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MLFMF: Data Sets for Machine Learning for Mathematical Formalization
<h3>MLFMF</h3><p><strong>MLFMF (Machine Learning for Mathematical Formalization) </strong>is a collection of data sets for benchmarking recommendation systems used to support formalization of mathematics with proof assistants. These systems help humans identify which previous entries (theorems, constructions, datatypes, and postulates) are relevant in proving a new theorem or carrying out a new construction. </p><p>The MLFMF data sets provide solid benchmarking support for further investigation of the numerous machine learning approaches to formalized mathematics. With more than 250,000 entries in total, this is currently the largest collection of formalized mathematical knowledge in machine learnable format. </p><p>In addition to benchmarking the recommendation systems, the data sets can also be used for benchmarking <strong>node classification</strong> and <strong>link prediction</strong> algorithms. </p><h3>The four data sets</h3><p>Each data set is derived from a library of formalized mathematics written in proof assistants <a href="https://agda.readthedocs.io/en/v2.6.4/"><i>Agda</i></a> or <a href="https://lean-lang.org/"><i>Lean</i></a>. The collection includes </p><ol><li>the largest Lean 4 library <a href="https://github.com/leanprover-community/mathlib4"><strong>Mathlib</strong></a>,</li><li>the three largest Agda libraries:<ul><li>the <a href="https://github.com/agda/agda-stdlib"><strong>standard library</strong></a></li><li>the library of univalent mathematics <a href="https://github.com/UniMath/agda-unimath"><strong>Agda-unimath</strong></a>, and</li><li>the <a href="https://github.com/martinescardo/TypeTopology"><strong>TypeTopology</strong></a> library.</li></ul></li></ol><p>Each data set represents the corresponding library in two ways: as a heterogeneous network, and as a list of syntax trees of all the entries in the library. The network contains the (modular) structure of the library and the references between entries, while the syntax trees give complete and easily parsed information about each entry.</p><p>The Lean library data set was obtained by converting <strong>.olean</strong> files into s-expressions (see the <a href="https://github.com/andrejbauer/lean2sexp"><strong>lean2sexp</strong></a> tool).</p><p>The Agda data sets were obtained with an <a href="https://github.com/andrejbauer/agda/tree/master-sexp">s-expression extension</a> of the official Agda repository (use either master-sexp or release-2.6.3-sexp branch).</p><p>For more details, see our <a href="https://arxiv.org/abs/2310.16005"><strong>arXiv copy</strong></a><strong> </strong>of the paper.</p><h3>Directory structure</h3><p>First, the <strong>mlfmf.zip</strong> archive needs to be unzipped. It contains a separate directory for every library (for example, the standard library of Agda can be found in the stdlib directory) and some auxiliary files. Every library directory contains</p><ul><li>the <strong>network file</strong> from which the heterogeneous network can be loaded,</li><li>a zip of the <strong>entries directory</strong> that contains (many) files with abstract syntax trees. Each of those files describes a single entry of the library.</li></ul><p>In addition to the auxiliary files which are used for loading the data (and described below), the zipped sources of lean2sexp and Agda s-expression extension are present.</p><h4>Loading the data</h4><p>In addition to the data files, there is also a simple python script <strong>main.py</strong> for loading the data. To run it, you will have to install the packages listed in the file <strong>requirements.txt</strong>: <strong>tqdm</strong> and <strong>networkx</strong>. The easiest way to do so is calling <i><strong>pip install -r requirements.txt</strong></i>.</p><p>When running <strong>main.py </strong>for the first time, the script will unzip the entry files into the directory named <strong>entries</strong>. After that, the script loads the syntax trees of the entries (see the <strong>Entry</strong> class) and the network (as <i>networkx.MultiDiGraph</i> object).</p><p><i>Note. The entry files have extension <strong>.dag </strong>(directed acyclic graph), since Lean uses node sharing, which breaks the tree structure (a shared node has more than one parent node).</i></p><h3>More information</h3><p>For more information about the <strong>data collection process</strong>, <strong>detailed data (and data format) description</strong>, and <strong>baseline experiments</strong> that were already performed with these data, see our <a href="https://arxiv.org/abs/2310.16005"><strong>arXiv copy</strong></a><strong> of the paper</strong>.</p><p>For the code that was used to perform the experiments and data format description, visit our github repository <a href="https://github.com/ul-fmf/mlfmf-data"><strong>https://github.com/ul-fmf/mlfmf-data.</strong></a></p><h3>Funding</h3><p>Since not all the funders are available in the Zenodo's database, we list them here:</p><ol><li>This material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-21-1-0024.</li><li>The authors also acknowledge the financial support of the Slovenian Research Agency via the research core funding No. P2-0103 and No. P1-0294.</li></ol><p> </p>
RRID Gold Set of annotations for software tools in the biomedical literature
<p>This data is a subset of a larger human curated, machine assisted gold standard data set of RRID citations within the text of the scientific literature. The full set is accessible via Hypothes.is at https://hypothes.is/users/scibot?q=group%3A__world__ and via individual RRID records such as RRID:SCR_016250 https://scicrunch.org/resolver/SCR_016250/mentions?q=&i=rrid:scr_016250 </p><p>The data is based on authors who added RRIDs into their manuscripts. The data was then extracted by SciBot (RRID:SCR_016250), into Hypothes.is (RRID:SCR_000430) and then manually checked by a curator to determine if the author and the database agreed. The list of annotators is available in Hypothes.is user group: SciBotCurationGroup.</p><p>There are 78,140 rows and each row contains an annotation (annotation id, URI), linked to a paper (paper identifiers: PMID, DOI, PMC) and linked to the RRID (scr_id, exact, text_quote_selector). </p><p>A second spreadsheet contains a list of 8,322 software tools from the SciCrunch Registry (available here https://scicrunch.org/resources/data/source/nlx_144509-1/search ), enhanced by additions by thousands of individual authors, and curated over 10 years (Ozyurt et al., 2016). </p><p>The third spreadsheet contains a data dictionary and links to related ontologies, and tagging sets. </p><p> </p><p> </p>
Coordinates and checklists of alien species populations as obtained from the DASCO workflow and the SInAS data set
<p>This data set contains coordinate records of alien (i.e., non-native) species populations worldwide and aggregated checklists of alien species for individual regions. The regions consists of non-overlapping polygons representing countries, sub-national or coastal marine ecoregions. </p><p>The data set was produced by applying the DASCO workflow (https://doi.org/10.5281/zenodo.5841930) using the SInAS database (version 2.5; https://doi.org/10.5281/zenodo.10038256). The workflow imports checklists of alien species such as those stored in SInAS, and extracts coordinates for the alien regions (according to SInAS) from GBIF and OBIS. After cleaning and thinning the coordinates, the workflow exports a list of coordinates of alien populations for all species included in SInAS and with records on GBIF or OBIS.</p><p>These files are part of a manuscript published in the journal Neobiota, where the workflow is described in detail (Seebens & Kaplan 2022, https://doi.org/10.3897/neobiota.74.81082).</p><p>DASCO_AlienCoordinates_SInAS_2.5.gz contains the coordinates of alien populations.</p><p>DASCO_AlienRegions_SInAS_2.5.csv contains the checklists of alien species per region. Note that this only includes species with GBIF and OBIS records. For more comprehensive checklists, other databases such as those listed here (https://doi.org/10.5281/zenodo.10038256) should be consulted.</p><p>OBIS_SpeciesKeys_SInAS_2.5.csv contains the species keys from OBIS.</p><p>GBIF_SpeciesKeys_SInAS_2.5.csv contains the species keys from GBIF.</p><p>DASCO_TaxonHabitats_SInAS_2.5.csv contains habitat information for individual species if available from WoRMS, Fishbase or Sealifebase (used to identify marine species).</p><p>The file DASCO_ListOriginalGBIFData_keys_SInAS_2.5.csv contains the DOIs of the originally downloaded files from GBIF, which provides the basis for the generation of the GBIF part (ie. the DASCO workflow was applied to these data sets from GBIF). Note that OBIS does not provide a DOI for downloads, and thus we cannot provide this.</p>
Parker Solar Probe Reconnection Exhaust Data Set for Close Encounters 4-11
<p>The data set contained in the ascii text files consist of the current sheet (CS) time intervals and the associated parameters of identified magnetic reconnection exhausts across CSs recorded by the Parker Solar Probe within R<0.26 AU from the Sun for close encounters 4-11. A detailed description is available in a manuscript submitted to The Astrophysical Journal by Eriksson et al. [2024] entitled "Parker Solar Probe Observations of Magnetic Reconnection Exhausts in Quiescent Plasmas Near the Sun".</p><p>The ascii data file zenodo_psp_ce_lmn_cs_output_sorted_tctr.txt contains a center time of the optimized Walen relation (tc, column 1) and columns 2-3 show the CS start (CS1) and end (CS2) times. This is followed by the time duration dtcs in seconds (column 4) and whether a SPAN-I instrument survey (s=sf00) or burst (a=af00) data product exist (column 5). The RTN components of the adjacent time averaged magnetic field before (B1_R, B1_T, B1_N) and after (B2_R, B2_T, B2_N) the CS then follows in columns 6-11 and a corresponding magnetic field rotation angle (Bshear1) at the CS (column 12). The CP dL, dM, dN columns 13-15 list the distances covered along the hybrid-LMN coordinate system based in a Cross-Product normal to the CS in terms of the average ion inertial length diavg (column 16), which is based on a non-corrected proton density (Np1+Np2)/2=Npavg (columns 17-19). The subsequent columns 20-28 are the adjacent VL,VM,VN on the two sides of the CS from the MVAB LMN system as well as their averages (VL1+VL2)=VLavg, (VM1+VM2)/2=VMavg and (VN1+VN2)/2=VNavg. This is followed (columns 29-37) by the corresponding VL,VM,VN and averages in the hybrid-LMN system using the cross-product (CP) normal. The adjacent average total magnetic field Btot1 and Btot2 (columns 38-39) and the LMN components of the magnetic field in the MVAB system [BL1, BL2], [BM1, BM2], [BN1, BN2] in columns 40-45 are followed by the corresponding magnetic fields in the CP hybrid-LMN system (columns 46-51). The ratio of the intermediate to minimum eigenvalue ratio is included (column 52) followed by the unit vectors of the MVAB system L=[L_R, L_T, L_N], M=[M_R, M_T, M_N], and N=[N_R, N_T, N_N] in columns 53-61. The times adjacent to the CS used to obtain this set of MVAB eigenvectors is found in columns 62-63 (MVAB UT1 and MVAB UT2). The magnetic fields (B1 and B2) used to obtain the local hybrid-LMN system relative these MVAB times relatively farther from the CS are associated with a rotation angle (Bshear2) in column 64. The local hybrid-LMN system from the CP normal is then listed in columns 65-73 LCP=[L_R, L_T, L_N], MCP=[M_R, M_T, M_N], NCP=[N_R, N_T, N_N]. The angle (degrees) between the MVAB N and NCP is listed in column 74 (Nmv*Ncp) followed by the last two columns 75-76 that contain the time interval (s) relative CS1 and CS2 to obtain average plasma data (dext) and magentic field data (bext).</p><p>A letter "m" right before column 1 flags the five events in this list of 236 events when the magnetic field did not change sign across the assumed CS. A letter "d" marks the 10 events associated with two opposite (double) exhausts.</p><p>The ascii data file zenodo_psp_ce_lmn_cs_output_sorted_qtn.txt contains the same current sheet (CS) start (CS1) and end (CS2) times (columns 1-2). This file also lists the adjacent average proton (non-corrected) density in columns 3-4 as in the first (tctr) file which is followed by the average proton temperatures (Tp1 and Tp2 in columns 5-6), solar wind speed (Vtot1 and Vtot2 in columns 7-8) and the hybrid system L-components of the velocity (VL1 and VL2 in columns 9-10). Columns 11-16 contain the minimum and maximum values within each CS of the non-correct proton density, proton temperature and VL component. Column 17 lists the daily median density ratio from the electron density (Ne) and the non-corrected proton density (Np) where Ne is obtained through quasi-thermal noise spectroscopy by Kruparova et al. (2023). Column 18 lists the average of this daily ratio. Column 19 lists a value fc. A value larger than 1.0 indicates that a CS event time period is corrected to a local Ne value using Npfc=(Ne/Np)*Np*fc, where Np is a non-corrected proton density, Ne/Np is the median of the daily Ne/Np ratio.</p><p>The ascii data file zenodo_psp_ce_lmn_cs_output_sorted_positions.txt contains the Parker Solar Probe median of its radial position within each CS1-CS2 time period in both solar radius and astronomical unit.</p><p>The PDF figures psp_ceXX_apj_plots_qtn_final.pdf for XX=04,05,06,07,08,09,10 and 11 contain all CS events with a reconnection exhaust for each close encounter 04-11 on the basis of the agreement with a Walen prediction. Each plot shows the pitch-angle distribution (0-180 degrees) of the supra-thermal energy flux, proton temperature (MK), proton density (black) corrected to a daily median ratio Ne/Np as Npcorr=(Ne/Np)*Np, where Ne (red dots) is obtained from a QTN analysis by Kruparova et al. (2023), L-components of the magnetic field and proton velocity with a red trace showing a Walen prediction to this VL across the CS, the M and N components of the magnetic field with BM shifted by its time-period average, and the M and N components of the proton velocity with VM shifted by its time-period average. The LMN unit vectors of the employed hybrid LMN system are shown below each plot for reference.</p><p>Finally, the eight ~11-day overview plots for each close encounter 4-11 marks the center times (tc) of each confirmed exhaust interval in this study of 231 events (red vertical dotted line) with the five marginal events marked as a black vertical dotted line. The panels from the top show the pitch-angle distribution (0-180 degrees) of the supra-thermal energy flux, proton temperature (MK), magnetic field strength, R-components of B and V, N-components of B and V, and the radial postion of Parker Solar Probe in terms of the solar radius. Here, R and N are two of the three RTN system components of B and V.</p>
A challenging data set for evaluating part-of-speech taggers
<p>This data set contains 2,227 sentences, with a part-of-speech (POS) tag specified for a single word in the sentence. The data file is a tab-separated text file where each row (after the header row) is formatted as follows:</p><p><i>sentence <TAB> POS tag <TAB> (optional) motivation</i></p><p>Note that, in the sentence (= a string of space-separated characters), the POS-tagged word is indicated by the POS tag in brackets, placed just after the word to which it refers. Example:</p><p><i>The road bends [VERB] to the right . VERB</i></p><p>In this example, the optional motivation is not included, as the tagged word can easily be identified as being a verb.</p>
4D cone beam computed tomography phantom data set
<p>This data set accompanies the following Medical Physics publication: <a href="https://doi.org/10.1002/mp.14441"><i>Madesta, F., Sentker, T., Gauer, T., & Werner, R. (2020). Self‐contained deep learning‐based boosting of 4D cone‐beam CT reconstruction. Medical Physics, 47(11), 5619-5631</i></a><i>.</i></p><p>It comprises 6 time-resolved (4D) cone-beam computed tomography scans with the following scan configurations:</p><ul><li>4D CBCT Scanner: Varian TrueBeam (the detailed scan geometry and further details can be found in Scan.xml included in each scan)</li><li>Phantom: <a href="https://www.cirsinc.com/products/radiation-therapy/dynamic-thorax-motion-phantom/">Dynamic Thorax Phantom: Model 008A</a></li><li>The following motion patterns are included:<ol><li>SI amplitude of insert: ±10mm, pattern: sin, period: 5.0s</li><li>SI amplitude of insert: ±10mm, pattern: cos**4, period: 5.0s</li><li>SI amplitude of insert: ±10mm, pattern: sin, period: 2.5s</li><li>SI amplitude of insert: ±10mm, pattern: cos**4, period: 2.5s</li><li>SI amplitude of insert: ±10mm, pattern: sin, period: 7.5s</li><li>SI amplitude of insert: ±10mm, pattern: cos**4, period: 7.5s</li></ol></li></ul>
RDP Classifier 2.14 and the RDP bacterial and archaeal taxonomy training set No. 19
<p>RDP Classifier 2.14 (August 2023) Release Note:</p><p>The Bacteria and Archaea hierarchy model used by RDP Classifier has been updated to training set No. 19. The new version has over 600 genera and 2500 species added since last version No. 18 released in July 2020. The information that is used to update the RDP taxonomy to training set version No. 19, and RDP Classifier version 2.14 came from publicly available scientific articles and public sequence repository, mostly from International Journal of Systematic and Evolutionary Microbiology (IJSEM), the All-Species Living Tree Project (LTP) and GenBank. </p><p>It is worth noting that most of the phyla have new names, according to article "</p><p>Oren A, Garrity GM. Valid publication of the names of forty-two phyla of prokaryotes. Int J Syst Evol Microbiol. 2021 Oct;71(10). doi: 10.1099/ijsem.0.005056. PMID: 34694987."</p><p>In addition to the files to train and run the RDP Classifier, new file formats are made available to accommodate the needs of users:</p><p>1. A new file trainset19_072023_speciesrank.fa has been added to the release in RDPClassifier_16S_trainsetNo19_rawtrainingdata.zip. This file is NOT needed to train the classifier. In addition to sequences, it contains genus, species, strain, type status and taxonomy rank, which are useful for closest species identification using third-party tools (e.g. BLAST).</p><p>2. Two new files in RDPClassifier_16S_trainsetNo19_QiimeFormat.zip to retrain the RDP Classifier included in Qiime2 package.</p>
An updated modular set of synthetic spectral energy distributions for young stellar objects
<p>These are the models released with the following publication:</p> <p><strong><em>An updated modular set of synthetic spectral energy distributions for young stellar objects</em></strong> (<a href="https://ui.adsabs.harvard.edu/abs/2024ApJ...961..188R/abstract" target="_blank" rel="noopener">Richardson et al. 2024</a>).</p> <p>This is a set of young stellar object (YSO) models with associated spectral energy distributions (SEDs) calculated through radiative transfer. It is a significant update to the data published alongside Robitaille (2017, R17). It contains the parameters shaping each model and adds the newly calculated parameters of envelope mass, average dust temperature, disk stability, and line-of-sight extinction. It also makes explicit quantities, such as source luminosity, that were left implicit in the previous release. This set also convolves the SEDs with several new filters, primarily those on the James Webb Space Telescope, and adds a script to facilitate convolution of these models with additional filters as desired by users. All data included in Version 1.1 of the R17 set (the most recent) are included here.</p> <p>Like their predecessors, these models are versioned. Updates will be released as more models are completed or other changes are made.</p> <p>Files unzip to r+24_models-{version}/{geometry}. "files.tar.gz" contains scripts for SED convolution and main sequence comparison, the opacity to absorption of dust used in the radiative transfer calculations, main sequence T/L values used for results in the accompanying work, and reference material for the contents of the dataset and latest version.</p> <p>The primary use of these models is as templates for SED fitting. The R17 models were structured for use with the <a href="https://sedfitter.readthedocs.io/en/stable/" target="_blank" rel="noopener">sedfitter</a> python package, which enables fitting and analysis of the fit results. For a version of sedfitter which accommodates the new additions, use <a href="https://github.com/richardson-t/sedfitter/tree/dev" target="_blank" rel="noopener">this fork</a>.</p>
RESONANCES Database : Experiment no. 1 — VR Settings
<p>This dataset is an output of the RESONANCES physiological signal-based testing protocol aimed at recognizing and measuring atmospheric emotions. It contains the virtual reality (VR) settings designed to carry out the experiment: a dark, blue, amber, and bright corridor. The training configuration is helpful to teach participants to memorize the protocol, become familiar with the VR technology, and interact with the questionnaire interface.</p> <p>This dataset is made of 7 files:<br>no. 1 dataset summary (.pdf)<br>no. 1 instructions sheet (.txt)<br>no. 5 zipped folders (.zip) including files to run the VR settings (00_Corridor_Training; 01_Corridor_Dark; 02_Corridor_Amber; 03_Corridor_Blue; 04_Corridor_Bright)</p>
CURE Companion Data Set
<p>Companion dataset to superabsorbent polymer manufacture case study described in paper “<span>A framework for early-stage sustainability assessment of innovation projects enabled by weighted sum multi-criteria decision analysis in the presence of uncertainty”.</span></p>
Identifying the interplay between protective measures and settings on the SARS-CoV-2 transmission using a Bayesian network [Dataset]
<p>data07B.csv: dataset for the study of the SARS-CoV-2 transmission.</p> <p>CPTNetica.txt: conditional probabilities tables of each variable given through Netica once the BN obtained in R code is loaded.</p> <p>code01.R: code to learn structure and parameters of the SARS-CoV-2 BN model.</p>
BTSbot v10 training set
<p>This is the production version of the BTSbot training set, limited to public (programid=1) ZTF alerts. BTSbot is a multi-modal convolutional neural network designed for real-time identification bright extragalactic transients in Zwicky Transient Facility (ZTF) data. BTSbot provides a bright transient score to individual ZTF detections using their image data and 25 extracted features. BTSbot is able to eliminate the need for daily visual inspection of new transients by automatically identifying and requesting spectroscopic follow-up observations of new bright transient candidates. </p> <p>The training data is split into two zipped files. metadata_v10.zip contains alert packet features for the alerts in the train, validation, and test splits stored a separate .csv files. images_v10.zip contains the corresponding image cutouts stored as three .npy files. The BTSbot source code contains routines for reading these files and training a model on them. They can also easily be loaded with pandas.read_csv() and numpy.load().</p> <p>This training set data is necessary for reproducing the results of the BTSbot study, although this dataset only contains ZTF public data while the production BTSbot model also trained on ZTF partnership data.</p> <p>If you use reference this data or BTSbot please cite the <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240115167R/abstract" target="_blank" rel="noopener">BTSbot paper</a>.</p> <p> </p>
Series production data set for 5-axis CNC milling
<p>The data set described encompasses features extracted from the machine control of a five-axis milling machine across thirteen series of productions. Each production series entails a setup changeover to prepare the machine for another product type. Alongside timestamps and twenty features derived from Numerical Control (NC) variables, the data set includes labels denoting various production phases. These labels, up to 23 in total, are structured around a generalized milling process. Comprising thirteen .csv files, each corresponding to a series production, the dataset was gathered within a production company operating in the contract manufacturing sector. These components are tied to actual series orders within ongoing industrial production.</p> <p>The complete description of the data set is published here: <a href="https://www.mdpi.com/2306-5729/9/5/66" target="_blank" rel="noopener">https://doi.org/10.3390/data9050066</a></p>
DCASE 2024 Task 5: Few-shot Bioacoustic Event Detection Development Set
<p><strong>General Description:</strong></p> <p>The development set for task 5 of DCASE 2024 "Few-shot Bioacoustic Event Detection" consists of 217 audio files acquired from different bioacoustic sources. The dataset is split into training and validation sets. </p> <p>Multi-class annotations are provided for the training set with positive (POS), negative (NEG) and unkwown (UNK) values for each class. UNK indicates uncertainty about a class. </p> <p>Single-class (class of interest) annotations are provided for the validation set, with events marked as positive (POS) or unkwown (UNK) provided for the class of interest. </p> <p><strong>Folder Structure:</strong></p> <p><em>Development_set.zip</em></p> <p>|_Development_Set/</p> <p> |__Training_Set/</p> <p> |___JD/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___HT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___BV/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___MT/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___WMW/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p> |__Validation_Set/</p> <p> |___HB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PB/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___ME/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PB24/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___RD/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> |___PW/</p> <p> |____*.wav</p> <p> |____*.csv</p> <p> </p> <p><em>Development_set_annotations.zip</em> has the same structure but contains only the *.csv files</p> <p> </p> <p><strong>Dataset statistics</strong></p> <p>Some statistics on this dataset are as follows, split between training and validation set and their sub-folders:</p> <p>-----------------------------------------------------<br>TRAINING SET<br>-----------------------------------------------------<br>Number of audio recordings | 174<br>Total duration | 21 hours<br>Total classes | 47<br>Total events | 14229<br>-----------------------------------------------------<br>TRAINING SET/BV<br>-----------------------------------------------------<br>Number of audio recordings | 5<br>Total duration | 10 hours<br>Total classes | 11<br>Total events | 9026<br>Sampling rate | 24000 Hz<br>-----------------------------------------------------<br>TRAINING SET/HT<br>-----------------------------------------------------<br>Number of audio recordings | 5<br>Total duration | 5 hours<br>Total classes | 5<br>Total events | 611<br>Sampling rate | 6000 Hz<br>-----------------------------------------------------<br>TRAINING SET/JD<br>-----------------------------------------------------<br>Number of audio recordings | 1<br>Total duration | 10 mins<br>Total classes | 1<br>Total events | 357<br>Sampling rate | 22050 Hz<br>-----------------------------------------------------<br>TRAINING SET/MT<br>-----------------------------------------------------<br>Number of audio recordings | 2<br>Total duration | 1 hour and 10 mins<br>Total classes | 4<br>Total events | 1294<br>Sampling rate | 8000 Hz<br>-----------------------------------------------------<br>TRAINING SET/WMW<br>-----------------------------------------------------<br>Number of audio recordings | 161<br>Total duration | 4 hours and 40 mins<br>Total classes | 26<br>Total events | 2941<br>Sampling rate | various sampling rates<br>-----------------------------------------------------</p> <p>-----------------------------------------------------<br>VALIDATION SET<br>-----------------------------------------------------<br>Number of audio recordings | 43<br>Total duration | 49 hours and 57 minutes<br>Total classes | 7<br>Total events | 3504<br>-----------------------------------------------------<br>VALIDATION SET/HB<br>-----------------------------------------------------<br>Number of audio recordings | 10<br>Total duration | 2 hours and 38 minutes<br>Total classes | 1<br>Total events | 712<br>Sampling rate | 44100 Hz<br>-----------------------------------------------------<br>VALIDATION SET/PB<br>-----------------------------------------------------<br>Number of audio recordings | 6<br>Total duration | 3 hours<br>Total classes | 2<br>Total events | 292<br>Sampling rate | 44100 Hz<br>-----------------------------------------------------<br>VALIDATION SET/ME<br>-----------------------------------------------------<br>Number of audio recordings | 2<br>Total duration | 20 minutes<br>Total classes | 2<br>Total events | 73<br>Sampling rate | 44100 Hz<br>-----------------------------------------------------<br>VALIDATION SET/PB24<br>-----------------------------------------------------<br>Number of audio recordings | 4<br>Total duration | 2 hours<br>Total classes | 2<br>Total events | 350<br>Sampling rate | 44100 Hz<br>-----------------------------------------------------<br>VALIDATION SET/RD<br>-----------------------------------------------------<br>Number of audio recordings | 6<br>Total duration | 18 hours<br>Total classes | 1<br>Total events | 1372<br>Sampling rate | 48000 Hz<br>-----------------------------------------------------<br>VALIDATION SET/PW<br>-----------------------------------------------------<br>Number of audio recordings | 15<br>Total duration | 24 hours<br>Total classes | 1<br>Total events | 705<br>Sampling rate | 96000 Hz<br>-----------------------------------------------------</p> <p><strong>Annotation structure</strong></p> <p>Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows:</p> <p>TRAINING SET<br>---------------------<br>Audiofilename, Starttime, Endtime, CLASS_1, CLASS_2, ...CLASS_N</p> <p>VALIDATION SET<br>---------------------<br>Audiofilename, Starttime, Endtime, Q</p> <p> </p> <p><strong>Classes</strong></p> <p>DCASE2024_task5_training_set_classes.csv and DCASE2024_task5_validation_set_classes.csv provide a table with class code correspondence to class name for all classes in the Development set. Additionally, DCASE2024_task5_validation_set_classes.csv also provides a recording names column.</p> <p>DCASE2024_task5_training_set_classes.csv<br>---------------------<br>dataset, class_code, class_name</p> <p>DCASE2024_task5_validation_set_classes.csv<br>---------------------<br>dataset, recording, class_code, class_name</p> <p> </p> <p><strong>Evaluation Set</strong></p> <p>The Evaluation set for this task will be released on the 1 June 2024</p> <p><strong>Open Access:</strong></p> <p>This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.<br> </p> <p><strong>Contact info:</strong></p> <p>Please send any feedback or questions to:</p> <p>Burooj Ghani - burooj.ghani@naturalis.nl | Ines Nolasco - i.dealmeidanolasco@qmul.ac.uk</p> <p>Alternately, join us on Slack: <a href="https://join.slack.com/t/dcase/shared_invite/zt-12zfa5kw0-dD41gVaPU3EZTCAw1mHTCA">task-fewshot-bio-sed</a></p> <p> </p>
A Resilient Workflow to Control a Biomedical HPC Simulation in an Urgent Computing Setting
<p><span><span><span><span>We demonstrate a resilient workflow enabled by the LEXIS Platform, running a time- and safety-critical biomedical simulation of virtual stent placement in intracranial arteries using the HemoFlow application. The workflow, as captured on the video, gracefully handles failures of single computing steps or entire computing systems and thus lends itself to urgent computing applications. <br><br><span><span>The concept of this workflow has potential for realising ab-initio computational biomedical simulations which can provide live, targeted guidance to surgeons.</span></span></span></span></span></span></p>
mouse GSE199308 scRNA data set objects
<p>scRNA data from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE199308 (Huang et al. 2023), see a detailed description of the study here: https://atlas.gs.washington.edu/mmca_v2/public/about.html</p> <p>Data were downloaded from https://atlas.gs.washington.edu/mmca_v2/public/download.html to create an AnnData (h5ad) file with meta data to be able to analyse with e.g. python scanpy package.</p> <p>If you use this data, please cite Huang et al. 2023.</p>
Maximum Independent Set Satellite Scheduling World Cities Data Set
<h1>Satellite Scheduling World Cities Data Set</h1> <p>The Satellite Scheduling World Cities Data Set is the a set of cities treated as point locations used to simulate a set of image collection tasking requests for AIAA paper "A Maximum Independent Set Method for Scheduling Earth-Observing Satellite Constellations". It provides an open reference and benchmark for the satellite task scheduling problem. This could also be considered as a sparse Maximum Independent Set problem for a generic graph. The requests represent point collects, from which we can compute multiple distinct collection opportunities. The tasking problem is then to select a subset of these collects that it is possible for the spacecraft to feasibly collect in a given time period, subject to constraints on the spacecraft's agility and constraints on only collecting a single collect per request (no duplication of effort).<br><br>The data set is hosted on both <a href="https://github.com/duncaneddy/aiaa-mis-satellite-scheduling-dataset">Github</a> and <a href="../">Zenodo</a>. The Github repository contains the original source data, the associated requests generated from the source data, and scripts to reproduce the scenario files. Zenodo (DOI 10.5281/zenodo) hosts copies of the output Metis graph files and collect data files. Due to the large size of produced files these are not included in the Github repository.</p> <h2>Notes</h2> <p><strong>Notes</strong><br><br>Please note that while the source data and generation methods are identical to the satellite task planning paper it was created for. The specific generated problems do not exactly reproduce the scenario in the paper. Since the original reproduction, updates in upstream software dependencies have changed the output of the generation process (specifically, Earth orientaiton parameter handling libraries). This can be determined by considering the cardinality of the generated collect set. However, these differences are generally small and since the constriant rate is similar, the results should be comparable.</p> <table> <tbody> <tr> <td>Spacecraft Count</td> <td>Orignial Publication Collect Count</td> <td>Reproduction Collect Count</td> </tr> <tr> <td>4</td> <td>59356</td> <td>59624</td> </tr> <tr> <td>6</td> <td>90777</td> <td>91204</td> </tr> <tr> <td>12</td> <td>180008</td> <td>180939</td> </tr> <tr> <td>24</td> <td>359170</td> <td>361519</td> </tr> </tbody> </table> <p><br>This repository also adds additional scenarios for 1, 2, and 36 satellites. Note, the provided scenarios represent the largest 10,000 request data set. Should a smaller request set be desired, the requests should be filtered to the top `x` request based on city population and any collects not associated with those requests should be discarded.</p> <p>Note the Zenodo repository excludes the collect and graph files for the 1 and 2 satellite scenarios to avoid the file limits. These can still be reproduced from the Github source code.</p> <h2>Acknolwedgement</h2> <p>If this data set is used in your research, please cite the following paper</p> <p><a href="https://arc.aiaa.org/doi/abs/10.2514/1.A34931">A Maximum Independent Set Method for Scheduling Earth-Observing Satellite Constellations</a></p> <blockquote> <pre><code>@article{eddy2021maximum, title={A Maximum Independent Set Method for Scheduling Earth-Observing Satellite Constellations}, author={Eddy, Duncan and Kochenderfer, Mykel J}, journal={Journal of Spacecraft and Rockets}, volume={58}, number={5}, pages={1416--1429}, year={2021}, publisher={American Institute of Aeronautics and Astronautics} }</code></pre> </blockquote> <h2>Licensing</h2> <p>The source of the world cities data is from the <a href="https://simplemaps.com/data/world-cities">simplemaps.com</a> website,<br>licensed under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License </a>with the specific license found at `./data/worldcities_license.txt`.</p>
JR100 Expedition 379T Site J1002 beryllium isotope, XRF element count and carbon isotope data sets
<h2>JR100 Expedition 379T Site J1002 beryllium isotope, XRF element count and carbon isotope data sets (Finalised 8th of April 2024)</h2> <h3>How to cite these data:</h3> <p>The full data were published in Sproson <em>et al.</em>, 2024.</p> <p>Sproson AD, Yokoyama Y, Miyairi Y, Aze T, Clementi VJ, Riechelson H, Bova SC, Rosenthal Y, Childress LB & Expedition 379T Scientists. Near-synchronous Northern Hemisphere and Patagonian ice sheet variation over the last glacial cycle. <em>Nature Geoscience</em> <a href="https://doi.org/10.1038/s41561-024-01436-y">https://doi.org/10.1038/s41561-024-01436-y</a> (2024).</p> <h3>Files:</h3> <p><strong>Supplementary Table 1: </strong>Multiple linear regression results between 10Be/9Be ratios and sedimentation rate, K/Ca, Fe/Ca, Al/Ti (this study), Green/Blue (Li <em>et al.</em>, 2022), and Global Mean Sea Level (Lambeck <em>et al.</em>, 2014). The multiple linear regression was calculated using the MATLAB(R) function “regress”.</p> <p><strong>Supplementary Table 2: </strong> Age-depth model and beryllium isotope measurements for Site J1002. The age-depth model was calculated from radiocarbon dates and oxygen isotope stratigraphy (Li <em>et al.</em>, 2022) using the BIGMACS modelling routine (Lee <em>et al.</em>, 2022). Beryllium-9 and beryllium-10 were measured by Adam D. Sproson by HR-ICP-MS and AMS at the Atmosphere and Ocean Research Institute (Sproson <em>et al.</em>, 2021) and University of Tokyo (Matsuzaki et al., 2007), respectively. 10Be/9Be* ratios were corrected for 10Be paleo-production following von Blanckenburg <em>et al.</em> (2015). </p> <p><strong>Supplementary Table 3:</strong> X-ray Fluorescence Ti, K, Fe, Ca, and Al element counts per second for Site J1002 measured at the Lamont-Doherty Earth Observatory by Vincent J. Clementi.</p> <p><strong>Supplementary Table 4: </strong>Carbon isotope measurements for the benthic foraminifera, U. peregrina, measured at Rutgers University by Vincent J. Clementi.</p> <h3>Format:</h3> <p>Depth (m CCSF-A) = core composite depth below seafloor.</p> <p>Calendar age (kyr BP) = age in thousand years before present.</p> <p>[10Be]reac, [9Be]reac = the concentration of 10Be and 9Be in the reactive phase of marine sediments. </p> <p>Sample ID = expedition sample designation specifying hole (e.g., A), core number (e.g., 1), type (i.e., H), section number (e.g., 1), and then section half (i.e., W).</p> <p>σ = standard deviation.</p> <h3>References:</h3> <p>Lambeck K, Rouby H, Purcell A, Sun Y, Sambridge M. Sea level and global ice volumes from the Last Glacial Maximum to the Holocene. <em>Proceedings of the National Academy of Sciences.</em> 2014;111(43):15296-15303. </p> <p>Lee T, Rand D, Lisiecki LE, Gebbie G, Lawrence CE. Bayesian age models and stacks: Combining age inferences from radiocarbon and benthic δ18O stratigraphic alignment. <em>EGUsphere.</em> 2022;2022:1-29.</p> <p>Li C, Clementi VJ, Bova SC, <em>et al.</em> The sediment green‐blue color ratio as a proxy for biogenic silica productivity along the Chilean Margin. <em>Geochemistry, Geophysics, Geosystems</em>. 2022:e2022GC010350. </p> <p>Matsuzaki H, Nakano C, Tsuchiya Y, <em>et al.</em> Multi-nuclide AMS performances at MALT. <em>Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms.</em> 2007;259(1):36-40. </p> <p>Sproson AD, Aze T, Behrens B, Yokoyama Y. Initial measurement of beryllium‐9 using high‐resolution inductively coupled plasma mass spectrometry allows for more precise applications of the beryllium isotope system within the Earth Sciences. <em>Rapid Communications in Mass Spectrometry.</em> 2021;35(8):e9059. </p> <p>Von Blanckenburg F, Bouchez J, Ibarra DE, Maher K. Stable runoff and weathering fluxes into the oceans over Quaternary climate cycles. <em>Nature Geoscience. </em>2015;8(7):538-542. </p>
Additional Artifacts - Supplements to: A Resilient Workflow to Control a Biomedical HPC Simulation in an Urgent Computing Setting
<p>In this dataset, we have collected supplementary artifacts to support an understanding of the workflow presented in the submission cited (see related identifiers).</p> <p>These artifacts are (cf. README.md in the main folder of the tar.gz archive):</p> <p>A1: modified HemoFlow code (cf. https://github.com/gzavo/hemoflow) for our workflow experiments (subfolder "hemoflowcfd");<br>A2: workflow descriptions in python for Apache Airflow (subfolder "workflow");<br>A3: inputs (.xml/.npz) and output (.txt) for the example (subfolder "case").</p> <p> </p>
Data set of a thermal response test on a planar trench collector
<p>This data set provides three different temperature measurements (PT100, fiber optic measurement, thermistors), as well as the determination of the volumetric water content and the bulk electrical conductivity of the subsurface. During the published period, a thermal response test was carried out. On April 28th, at 15:25, the fluid circulation began, and heat injection started at 15:38 on April 28th and continued until May 3rd. The test was conducted with a constant volume flow of 1.00 m³/h and a constant heat injection rate of 0.88 kW.</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.