Find research datasets worth reusing

Fig. 8 in Post-extinction brachiopod faunas from the Late Permian Wuchiapingian coal series of South China

Fig. 8. Reconstruction of ventral interior of Perigeyella costellata Wang, 1955 (based on specimen NMV P309620).

Fig. 5 in Post-extinction brachiopod faunas from the Late Permian Wuchiapingian coal series of South China

Fig. 5. Abundance of the Edriosteges poyangensis–Spinomarginifera lopingensis Association. A total of 51 specimens were collected from a shell bed at the basal Lungtan Formation.

Fig. 3 in Post-extinction brachiopod faunas from the Late Permian Wuchiapingian coal series of South China

Fig. 3. Correlations of the described faunas with previously published brachiopod assemblages from South China. The dashed lines indicate that the zonal boundaries are not well defined.

Fig. 2 in Post-extinction brachiopod faunas from the Late Permian Wuchiapingian coal series of South China

Fig. 2. Composite stratigraphy of the Lungtan Formation at the Daijiagou and Chuanmu sections, showing lithology, microfacies and fossil horizons. A. Daijiagou section. B. Chuanmu section. Lithology: 1, coarse sandstone; 2, alternating mudstone and sandstone; 3, siltstone; 4, fine sandstone; 5, clay; 6, coal seam; 7, cherty limestone; 8, bioclastic limestone; 9, limestone. Microfacies: 1, low−angle cross bedding; 2, wavy bedding; 3, bimodal cross bedding; 4, brackish water ironstone concretions; 5, black shale; 6, coal seam; 7, sandy mudstone; 8, siltstone; 9, quartz sandstone; 10, alternating siliceous shale and cherty limestone; 11, sparite; 12, grainstone−packstone; 13, roots (coal seats); 14, weathered clay; 15, cherty limestone.

opencc-by-4.0Oct 2023View details →

UniSpec: Deep Learning for Predicting the Full Range of Peptide Fragment Ion Series to Enhance the Proteomics Data Analysis Workflow

<p>UniSpec is a comprehensive DL spectrum predictor that can predict the intensity of the entire HCD MS/MS fragment ion series, going beyond existing tools limited to b/y ion series. </p> <p>All datasets developed for UniSpec model are shared on Zenodo as part of the UniSpec publication, "UniSpec: Deep Learning for Predicting Comprehensive Peptide Fragment Ion Series to Improve Peptide-Spectrum Matches from Shotgun Proteomics Experiments".</p> <p>This includes UniSpec datasets, downstream evaluation and analysis, and application case studies.</p> <p>1. pre-processed training, evaluation and testing data for machine learning;</p> <p>         UniSpec-Datasets.7z, Readme_UniSpecDatasets.txt</p> <p>2. Streamlined  input datasets based on the fragmentation dictionary;</p> <p>        Streamlined_inputdatasets.7z, Readme_Streamlined_inputdatasets.txt</p> <p>3. Predictions on the validation and test sets;</p> <p>       UniSpecPred_Validation-Test.7z, Readme_Predictons_ValidationTest.txt</p> <p>4. Evaluation by comparison with Prosit;</p> <p>      a. Predictions: prosit_and_unispec_predictions.7z, Readme_prosit_and_unispec_predictions.txt</p> <p>      b. Cosine similarity scores: prosit_vs_unispec_CS.7z, Readme_prosit_vs_unispec_CS.txt</p> <p>5. CSS for Different HCD Fragment Ion Series;</p> <p>       CS_for_ion_splits.tsv</p> <p>6. Application 1: PSM rescoring;</p> <p>      PSM rescoring_zipfiles.7z,  PSM rescoring_readme.txt</p> <p>7. Application 2: In-silico spectral library search  </p> <p>      in-silico_librarysearch.7z, in-silico_librarysearch_readme.txt</p> <p> </p>

EBEC-MicroED: Static electron diffraction movies collected at different incident flux on a direct electron detector (DE Apollo) on crystals of (S,S) Jacobsen's salen ligand and Co(II) porphyrin, and diffraction tilt series recorded on the DE Apollo and CetaD detector for the same crystals of Jacobsen's Ligand

<p>This record contains static diffraction movies recorded from crystals of (S,S) Jacobsen's salen ligand, and crystals of Co(II) meso-tetraphenyl porphyrin, using a direct electron detector (DE Apollo) in counting mode. Data were acquired at varying different incident flux settings, referred to as "spotsize11" or "spot11" (0.01 electrons per square Angstoms per second),  "spotsize10" or "spot10" (0.03 electrons per square Angstrom per second),  "spotsize9" or "spot9" (0.045 electrons per square angstrom per second)", and "spotsize8" or "spot8" (0.084 electrons per sqaure Angstrom per second. For each compound these trials, the same crystal ("crystal1", "crystal2", etc.) was conserved across a dose series, and illuminated at each incident flux from lowest to highest in sequence.</p> <p>Additionally, this record contains diffraction tilt series acquired from crystals of (S,S) Jacobsen's ligand, first on the Ceta D and next on the DE Apollo, rotating at 2 degrees per second with an incident flux of either 0.01 or 0.045 electrons per square Angstrom per second.</p> <p>All data is saved in mrc file format, with the exception of movies from the Ceta D, which are saved in ser file format.</p>

Pressure time series for waves on shelf

<div>Time: in UTC</div> <div>location: N 28° 52.077’ W 90° 29.466’</div> <div>Units:   Pressure (dbar); Sea Pressure (dbar); depth (m)</div> <div>Instrument: RBRvirtuoso pressure sensor</div> <div>Data type: 4Hz ASCII data directly converted from the raw data with no additional user processing.</div> <div>PI: Chunyan Li</div> <div>Funded by NSF (1736713)</div> <div> </div>

EBEC-MicroED: Electron diffraction tilt series recorded on crystals of thiostrepton at 100 K using a direct electron detector (DE Apollo)

<p>This record contains diffraction tilt series recorded from crystals of thiostrepton using a direct electron detector (DE Apollo) in counting mode. Incident flux and stage rotation rate are varied, for a total of three data collection settings giving variable total electron beam fluence. The keywords "fast" and "slow" in the titles of the dataset file indicates that the stage was rotated at either 2 degrees/second, or 0.33 degrees/second, respectively. Data were additionally acquired at two different incident flu settings, referred to as "spotsize11" or "spot11" (0.01 electrons per square Angstoms per second) and "spotsize9" or "spot9" (0.045 electrons per square angstrom per second)"</p> <p>The keyword "clfix" in certain file names indicates that these datasets are versions of the correspondingly named dataset file with an adjustment to pixel values to correct for coincidence loss applied to the frames.</p> <p>All data is saved in mrc file format.</p>

EBEC-MicroED: Electron diffraction tilt series recorded on crystals of (S,S) Jacobsen's salen ligand at 293 K using a direct electron detector (DE Apollo)

<p>This record contains diffraction tilt series recorded from crystals of (S,S) Jacobsen's salen ligand using a direct electron detector (DE Apollo) in counting mode. Incident flux and stage rotation rate are varied, for a total of three data collection settings giving variable total electron beam fluence. The keywords "fast" and "slow" in the titles of the dataset file indicates that the stage was rotated at either 2 degrees/second, or 0.33 degrees/second, respectively. Data were additionally acquired at two different incident flu settings, referred to as "spotsize11" or "spot11" (0.01 electrons per square Angstoms per second) and "spotsize9" or "spot9" (0.045 electrons per square angstrom per second)"</p> <p>All data is saved in mrc file format.</p>

EBEC-MicroED: Electron diffraction tilt series recorded on crystals of biotin at 100 K using a direct electron detector (DE Apollo)

<p>This record contains diffraction tilt series recorded from crystals of biotin using a direct electron detector (DE Apollo) in counting mode. Incident flux and stage rotation rate are varied, for a total of three data collection settings giving variable total electron beam fluence. The keywords "fast" and "slow" in the titles of the dataset file indicates that the stage was rotated at either 2 degrees/second, or 0.33 degrees/second, respectively. Data were additionally acquired at two different incident flu settings, referred to as "spotsize11" or "spot11" (0.01 electrons per square Angstomr per second) and "spotsize9" or "spot9" (0.045 electrons per square angstrom per second)"</p> <p>All data is saved in mrc file format.</p>

Pressure time series for waves on shelf south of Timbalier Island

<div>Time: in UTC</div> <div>Position 90º32.00’W 29º3.20’N</div> <div>Platform CSI 5</div> <div>Station ioos:station:WAVCIS:CSI05</div> <div>Description S. of Timbalier Island, LA </div> <div>Units:   Pressure (dbar); Sea Pressure (dbar); depth (m)</div> <div>Instrument: RBRvirtuoso pressure sensor</div> <div>Data type: 4Hz ASCII data directly converted from the raw data with no additional user processing.</div> <div>Time offset: 578073773077 millisecond + 5 hours (due to setup error, this time offset must be added) which gives time in UTC.</div> <div> </div> <p> </p>

opencc-by-nc-4.0Sep 2024View details →

Lithium-Ion Battery Field Data: 28 LFP battery systems with 8 cells in series, up to 5 years of operation

<div> <div> <div>This data set contains data from 28 portable 24V lithium iron phosphate (LFP) battery systems with approximately 160Ah nominal capacity. Each system's specific use case is unknown, but battery systems of this size are typically used as power sources for recreational vehicles, solar energy storage, and more.</div> <br> <div>All battery systems in this data set showed some form of unsatisfactory behavior and were returned to the manufacturer. Many reasons can cause a consumer to return a battery to the manufacturer for maintenance. The user's individual decisions may be motivated by personal judgment, BMS warnings, or customer support advice. This data set comprises a very small fraction of batteries sold of this version. Therefore, this data set is biased and not representative of the operational data of the entire population of this system version. An improved version replaced this battery system type. The battery system manufacturer provided the data set for this study and allowed its open-source release under the condition of anonymity.</div> <br> <div>Each battery system consists of 8 prismatic cells in series. Each system has one load current sensor, and each cell has one voltage sensor. The four temperature sensors are placed between adjacent cells, i.e., each temperature sensor is shared by two cells. Furthermore, the battery systems have active cell balancing. The available measurements vary from a single month to five years. Consequently, the number of data rows per system varies from several thousand to millions, depending on the duration of battery operation. The data set contains a total of 133 million rows of measurements.</div> </div> </div> <div> </div> <div> <div><strong>Associated Python Library</strong></div> <div>BattGP <a href="https://github.com/JoachimSchaeffer/BattGP" target="_blank" rel="noopener">https://github.com/JoachimSchaeffer/BattGP</a></div> <div>This library contains classes and functions to analyze the data set with Gaussian processes.</div> <div>Furthermore, data visualization functions are part of the library.</div> <div><br><strong>Associated Article<br></strong>Gaussian Process-based Online Health Monitoring and Fault Analysis of Lithium-Ion Battery Systems from Field Data</div> <div>Cell Report Physical Science <br> <div><a href="https://doi.org/10.1016/j.xcrp.2024.102258">https://doi.org/10.1016/j.xcrp.2024.102258</a></div> <div> </div> <div> </div> </div> </div>

Public Procurement: a systemic approach to sustainability and technology – SAPIENS Network Webinar Series

<p>On the 5th of June 2024, the SAPIENS Network Early Stage Researchers <a href="https://sapiensnetwork.eu/research/early-stage-researcher-projects/sap-in-construction/">Alexandru Buftic</a> (Babeș-Bolyai University), <a href="https://sapiensnetwork.eu/research/early-stage-researcher-projects/delivering-sap-through-collaboration/">Felippe Vilaça</a> (University of Gävle), <a href="https://sapiensnetwork.eu/research/early-stage-researcher-projects/measuring-lcc/">Haitham Ghaida</a> (Hasselt University) and <a href="https://sapiensnetwork.eu/research/early-stage-researcher-projects/industry-4-for-sp/">Nadia Sava</a> (Babeș-Bolyai University) delivered a scientific webinar on the topic of <em>Public Procurement: a systemic approach to sustainability and technology</em>.</p> <p>The webinar was moderated by <a href="https://sapiensnetwork.eu/dacian-dragos/">Dacian C. Dragoș</a>, Professor of Administrative and European Law with the Public Administration and Management Department at Babes-Bolyai University, <a href="https://sapiensnetwork.eu/sebastien-lizin/">Sebastien Lizin</a>, Assistant Professor at the Faculty of Business Economics and vice-chair of the Department of Economics at Hasselt University and <a href="https://www.linkedin.com/in/roxana-vornicu-phd-49470347/">Roxana Vornicu</a>, PhD, Senior Lecturer at King´s College London, Centre of Construction Law, and Managing Partner of Sirbu & Vornicu Law.</p> <p>The webinar tackled various current challenges related to sustainability in public procurement. The presentations covered different topics, united by the concepts of sustainability and digitalisation in public procurement. The speakers highlighed the value of taking a systemic approach to sustainability in public procurement, the role of monitoring green and sustainable considerations in the process, the importance of EU legislation in the uptake of sustainability as well as effect of life cycle assessment reserch for sustainable building design. </p>

LoRa signal quality and GPS positioning time series dataset

<p>This time series dataset contains measurements taken by moving devices equipped with LoRa communication and GPS positioning capabilities. The measurements have been captured at the Sálvora Archipelago in Galicia (Spain), which belong to the Atlantic Islands of Galicia National Park, in which we have deployed three LoRa gateways at the following locations (latitude, longitude, altitute):</p> <ul> <li>Gateway 1 (42.46972, -9.01345, 73)</li> <li>Gateway 2 (42.49955, -9.00654, 5)</li> <li>Gateway 3 (42.50893, -9.04902, 31)</li> </ul> <p>The dataset is provided as a single comma-separated values (CSV) file, with the following data in each line:</p> <ul> <li>Device identification</li> <li>Received signal strength indicator (RSSI) from LoRa Gateway 1</li> <li>Signal-to-noise ratio (SNR) from Gateway 1</li> <li>RSSI from LoRa Gateway 2</li> <li>SNR from Gateway 2</li> <li>RSSI from LoRa Gateway 3</li> <li>SNR from Gateway 3</li> <li>LoRa spreading factor</li> <li>Timestamp</li> <li>Device latitude received from GPS</li> <li>Device longitude received from GPS</li> <li>Device altitude received from GPS</li> </ul>

NYC Bike Sharing Network: Time-Series Enhanced Nodes and Edges Dataset

<p>This dataset presents a comprehensive graph representation of the New York City Bike Sharing system, structured with nodes representing stations and edges delineating trips between these stations. The dataset is distinctive in integrating dynamic properties as time series data, which are meticulously updated using historical records (csv files) and live data feeds (gbfs files) provided by<a href="https://citibikenyc.com/system-data" target="_blank" rel="noopener"> NYC Bike sharing system</a>. </p> <ul> <li> <p><strong>Nodes</strong>:</p> <ul> <li><strong>Source</strong>: Data is collected from the New York City Bike Station Information API.</li> <li><strong>Attributes</strong>: <ul> <li><strong>ID</strong>: Unique identifier for each station.</li> <li><strong>Name</strong>: Name of the station.</li> <li><strong>Capacity</strong>: Number of bikes the station can accommodate.</li> <li><strong>Short ID</strong>: A condensed identifier used internally.</li> </ul> </li> <li><strong>Time Series Data</strong>: <ul> <li>Updated every 5 minutes from the Station Status API.</li> <li>Captures changes in bike availability, recording values only when they differ from previous data points.</li> </ul> </li> </ul> </li> <li> <p><strong>Edges</strong>:</p> <ul> <li><strong>Source</strong>: Compiled from trip data provided in CSV format specific to NYC Bike Sharing.</li> <li><strong>Attributes</strong>: <ul> <li><strong>Trip Counter</strong>: Total number of trips recorded.</li> <li><strong>Bike Type Counter</strong>: Counts trips made with electric versus classic bikes.</li> <li><strong>Trip Type Counter</strong>: Separates trips made by members versus casual riders.</li> <li><strong>Active Trips Tracker</strong>: Tracks the number of active trips at any given moment.</li> </ul> </li> <li><strong>Aggregation</strong>: Trip data between identical start and end points, in the same direction, are aggregated into a single edge, with time-series tracking the frequency of these trips.</li> </ul> </li> </ul>

opencc-zeroJan 2024View details →

Linked collectors and determiners for: Taxonomic Revision of the Ant-Acacias (Fabaceae, Mimosoideae, Acacia, Series Gummiferae) of the New World.

Natural history specimen data linked to collectors and determiners held within, "Taxonomic Revision of the Ant-Acacias (Fabaceae, Mimosoideae, Acacia, Series Gummiferae) of the New World". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/a839d8c3-a899-4278-86d2-667a5768008d">https://bionomia.net/dataset/a839d8c3-a899-4278-86d2-667a5768008d</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/a839d8c3-a899-4278-86d2-667a5768008d">https://gbif.org/dataset/a839d8c3-a899-4278-86d2-667a5768008d</a>. Formatted as a Frictionless Data package.

opencc-zeroJan 2024View details →

Linked collectors and determiners for: The Exsiccatal Series "Triebel, Microfungi exsiccati".

Natural history specimen data linked to collectors and determiners held within, "The Exsiccatal Series "Triebel, Microfungi exsiccati"". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/7ba35058-f762-11e1-a439-00145eb45e9a">https://bionomia.net/dataset/7ba35058-f762-11e1-a439-00145eb45e9a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/7ba35058-f762-11e1-a439-00145eb45e9a">https://gbif.org/dataset/7ba35058-f762-11e1-a439-00145eb45e9a</a>. Formatted as a Frictionless Data package.

opencc-by-4.0Oct 2024View details →

PECD consistent heat demand and COP time series from when2heat

<p>This dataset uses the when2heat package to compute heat demand and COP time series. The data covers the years 1979 to 2019 and the sub-national level down to a resolution grouping Europe into 96 clusters based on ERA5 reanalysis data. Thanks to this temporal scope and spatial resolution, the data is consistent with the Pan-European Climatic Database (PECD) and enables multi-year analysis of multi-sector energy systems that include the heating sector.</p> <p>The "main data" folder provides the time series with COPs for various heat pump technologies and heat demand. The heat demand data deviates from the immediate when2heat outputs in two ways. First, it aggregates the demand for space heating and warm water in commercial and residential buildings into a single time series. The compositionHeatDemand.xlsx in the "background info" folder provides the weights assumed for this aggregation. Since the composition of heat demand changes in the future, the data includes a different time series for every fifth year from 2015 to 2050. Second, we normalize each profile over the historical years from 1979 to 2019 to its maximum value. Accordingly, multiplying the time series with maximum heat demand will give an absolute demand profile.</p> <p>A subfolder in "background_info" provides shape files describing the different spatial resolutions used. The "mappingRegions.csv" specifies which region names in this dataset correspond to region names in the PECD data. The "heating_thresholds.csv" in the "background info" folder provides the assumed heating thresholds to compute the heating demand.</p>

opencc-zeroJan 2024View details →

Linked collectors and determiners for: Time series of zooplankton abundance in the Florida Keys, collected by the South Florida Program (NOAA/AOML) and the Marine Biodiversity Observation Network (MBON).

Natural history specimen data linked to collectors and determiners held within, "Time series of zooplankton abundance in the Florida Keys, collected by the South Florida Program (NOAA/AOML) and the Marine Biodiversity Observation Network (MBON)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/ec0d2fe8-21b1-4ab1-8b91-67873e8ca912">https://bionomia.net/dataset/ec0d2fe8-21b1-4ab1-8b91-67873e8ca912</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/ec0d2fe8-21b1-4ab1-8b91-67873e8ca912">https://gbif.org/dataset/ec0d2fe8-21b1-4ab1-8b91-67873e8ca912</a>. Formatted as a Frictionless Data package.