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FIG. 1 in Update and observations on the extraction of ostracods (Crustacea) from the Permian hard carbonate rocks of Iran
FIG. 1. — Historical background of the Guadalupian-Lopingian successions in the northwest of Iran; following Stepanov et al. (1969), Teichert et al. (1973), Partoazar (2002), Ghaderi (2014) and Ghaderi et al. (2014a, b, 2016).
FIG. 3 in Update and observations on the extraction of ostracods (Crustacea) from the Permian hard carbonate rocks of Iran
FIG. 3. — Stratigraphic log and vertical distribution of ostracods (obtained from CH2O2 protocol) in the Khachik Formation from the Ali-Bashi section.
Figure 2. Macrobrachium tenellum adult male who underwent a second spermatophore extraction using the electrostimulation technique. A in Sperm viability in wild-caught males of Macrobrachium tenellum (Smith, 1871) (Decapoda: Caridea: Palaemonidae) fed with different diets
Figure 2. Macrobrachium tenellum adult male who underwent a second spermatophore extraction using the electrostimulation technique. A= The dark brown, melanized spermatophore is different from that observed in healthy males.
FIG. 5 in Update and observations on the extraction of ostracods (Crustacea) from the Permian hard carbonate rocks of Iran
FIG. 5. — Ostracods from the Khachik beds of Ali-Bashi section, NW Iran, extracted by CH2O2 technique (A1-J2) and by CH3COOH technique (K1-K3): A1- A6, Bairdia deducta deducta (Zalányi, 1974); A1, right lateral view, sample A. 197, MNHN.F.F72157; A2-A4, right lateral view, sample A. 210, MNHN.F.F72158- F72160; A5, right lateral view, sample A. 189, MNHN.F.F72161; A6, right lateral view, sample A. 207, MNHN.F.F72162; B1-B4, Bairdia hungarica Zalányi, 1974; B1, left lateral view, sample A. 189, MNHN.F.F72163; B2, right lateral view, sample A. 189, MNHN.F.F72164; B3, left lateral view, sample A.170, MNHN.F.F72165; B4, right lateral view, sample A. 176, MNHN.F.F72166; C1-C4, Bairdia sp.; C1, left lateral view, sample A. 189, MNHN.F.F72167; C2, right lateral view, sample A. 189, MNHN.F.F72168; C3, left lateral view, sample A.170, MNHN.F.F72169; C4, right lateral view, sample A. 176, MNHN.F.F72170; D1-D6, Fabalicypris parva Wang, 1978; D1, right lateral view, sample A. 194, MNHN.F.F72171; D2, right lateral view, sample A. 151, MNHN.F.F72172; D3, right lateral view, sample A. 158, MNHN.F.F72173; D4, right lateral view, sample A. 192, MNHN.F.F72174; D5, D6, right lateral view, sample A. 210, MNHN.F.F72175, F72176; E, Fabalicypris sp. 1., right lateral view, sample A. 207, MNHN.F.F72177; F, Fabalicypris sp. 2., right lateral view, sample A. 210, MNHN.F.F72178; G1-G3, Hollinella (Hollinella) herrickana (Girty, 1909); G1, left lateral view, sample A. 15, MNHN.F.F72179; G2, left lateral view, sample A. 238, MNHN.F.F72180; G3, right lateral view, sample A. 238, MNHN.F.F72181; H, Hollinella sp., left lateral view, sample A. 188, MNHN.F.F72182; I, Sargentina transita (Kozur, 1985), left lateral view, sample A. 176, MNHN.F.F72183; J1, J2, Silenites sp.; J1, right lateral view, sample A. 238, MNHN.F.F72184; J2, ventral view, sample A. 238, MNHN.F.F72185; K1-K3, Ostracoda indet. Specimens are stored in the MNHN (Muséum national d'Histoire naturelle) collections (Paris, France). Scale bar: 100 µm.
Figs. 1 A-H in Phytotoxic activity of extracts obtained from cagaita (Eugenia dysenterica DC. - Myrtaceae) on the growth of black-jack (Bidens pilosa L.)
Figs. 1 A-H. Effect of different leaf and stem extracts of Eugenia dysenterica DC. on the radicle growth (mm) of Bidens pilosa L. seeds. Means followed by the same letter in the column do not differ from each other by the Tukey test at 5% probability. A. Leaf Aqueous Extract (LAE); B. Leaf Ethanolic Extract (LEE); C. Leaf Hydroalcoholic Extract 70:30 (LHE 70:30); D. Leaf Hydroalcoholic Extract 50:50 (LHE 50:50), E. Stem aqueous extract (SAE); F. Stem ethanolic extract (SEE), G. Stem hydroalcoholic extract 70:30 (SHE 70:30); H. Stem hydroalcoholic extract 50:50 (SHE 50:50).
Figs. 2 A-H in Phytotoxic activity of extracts obtained from cagaita (Eugenia dysenterica DC. - Myrtaceae) on the growth of black-jack (Bidens pilosa L.)
Figs. 2 A-H. Effect of different leaf and stem extracts of Eugenia dysenterica DC. on the hypocotyl growth (mm) of Bidens pilosa L. seeds. Means followed by the same letter in the column do not differ from each other by the Tukey test at 5% probability. A. Leaf Aqueous Extract (LAE); B. Leaf Ethanolic Extract (LEE); C. Leaf Hydroalcoholic Extract 70:30 (LHE 70:30); D. Leaf Hydroalcoholic Extract 50:50 (LHE 50:50); E. Stem aqueous extract (SAE); F. Stem ethanolic extract (SEE); G. Stem hydroalcoholic extract 70:30 (SHE 70:30); H. Stem hydroalcoholic extract 50:50 (SHE 50:50).
BioPropaPhenKG on Multi-Relation Extraction Methods on Online Newspapers
<p>The coronavirus disease (COVID-19) spread rampantly around the world at the beginning of 2020 before the governments of each country could prevent it by making decisions based on medical data analysis. With proper formalization, the terabytes of new textual data available online every day could have been used for the early description and detection of cases of this virus. Since then, the number of Event-Based Surveillance (EBS) applications has increased exponentially. These applications aim to mine channels of unstructured data to detect signs of possible public health events. However, one problem with such systems is the need for expert intervention to define which event will be captured, which relevant terms should be used in the search, and to analyze the events to modify the search procedure constantly. Another problem is that many of these applications do not consider both spatial and temporal characteristics. Addressing such limitations, this article presents a novel approach. We propose the biomedical domain specialization of the Core Propagation Phenomenon Ontology (PropaPhen) to capture spatiotemporal characteristics of the propagation of health-related phenomena. We also propose the Description-Detection-Framework (DDF), which leverages PropaPhen, UMLS, and OpenStreetMaps to detect new medical events automatically. Finally, we demonstrate a use case with experiments on extracts from online newspapers about COVID-19. The results show that DDF can be useful for detecting clusters of suspicious cases of possible emerging health-related phenomena.</p> <p>BioPropaPhenKG, its ontology and other useful information can be found in <a href="../records/10911980">https://zenodo.org/records/10911980</a>. The code used for this use case can be found in <a href="https://github.com/Gabriel382/DDPF-Health-Risks">https://github.com/Gabriel382/DDPF-Health-Risks</a> . Finally, the datasets used where UMLS MetamorphoSys, OpenStreetMaps, Wikidata, <a href="https://aylien.com/blog/free-coronavirus-news-dataset">Aylien</a> (data only from November of 2019).</p> <p> </p> <p>To read, you just need to load it with Neo4j:4.4.3. Alternatively, you can open it with docker using the following command: </p> <p>docker run --interactive --tty --rm \<br> --publish=7474:7474 --publish=7687:7687 \<br> --volume=/path-to-data-folder:/data --user="$(id -u):$(id -g)"\<br> neo4j:4.4.3 \<br>neo4j-admin load --from=/data/BioPropaPhenKG-Journal-MultiRE.dump --database "neo4j" --force</p>
3D Printed Bigel: A Novel Delivery System for Cannabidiol-Rich Hemp Extract
<h2>Abstract</h2> <div>The therapeutic potential of <span>Cannabis sativa</span> L. extract has gained significant attention due to its diverse medical applications. Sublingual administration remains a common delivery method of cannabinoids; however, challenges often arise due to the inconvenient form of the extract and its taste. To address these issues, a novel bigel formulation was developed, combining water and oil phases to enhance stability and bioavailability. This formulation incorporates a cannabidiol-rich hemp extract, hyaluronic acid for its moisturizing properties, and a taste-masking agent to improve patient compliance and comfort. Using a standardized hemp extract rich in cannabinoids and a well-characterized terpene profile, the printability of the bigels was evaluated through 3D printing technology. A printout with known cannabidiol (CBD) and cannabidiolic acid (CBDA) content of 11.613 mg ± 0.192 of CBD and 4.732 mg ± 0.280 of CBDA in the printout was obtained. In addition, the release profile of CBD and CBDA was evaluated to determine the delivery efficiency of the active ingredient—dissolved active ingredient levels ranged from 74.84% ± 0.50 to 80.87% ± 3.20 for CBD and from 80.84 ± 1.33 to 98.31 ± 1.70 for CBDA depending on the formulation. Rheological studies were conducted to evaluate the viscosity of the bigels under varying temperature conditions, ensuring their stability and usability. Findings suggest that this 3D-printed bigel formulation could significantly enhance the delivery of cannabis extracts, offering a more convenient and effective therapeutic option for patients. This research underscores the importance of innovation in cannabinoid therapies and paves the way for further advancements in personalized medicine.</div>
MultiPosture: A Dataset of body joints keypoints extracted using MediaPipe for multi-task sitting posture recognition with upper and lower body labels
<p>This dataset contains skeletal pose data extracted from video recordings of 13 participants performing various sitting postures in home environments. The data was processed using MediaPipe Pose Heavy model and includes 4,800 frames of 3D skeletal coordinates (x, y, z) for 11 key body joints, with each frame manually labeled for both upper and lower body posture classifications.</p> <p>The data is stored in CSV format with normalized coordinates relative to hip center, containing 33 input dimensions (11 joints × 3 coordinates) representing key skeletal points. To protect participant privacy, only the processed skeletal coordinates are included, with no raw video or image data due to privacy constraints.<br><br></p> <p>Upper Body Labels:</p> <ul> <li>TUP: Upright trunk position</li> <li>TLB: Trunk leaning backward</li> <li>TLF: Trunk leaning forward</li> <li>TLR: Trunk leaning right</li> <li>TLL: Trunk leaning left</li> </ul> <p>Lower Body Labels:</p> <ul> <li>LAP: Legs apart</li> <li>LWA: Legs wide apart</li> <li>LCS: Legs closed</li> <li>LCR: Legs crossed right over left</li> <li>LCL: Legs crossed left over right</li> <li>LLR: Legs lateral right</li> <li>LLL: Legs lateral left</li> </ul> <p>Each frame in the dataset has been manually labeled and validated by experts, making it particularly suitable for developing and evaluating machine learning models for ergonomic monitoring systems, ambient assisted living applications, and general posture recognition research.</p> <p>This dataset was collected as part of the study:</p> <p><strong>D. Carneros-Prado, L. Cabañero-Gómez, E. Johnson, I. González, J. Fontecha and R. Hervás, "A Comparison Between Multilayer Perceptrons and Kolmogorov-Arnold Networks for Multi-Task Classification in Sitting Posture Recognition," in <em>IEEE Access</em>, vol. 12, pp. 180198-180209, 2024, doi: 10.1109/ACCESS.2024.3510034. </strong><em><a href="https://ieeexplore.ieee.org/abstract/document/10772228" target="_blank" rel="noopener">link</a></em></p> <p> </p>
Data Extracted for the Systematic Literature Review on Non-profit Open data Intermediaries and their effects on Open data Usability Barriers
<p>The dataset contains the data extracted from the literature for the Systematic Literature Review and is referenced or used in the extended abstract titled "How do Non-profit Open data Intermediaries enhance Open data Usability? A Systematic Literature Review", submitted to the 18th International Symposium on Open Collaboration (Companion), September 6–10, 2022, Madrid, Spain. <a href="https://doi.org/10.1145/3555051.3555061" target="_blank" rel="noopener">https://doi.org/10.1145/3555051.3555061</a> </p>
Monthly averaged lightning data extracted from 1-year EMAC simulation including LCC-lightning (between 1 March, 2017 and 28 February, 2018, T42L90MA resolution).
<p>About Dataset<br> Monthly averaged lightning data extracted from 1-year EMAC simulation (between 1 March, 2017 and 28 February, 2018, T42L90MA resolution).</p> <p>Authors: Francisco J. Perez-Invernon, Heidi Huntrieser, Patrick Joeckel and Francisco J. Gordillo-Vazquez</p> <p>Description of the data<br> P_cth.tar: Lightning parameterization based on cloud top height. Scaling factor is applied.<br> L_cth.tar: Lightning parameterization based on cloud top height and modified over the oceans. A scaling factor of 0.571 have to be applied.<br> G_updr: Lightning parameterization based on updraft velocity. Scaling factor is applied.<br> A_prec: Lightning parameterization based on convective precipitation. A scaling factor of 0.76 have to be applied.<br> A_updr: Lightning parameterization based on Updraft strength at 440~hPa. A scaling factor of 0.01618 have to be applied.<br> P_cth + A_prec: Lightning parameterization based on cloud top height and updraft velocity. A scaling factor of 1.13 have to be applied.</p> <p>File format: netcdf</p> <p> Example:<br> <br> netcdf LCC_2017_______20170201_0000_mmlb_PaR_T {<br> dimensions:<br> time = UNLIMITED ; // (1 currently)<br> lon = 128 ;<br> lat = 64 ;<br> tbnds = 2 ;<br> variables:<br> double time(time) ;<br> time:long_name = "time" ;<br> time:bounds = "time_bnds" ;<br> time:units = "day since 2017-01-01 00:00:00" ;<br> time:calendar = "gregorian" ;<br> double YYYYMMDD(time) ;<br> YYYYMMDD:long_name = "time" ;<br> YYYYMMDD:units = "days as %Y%m%d.%f" ;<br> YYYYMMDD:calendar = "gregorian" ;<br> double dt(time) ;<br> dt:long_name = "delta_time" ;<br> dt:units = "s" ;<br> double nstep(time) ;<br> nstep:long_name = "current time step" ;<br> float lon(lon) ;<br> lon:long_name = "longitude" ;<br> lon:units = "degrees_east" ;<br> float lat(lat) ;<br> lat:long_name = "latitude" ;<br> lat:units = "degrees_north" ;<br> float aps(time, lat, lon) ;<br> aps:long_name = "surface pressure" ;<br> aps:units = "Pa" ;<br> aps:representation = "GP_2D_HORIZONTAL" ;<br> aps:grid_type = "gaussian" ;<br> aps:table = 128 ;<br> aps:code = 134 ;<br> aps:REFERENCE_TO = "g3b: aps" ;<br> aps:coordinates = "lon lat" ;<br> aps:cell_methods = "time: point" ;<br> float aps_ave(time, lat, lon) ;<br> aps_ave:long_name = "surface pressure" ;<br> aps_ave:units = "Pa" ;<br> aps_ave:representation = "GP_2D_HORIZONTAL" ;<br> aps_ave:grid_type = "gaussian" ;<br> aps_ave:table = 128 ;<br> aps_ave:code = 134 ;<br> aps_ave:REFERENCE_TO = "g3b: aps" ;<br> aps_ave:coordinates = "lon lat" ;<br> aps_ave:cell_methods = "time: mean" ;<br> float fpscg(time, lat, lon) ;<br> fpscg:long_name = "CG flash frequency" ;<br> fpscg:units = "1/s" ;<br> fpscg:REFERENCE_TO = "lnox_PaR_T_gp: fpscg" ;<br> fpscg:coordinates = "lon lat" ;<br> fpscg:cell_methods = "time: point" ;<br> float fpscg_ave(time, lat, lon) ;<br> fpscg_ave:long_name = "CG flash frequency" ;<br> fpscg_ave:units = "1/s" ;<br> fpscg_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpscg" ;<br> fpscg_ave:coordinates = "lon lat" ;<br> fpscg_ave:cell_methods = "time: mean" ;<br> float fpsic(time, lat, lon) ;<br> fpsic:long_name = "IC flash frequency" ;<br> fpsic:units = "1/s" ;<br> fpsic:REFERENCE_TO = "lnox_PaR_T_gp: fpsic" ;<br> fpsic:coordinates = "lon lat" ;<br> fpsic:cell_methods = "time: point" ;<br> float fpsic_ave(time, lat, lon) ;<br> fpsic_ave:long_name = "IC flash frequency" ;<br> fpsic_ave:units = "1/s" ;<br> fpsic_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsic" ;<br> fpsic_ave:coordinates = "lon lat" ;<br> fpsic_ave:cell_methods = "time: mean" ;<br> float fpsm2cg(time, lat, lon) ;<br> fpsm2cg:long_name = "CG flash density" ;<br> fpsm2cg:units = "1/s/m2" ;<br> fpsm2cg:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2cg" ;<br> fpsm2cg:coordinates = "lon lat" ;<br> fpsm2cg:cell_methods = "time: point" ;<br> float fpsm2cg_ave(time, lat, lon) ;<br> fpsm2cg_ave:long_name = "CG flash density" ;<br> fpsm2cg_ave:units = "1/s/m2" ;<br> fpsm2cg_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2cg" ;<br> fpsm2cg_ave:coordinates = "lon lat" ;<br> fpsm2cg_ave:cell_methods = "time: mean" ;<br> float fpsm2ic(time, lat, lon) ;<br> fpsm2ic:long_name = "IC flash density" ;<br> fpsm2ic:units = "1/s/m2" ;<br> fpsm2ic:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2ic" ;<br> fpsm2ic:coordinates = "lon lat" ;<br> fpsm2ic:cell_methods = "time: point" ;<br> float fpsm2ic_ave(time, lat, lon) ;<br> fpsm2ic_ave:long_name = "IC flash density" ;<br> fpsm2ic_ave:units = "1/s/m2" ;<br> fpsm2ic_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2ic" ;<br> fpsm2ic_ave:coordinates = "lon lat" ;<br> fpsm2ic_ave:cell_methods = "time: mean" ;<br> float fpslcc10(time, lat, lon) ;<br> fpslcc10:long_name = "LCC(>10 ms) flash frequency" ;<br> fpslcc10:units = "1/s" ;<br> fpslcc10:REFERENCE_TO = "lnox_PaR_T_gp: fpslcc10" ;<br> fpslcc10:coordinates = "lon lat" ;<br> fpslcc10:cell_methods = "time: point" ;<br> float fpslcc10_ave(time, lat, lon) ;<br> fpslcc10_ave:long_name = "LCC(>10 ms) flash frequency" ;<br> fpslcc10_ave:units = "1/s" ;<br> fpslcc10_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpslcc10" ;<br> fpslcc10_ave:coordinates = "lon lat" ;<br> fpslcc10_ave:cell_methods = "time: mean" ;<br> float fpslcc20(time, lat, lon) ;<br> fpslcc20:long_name = "LCC(>20 ms) flash frequency" ;<br> fpslcc20:units = "1/s" ;<br> fpslcc20:REFERENCE_TO = "lnox_PaR_T_gp: fpslcc20" ;<br> fpslcc20:coordinates = "lon lat" ;<br> fpslcc20:cell_methods = "time: point" ;<br> float fpslcc20_ave(time, lat, lon) ;<br> fpslcc20_ave:long_name = "LCC(>20 ms) flash frequency" ;<br> fpslcc20_ave:units = "1/s" ;<br> fpslcc20_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpslcc20" ;<br> fpslcc20_ave:coordinates = "lon lat" ;<br> fpslcc20_ave:cell_methods = "time: mean" ;<br> float fpsm2lcc10(time, lat, lon) ;<br> fpsm2lcc10:long_name = "LCC(>10 ms) flash density" ;<br> fpsm2lcc10:units = "1/s/m2" ;<br> fpsm2lcc10:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2lcc10" ;<br> fpsm2lcc10:coordinates = "lon lat" ;<br> fpsm2lcc10:cell_methods = "time: point" ;<br> float fpsm2lcc10_ave(time, lat, lon) ;<br> fpsm2lcc10_ave:long_name = "LCC(>10 ms) flash density" ;<br> fpsm2lcc10_ave:units = "1/s/m2" ;<br> fpsm2lcc10_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2lcc10" ;<br> fpsm2lcc10_ave:coordinates = "lon lat" ;<br> fpsm2lcc10_ave:cell_methods = "time: mean" ;<br> float fpsm2lcc20(time, lat, lon) ;<br> fpsm2lcc20:long_name = "LCC(>20 ms) flash density" ;<br> fpsm2lcc20:units = "1/s/m2" ;<br> fpsm2lcc20:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2lcc20" ;<br> fpsm2lcc20:coordinates = "lon lat" ;<br> fpsm2lcc20:cell_methods = "time: point" ;<br> float fpsm2lcc20_ave(time, lat, lon) ;<br> fpsm2lcc20_ave:long_name = "LCC(>20 ms) flash density" ;<br> fpsm2lcc20_ave:units = "1/s/m2" ;<br> fpsm2lcc20_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2lcc20" ;<br> fpsm2lcc20_ave:coordinates = "lon lat" ;<br> fpsm2lcc20_ave:cell_methods = "time: mean" ;<br> float fpssprite(time, lat, lon) ;<br> fpssprite:long_name = "Sprites flash frequency" ;<br> fpssprite:units = "1/s" ;<br> fpssprite:REFERENCE_TO = "lnox_PaR_T_gp: fpssprite" ;<br> fpssprite:coordinates = "lon lat" ;<br> fpssprite:cell_methods = "time: point" ;<br> float fpssprite_ave(time, lat, lon) ;<br> fpssprite_ave:long_name = "Sprites flash frequency" ;<br> fpssprite_ave:units = "1/s" ;<br> fpssprite_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpssprite" ;<br> fpssprite_ave:coordinates = "lon lat" ;<br> fpssprite_ave:cell_methods = "time: mean" ;<br> float fpsm2sprite(time, lat, lon) ;<br> fpsm2sprite:long_name = "Sprites flash density" ;<br> fpsm2sprite:units = "1/s/m2" ;<br> fpsm2sprite:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2sprite" ;<br> fpsm2sprite:coordinates = "lon lat" ;<br> fpsm2sprite:cell_methods = "time: point" ;<br> float fpsm2sprite_ave(time, lat, lon) ;<br> fpsm2sprite_ave:long_name = "Sprites flash density" ;<br> fpsm2sprite_ave:units = "1/s/m2" ;<br> fpsm2sprite_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2sprite" ;<br> fpsm2sprite_ave:coordinates = "lon lat" ;<br> fpsm2sprite_ave:cell_methods = "time: mean" ;<br> float bps(time, lat, lon) ;<br> bps:long_name = "BJ flash frequency" ;<br> bps:units = "1/s" ;<br> bps:REFERENCE_TO = "bluejetbPaR_T_gp: bps" ;<br> bps:coordinates = "lon lat" ;<br> bps:cell_methods = "time: point" ;<br> float bps_ave(time, lat, lon) ;<br> bps_ave:long_name = "BJ flash frequency" ;<br> bps_ave:units = "1/s" ;<br> bps_ave:REFERENCE_TO = "bluejetbPaR_T_gp: bps" ;<br> bps_ave:coordinates = "lon lat" ;<br> bps_ave:cell_methods = "time: mean" ;<br> float bpsm2(time, lat, lon) ;<br> bpsm2:long_name = "BJ flash density" ;<br> bpsm2:units = "1/s/m2" ;<br> bpsm2:REFERENCE_TO = "bluejetbPaR_T_gp: bpsm2" ;<br> bpsm2:coordinates = "lon lat" ;<br> bpsm2:cell_methods = "time: point" ;<br> float bpsm2_ave(time, lat, lon) ;<br> bpsm2_ave:long_name = "BJ flash density" ;<br> bpsm2_ave:units = "1/s/m2" ;<br> bpsm2_ave:REFERENCE_TO = "bluejetbPaR_T_gp: bpsm2" ;<br> bpsm2_ave:coordinates = "lon lat" ;<br> bpsm2_ave:cell_methods = "time: mean" ;<br> double time_bnds(time, tbnds) ;<br> time_bnds:long_name = "time bounds" ;<br> time_bnds:units = "days since 2017-01-01T00:00:00Z" ;<br> time_bnds:cell_methods = "time: point"</p>
Zero-Shot Information Extraction to Enhance a Knowledge Graph Describing Silk Textiles - English and Spanish neighborhood sub-graphs
<p>Two language-specific sub-graphs (English and Spanish) based on the ConceptNet Knowledge Graph. These two files are required to run the code for reproducing the results reported in the paper <a href="https://aclanthology.org/2021.latechclfl-1.16/">"Zero-Shot Information Extraction to Enhancea Knowledge Graph Describing Silk Textiles"</a> at the <a href="https://sighum.wordpress.com/events/latech-clfl-2021/">LaTeCH-CLfL 2021</a> workshop co-located with <a href="https://2021.emnlp.org/">EMNLP 2021</a>.</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 2000m Y1 & Y2
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 2000m particles releases for the years 1996 (Y1) and 1997 (Y2).</p>
Using convolutional neural networks to efficiently extract immense phenological data from community science images
<p>Community science image libraries offer a massive, but largely untapped, source of observational data for phenological research. The iNaturalist platform offers a particularly rich archive, containing more than 49 million verifiable, georeferenced, open access images, encompassing seven continents and over 278,000 species. A critical limitation preventing scientists from taking full advantage of this rich data source is labor. Each image must be manually inspected and categorized by phenophase, which is both time-intensive and costly. Consequently, researchers may only be able to use a subset of the total number of images available in the database. While iNaturalist has the potential to yield enough data for high-resolution and spatially extensive studies, it requires more efficient tools for phenological data extraction. A promising solution is automation of the image annotation process using deep learning. Recent innovations in deep learning have made these open-source tools accessible to a general research audience. However, it is unknown whether deep learning tools can accurately and efficiently annotate phenophases in community science images. Here, we train a convolutional neural network (CNN) to annotate images of Alliaria petiolata into distinct phenophases from iNaturalist and compare the performance of the model with non-expert human annotators. We demonstrate that researchers can successfully employ deep learning techniques to extract phenological information from community science images. A CNN classified two-stage phenology (flowering and non-flowering) with 95.9% accuracy and classified four-stage phenology (vegetative, budding, flowering, and fruiting) with 86.4% accuracy. The overall accuracy of the CNN did not differ from humans (p = 0.383), although performance varied across phenophases. We found that a primary challenge of using deep learning for image annotation was not related to the model itself, but instead in the quality of the community science images. Up to 4% of A. petiolata images in iNaturalist were taken from an improper distance, were physically manipulated, or were digitally altered, which limited both human and machine annotators in accurately classifying phenology. Thus, we provide a list of photography guidelines that could be included in community science platforms to inform community scientists in the best practices for creating images that facilitate phenological analysis.</p>
Paired Fusion Augmented Dataset for Vehicle Extraction and Counting (Domino Dataset)
<p>This dataset try to expedite the deep learning researcher's task of a model training to extract vehicles from aerial images in an urban environment. Vehicles included in the dataset are motorcycles and cars of any type, number of wheels and color. The specific process of acquisition, enhancing, fusion and augmentation is presented. Inclusion of height of cars using a Digital Surface Model (DSM) is described and comparison of the application of a U-net segmentation model over non height and height dataset is shown.</p>
Images for fracture extraction in FRIS
<p>The dataset inlcudes clipped MOA2009 and Landsat 8 images covering FRIS of summer, 2014-2015, which are applied for fraction extraction in FRIS.</p>
PubMed-OA-Extraction-dataset
<p>This is the train-test-validation dataset for pubmed open-access articles keyphrase extraction task. The small_* file contains the all articles that have 5to 25 extractive keyphrases (keyphrase in the article that is inside the abstract of the article).</p>
CH4 isotopic signatures of emissions from oil and gas extraction sites in Romania
<p>Dataset linked to the manuscript "CH4 isotopic signatures of emissions from oil and gas extraction sites in Romania", submitted to Elementa: Science of the Anthropocene.</p> <p>Abstract: Methane (CH4) emissions to the atmosphere from the oil and gas sector in Romania remain highly uncertain, despite their relevance for the European Union’s goals to reduce greenhouse gas emissions. Measurements of the isotopic composition of CH4 can be used for source attribution, which is important in top-down studies of emissions from extended areas. We performed isotope measurements of CH4 in atmospheric air samples collected from an aircraft (24 locations) and ground vehicles (83 locations), around oil and gas production sites in Romania, with focus on the Romanian Plain. Ethane to methane ratios (C2:C1) were derived at 412 locations of the same fossil fuel activity clusters. The resulting isotopic signals (δ13C and δ2H in CH4) covered a wide range of values, indicating mainly thermogenic gas sources (associated with oil production) in the Romanian Plain, mostly in Prahova county (δ13C from -67.8 to -22.4 per mille V-PDB; δ2H from -255 to -138 per mille V-SMOW) but also the presence of some natural gas reservoirs of microbial origin in Dolj, Ialomia, Prahova and likely Teleorman counties. The classification based on C2:C1 ratios was generally in agreement with the one based on CH4 isotopic composition, and confirmed the characterisation of the gas origin. In several cases, the CH4 enhancements sampled from the aircraft could directly be linked to the underlying production clusters using wind data. The combination of δ13C and δ2H signals determined on these samples confirm that the oil and gas production sector is the main source of CH4 emissions in the target areas.</p>
An annotated corpus of clinical trial publications supporting schema-based relational information extraction
<p>Repository of an annotated corpus of clinical trial abstracts supporting schema-based relational information extraction and the code for the inter-annotation agreement calculation and the baseline information extraction method.</p>
Collection of literature and extracted features in the field of under-frequency load shedding for the period 1954-2020
<p>The number of publications in the field of UFLS research is rapidly increasing. This confirms that the topic is timely, but due to the amount of publications, it is easy to lose track of the prevailing concepts driving the latest technologies. To overcome this problem, researchers resort to subjective review articles in the literature in which individual authors attempt to systematically categorize UFLS algorithms according to their own understanding of a variety of approaches. Since this is not exactly a trivial task and is undoubtedly subject to personal interpretation, it is better to use specialized mathematical techniques for this purpose. Recently, it has been shown that the use of clustering techniques and graph theory can be a useful tool for systematic literature reviews.</p> <p>If one chooses to search for similarities between UFLS algorithms using such techniques, one must first collect the relevant literature in the field and extract important information. Therefore, this collection provides an Excel spreadsheet (.xlsx) of 381 publications in the field of UFLS protection for the period from 1954 to 2020, with the following information for each publication:</p> <ol> <li><em>authors</em>,</li> <li><em>title</em>,</li> <li><em>year</em> of publication (journal, conference proceedings, doctoral dissertation, master's thesis, bachelor's thesis, other),</li> <li>the <em>source</em> from which we obtained the publication,</li> <li><em>DOI</em> (Digital Object Identifier),</li> <li>extracted <em>general features</em>, and</li> <li>extracted <em>specific features</em>.</li> </ol> <p>Relevant literature was obtained from various open access journals, impact factor journals, repositories of various universities, and archives of various electric utilities and transmission system operators.</p> <p>If the publication in the row can be assigned to the feature specified in column I3-BJ3, the corresponding cell contains the value "1". Otherwise, i.e., if the feature specified in column I3-BJ3 cannot be assigned to the publication, the cell is empty. Detailed descriptions of the individual feature can be found in "Description_of_features.docx". Namely, two identical documents are available, one in English and one in Slovene.</p> <p>Updated versions with more recent data will be uploaded with a differing version number and doi.</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.