al., "Automated plankton image analysis using convolutional neural networks." Limnology and Oceanography Methods.</p> <p>Data include:</p> <p>1) 42,564 item training library, sorted in 108 classes,</p> <p>2) 42,548 item test set for filtering thresholds, sorted into 38 groups. These images are independent from the training library, and are used for setting the thresholds for post-classification filtering.<br> CSV file: Luo_etal_FT_images_pred.csv contains the image name, predicted class, predicted probability, and validated group. Note that the file class_to_group.csv is needed to match up the class names to the group names.</p> <p>3) 75,000 item fully random, validated set for confusion matrix calculations, sorted into 38 groups. This set is a representation of the full dataset, selected at random after classification. <br> CSV file: Luo_etal_confusionmatrix_images.csv contains the image name, predicted class, predicted probability, and validated group. Note that the file class_to_group.csv is needed to match up the class names to the group names.</p> <p> </p> <p>Scripts and programs:</p> <p>1) Segmentation.zip contains the scripts and executables for the segmentation program.</p> <p>2) Plankton_template.zip contains the archived version of the SparseConvNet program used in manuscript (current version available at: https://github.com/btgraham/SparseConvNet or https://github.com/facebookresearch/SparseConvNet)<br> Note that google-sparsehash is necessary for running SparseConvNet.<br> Also, plankton_epoch-150.cnn are the weights from the training used in the manuscript, and should be placed in the /weights folder if you want to replicate the classifications.</p>
Mitigating Network Noise on Dragonfly Networks through Application-Aware Routing (code, data and scripts to reproduce paper results)
<p>This repository contains the data, code, and scripts required to reproduce the results of the paper "Mitigating Network Noise on Dragonfly Networks through Application-Aware Routing" by Daniele De Sensi, Salvatore Di Girolamo and Torsten Hoefler, presented at the 2019 International Conference for High Performance Computing, Networking, Storage, and Analysis. </p> <p>This repository does not contains the code of the library used to automatically tune the routing algorithm, which can be found at http://doi.org/10.5281/zenodo.3372785</p>
Replication data for: Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study
<p>This is the replication data for the scientific paper titled "Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study" accepted for publication in the Journal of Medical Internet Research (JMIR). For details on how to use the data files, please consider the "supplementary_material.R" file or the "supplementary_material.pdf" where the variables of interest and R code are presented.</p> <p>For the code to run correctly, have the files "multilevel_labels.R" and "glm_labels.R" in the same working directory as the .R or .Rmd script. They are executed in the background, applying modifications to labels inside the regression tables. </p> <p> </p>
Training data for neural network-based determination of nematic elastic constants
<p>Neural network training data packets (<strong><em>intensities_{i}.csv, K1K3_{i}.csv</em></strong>), each consisting of 1000 training data pairs, used in a machine learning-based method for determination of Frank elastic constants of nematic liquid crystals, experimental measurements of time-dependent light intensities (<strong><em>experimental_time</em></strong>_<strong><em>{i}.csv, experimental_intensity_{i}.csv</em></strong>), diode spectrum data (<strong><em>diode_lbd</em></strong><strong><em>.csv, diode_w.csv</em></strong>).</p> <p>These data sets are associated with the paper <a href="https://www.nature.com/articles/s41598-023-33134-x"><strong><em>[Zaplotnik et al. SciRep, 2023]</em></strong></a></p> <p>This is supplementary material for a Jupyter Notebook uploaded on <a href="https://zenodo.org/record/7368828">Zenodo</a>.</p>
The POPREBEL semantic social network data
<p>The <a href="https://populism-europe.com/poprebel/">POPREBEL project</a> explores the phenomenon of populism in Europe. As part of it, a team of ethnographers generated and coded the corpus contained in this dataset. It consists of coded interviews, realized between spring 2021 and spring 2022, to Internet users in Czechia, Germany and Poland, who used social media to gather information about the COVID-19 pandemic. The dataset is pseudonymized. POPREBEL is supported by the European Union's Horizon 2020 programme, grant n. 822682.</p> <ul> <li><a href="https://zenodo.org/record/7494327">Final ethnographic report.</a> Section 1.2 contains a detailed description of how and why data were collected.</li> <li><a href="https://wellbeing.edgeryders.eu">Funnel website</a> of the project.</li> <li><a href="https://hal.archives-ouvertes.fr/hal-02478720/document">About semantic social networks</a>.</li> <li><a href="https://edgeryders.eu/t/long-term-ssna-data-storage-documentation-manual/12786">Data export and documentation process</a> (contains links to the code used to export the data)</li> </ul>
Data from: Trade-offs in Coordination Strategies for Duet Jazz Performances Subject to Network Delay and Jitter
<p>This dataset is associated with the paper “Trade-offs in Coordination Strategies for Duet Jazz Performances Subject to Network Delay and Jitter” and includes recordings of improvisations by jazz duos over a network. The related paper is published in <em>Music Perception</em> and is accessible at <a href="https://doi.org/10.1525/mp.2024.42.1.48">doi:10.1525/mp.2024.42.1.48</a> </p> <p><strong>Introduction:</strong></p> <p>This dataset includes data from approximately four hours of live, improvised musical duo performances over a simulated network environment collected in Cambridge, United Kingdom between April-July 2022 as part of a doctoral research project. Data includes audio and video recordings of 130 individual performances, biometric data, and subjective evaluations and comments from the musicians. The primary aim of the project was to collect data via a novel performance capture and manipulation system for use in the empirical modelling of ensemble coordination strategies during networked music-making. This analysis is reported in Cheston, Cross, and Harrison (2023), "Trade-offs in Coordination Strategies for Networked Jazz Performances". Please refer to this publication for full details on the data collection procedure. Our codebook is <a href="https://github.com/HuwCheston/Jazz-Jitter-Analysis">hosted on GitHub</a> and, in conjunction with this dataset, can be used to reproduce the analysis contained in the article.</p> <p>The ten musicians shown in these recordings were recruited for their expertise in jazz improvisation. They were grouped into five duos consisting each of one pianist and drummer, with no musician performing in more than one duo. Participants were instructed to improvise together over a standard twelve-bar blues musical structure, but following a formula which required them to provide a clear and unambiguous pulse of continuous quarter notes. Varying amounts of network latency and jitter were simulated for each performance, consisting respectively of the minimum amount of delay applied to the live feedback a musician heard from their partner and the degree that this delay varied. The amount of latency and jitter applied to the performance is summarised in the file or directory name for each performance and is described in detail in the above publication. Note that latency and jitter conditions were presented in a random order for each duo.</p> <p><strong>Data collected includes:</strong></p> <ul> <li>audio recordings for each performance, with and without delay, collected via direct line-in (MIDI, WAV).</li> <li>video recordings, collected via high-quality webcams (MKV, AVI).</li> <li>streams of the quarter note pulse provided by each musician in a performance (MIDI).</li> <li>muxed audio-visual recordings of both participants in each performance (MP4)</li> <li>accelerometer and photoplethysmography streams, collected from arm-worn devices (TXT, duos 3-5 only)</li> <li>questionnaire responses from performers, evaluating each condition (XLSX)</li> <li>ratings of performance quality from an unbiased sample of listeners, collected during an online perceptual study (CSV)</li> </ul> <p><strong>Repository structure:</strong></p> <p><strong><em>NB: please see <a href="https://huwcheston.github.io/Jazz-Jitter-Analysis/getting-started.html">this section of the code documentation website</a> for a full description of how to recreate the analyses and models created in the paper.</em></strong></p> <p>The files <em>data.zip </em>and <em>data.z0*</em> contain all data collected from the study, APART from the perceptual study stimuli & results. To open these files, download the <em>data.zip</em> file and <em><strong>all the corresponding volumes ending in .z0 </strong></em>and open the <em>data.zip</em> file using a tool for opening multi-part zip files, such as WinRAR. <em>Do not try to open the files ending in .z0</em>, otherwise you may get a message about the data being corrupted. Inside <em>data.zip</em>, you'll see the following folders and files:</p> <ul> <li><em>avmanip_output</em>: the raw MIDI, audio, and video output from each performance <ul> <li>the subfolders are organised with a single folder per participant duo, experimental block, and condition.</li> <li>avmanip_output\trial_1\Block 1\Condition 1 - 23 05 relates to the performance of the first duo of participants in the first session of the experiment, in the first condition they encountered, with 23ms of latency and 0.5x jitter.</li> </ul> </li> <li><em>midi_bpm_cleaning</em>: the cleaned MIDI files (quarter note onset positions) <ul> <li>the subfolders are organised similarly to the <em>avmanip_output</em> folder, using the same conventions.</li> </ul> </li> <li><em>muxed_performances</em>: the combined audio-video .mp4 files from each performance <ul> <li>these files are labelled in the format: duo_session_latency_jitter_keysfmt_drumsfmt.</li> <li>muxed_performances\kdelay_ddelay\d1_s1_l23_j00_kdelay_ddelay.mp4 relates to the performance of the first duo of participants in the first session of the experiment, in the first condition they encountered, with 23ms of latency and 0.5x jitter, and with latency and jitter applied to both keys and drummer.</li> <li>for more information on recreating these videos, <a href="https://huwcheston.github.io/Jazz-Jitter-Analysis/getting-started.html#reproduce-combined-audio-visual-stimuli">see the linked section of the code documentation website.</a></li> </ul> </li> <li><em>questionnaire_anonymized</em>: the anonymized questionnaire responses given by participants, also contained in the supplementary material of the associated paper (see preprint).</li> </ul> <p>Alongside <em>data.zip </em>and the <em>data.z0*</em> archives, there are two further loose files, <em>Database View Participant - Dashboard.csv, Database View SuccessTrial - Dashboard.csv, </em>which are the anonymized demographic and response data from the perceptual experiment, and one loose archive folder <em>perceptual_study_videos.rar</em>, which contains the stimuli used in the perceptual experiment.</p> <p>To reproduce the analysis from the paper, all files should be unzipped into the \data\raw directory of the code repository created after <a href="https://github.com/HuwCheston/Jazz-Jitter-Analysis">cloning this from GitHub</a>. For more detail and instructions on installation, <a href="https://huwcheston.github.io/Jazz-Jitter-Analysis/getting-started.html">see the section of the code documentation website linked here</a>.</p> <p><strong>Usage:</strong></p> <p>These recordings of live, improvised duo performances are unattributed and anonymised as agreed with participants at the point of data collection. The musicians involved received a one-off, fixed payment for their time and had their travel expenses reimbursed, with funding provided by Cambridge Digital Humanities (<a href="https://www.cdh.cam.ac.uk/research/projects/newmusicsoftwareplatform/">project page</a>). All participants consented to the use of their recordings for projects by the current authors and for these recordings to be shared with interested members of the music psychology community, with the intention of furthering academic research. The musicians did not intend that the recordings be used for commercial, artistic, or entertainment purposes, and such use is not permitted.</p> <p><strong>Citation:</strong></p> <p>If you use this dataset in your research, please cite the paper it relates to:</p> <pre><code>@article{10.1525/mp.2024.42.1.48, author = {Cheston, Huw and Cross, Ian and Harrison, Peter M. C.}, title = "{Trade-offs in Coordination Strategies for Duet Jazz Performances Subject to Network Delay and Jitter}", journal = {Music Perception}, volume = {42}, number = {1}, pages = {48-72}, year = {2024}, month = {09}, issn = {0730-7829}, doi = {10.1525/mp.2024.42.1.48}, url = {https://doi.org/10.1525/mp.2024.42.1.48}, eprint = {https://online.ucpress.edu/mp/article-pdf/42/1/48/833292/mp.2024.42.1.48.pdf}, }</code></pre> <p><strong>Contact:</strong></p> <p>Huw Cheston - <a href="http://twitter.com/huwcheston/">@huwcheston</a> - hwc31@cam.ac.uk</p>
The TREASURE semantic social network data on the circular economy aspect of automotive manufacturing
<p>The <a href="https://www.treasureproject.eu/">TREASURE</a> project looks at industrial innovation to address the problem of making onboard electronics in the automotive industry easier to recycle, increasing the industry's contribution to the circular economy. As part of it, a team of ethnographers generated and coded the corpus contained in this dataset. interviews conducted between January 2022 and June 2023 with car owners and enthusiasts at car industry events. The interviews focus on experiences with car electronics and perspectives on sustainability and the circular economy. The dataset is pseudonymized. TREASURE is supported by the European Union's Horizon 2020 programme, grant n. 101003587.</p>
Data for: A tool based on the Industry Foundation Classes standard for dynamic data collection and automatic generation of Building Automation Control Networks
<p>This dataset shows the results obtained for a case study at TRL4 for the research paper title <em><strong>A tool based on the Industry Foundation Classes standard for dynamic data collection and automatic generation of Building Automation Control Networks</strong></em>, with DOI: https://doi.org/10.1016/j.jobe.2023.107625</p> <p>This dataset is an enhanced IFC (Industry Foundation Classes) file with the creation of the BACN (Building Automation Control Network). This IFC file includes the devices created automatically by the BACN2BIM tool (developed by CARTIF Technology Centre) for the case study validated at TRL4. The original IFC was obtained from the Institute for Automation and Applied Informatics (IAI) / Karlsruhe Institute of Technology (KIT) https://www.ifcwiki.org/images/e/e3/AC20-FZK-Haus.ifc, under an unrestricted license, as served as one of the case studies for this research.</p> <p>*Depending on the IFC viewer used, the included sensors may not be represented correctly. In this case, it is recommended to try with another IFC viewer, for example xBIM explorer https://docs.xbim.net/downloads/xbimxplorer.html or BimCollab Zoom Free https://www.bimcollab.com/en/support/downloads/</p>
LAGOS-US NETWORKS v1.0: Data module of surface water networks characterizing connections among lakes, streams, and rivers in the conterminous U.S
Knowing the degree of surface water connectivity among aquatic ecosystems can help scientists better understand and predict the movement of materials and biota across ecosystems. Methods to quantify surface water networks that include lake and stream connections at broad spatial scales are rare because it is difficult to balance accurate estimates of surface water connectivity and computational challenges. The LAGOS-US NETWORKS (NETS) module contains surface connectivity metrics for lake networks across the conterminous United States. We applied a graph theory approach to identify lake networks (i.e. a set of lakes connected by streams either upstream, downstream, or both) created from the medium resolution NHD lakes, streams, and rivers and subsequently derive surface water connectivity metrics for lakes and networks. Using this approach, we created a total of 898 networks that include 86,511 lakes. The NETS module includes a table with metrics for connections between lakes (both upstream and downstream), dams, network position, and whole networks. NETS also includes a flow table and bidirectional and unidirectional distance tables that provide the distances between every pair of connected lakes.
Stream and air temperature data from stream network in the Andrews Experimental Forest, 1997-2001
This study examines stream temperatures and associated air temperatures at multiple sites in stream networks within the Andrews Experimental Forest. Stream temperature sensors were placed at matched elevations in the main headwater streams of Lookout Creek, Mack Creek and McRae Creek as well as above and below major confluences in downstream reaches. Air temperatures were recorded 1.5 m above the stream at selected sites. Data were collected every half hour during late spring and summers. Some sites have data during fall and winter. Sensors were also placed in bottom of shallow piezometric wells in WS 3.
Monthly precipitation data from a network of standard gauges at the Jornada Experimental Range (Jornada Basin LTER) in southern New Mexico, January 1916 - ongoing
This ongoing dataset contains monthly precipitation measurements from a network of standard can rain gauges at the Jornada Experimental Range in Dona Ana County, New Mexico, USA. Precipitation physically collects within gauges during the month and is manually measured with a graduated cylinder at the end of each month. This network is maintained by USDA Agricultural Research Service personnel. This dataset includes 39 different locations but only 29 of them are current. Other precipitation data exist for this area, including event-based tipping bucket data with timestamps, but do not go as far back in time as this dataset.
MCR LTER: Coral Reef: Sensor Network: Bottom-mounted CTD Data - GUMPR, 2006-2012
Physical oceanographic data from bottom-mounted instrumentation (Seabird 16+ CTD) were sampled year-round on Gump reef in Cooks Bay on Moorea, French Polynesia (GUMPR site). Sampling began in 2006 until early 2012. The CTD measured conductivity, temperature, pressure, from which density and salinity were calculated. Data were collected every 5 minutes, processed and reported every 20 minutes. The instrument is mounted 2 m above the bottom in 6 m of depth. These data streamed near real-time as part of the Digital Moorea project (no longer active.) Daily, weekly, monthly and yearly means were calculated for temperature, salinity, and density. This is a completed timeseries which ended early 2012. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
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.