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Fig. 1 in Incidence of Spodoptera litura (Lepidoptera: Noctuidae) and its feeding potential on various citrus (Sapindales: Rutaceae) cultivars in the Sargodha Region of Pakistan
Fig. 1. Food consumption and performance of Spodoptera litura 3rd instars on 4 citrus cultivars: (A) leaf area consumption (cm2); (B) relative growth rate (RGR); (C) relative consumption rate (RCR); (D) leaf weight consumed (mg); (E) larval weight (mg); (F) weight of feces produced (mg); (G) efficiency of conversion of ingested food (ECI).
Fig. 1 in Effects of relative humidity on the vector of rose rosette disease, Phyllocoptes fructiphilus (Eriophyidae), and incidence of disease symptoms
Fig. 1. Mean (± SE) number of Phyllocoptes fructiphilus under various relative humidity regimes (A) by wk and (B) for the duration of the experiment. The same letters within a wk afer infestation or bars are not significantly different (ANOVA followed by Tukey's HSD test; α = 0.05). Where no differences were observed, no letters are included.
Fig. 2 in Effects of relative humidity on the vector of rose rosette disease, Phyllocoptes fructiphilus (Eriophyidae), and incidence of disease symptoms
Fig. 2. Mean (± SE) (A) proportion of rose rosette disease symptomatic terminals and (B) value of the Horsfall-Barratt scale on the severity of rose rosette disease. The same letters within a wk afer infestation are not significantly different (ANOVA followed by Tukey's HSD test; α = 0.05). Where no differences were observed, no letters are included.
Figure 2 in Variability Modeling of Rainfall, Deforestation, and Incidence of American Tegumentary Leishmaniasis in Orán, Argentina, 1985-2007
Figure 2. - Relation between cumulative trophic diversity and number of analyzed stomachs of Gaidropsarus guttatus from Faial Island, Azores.
Figure 3 in Variability Modeling of Rainfall, Deforestation, and Incidence of American Tegumentary Leishmaniasis in Orán, Argentina, 1985-2007
Figure 3. - Index of relative importance (%Rw) regarding the major prey items found in the stomachs of Gaidropsarus guttatus from Faial Island, Azores.
Figure 1 in Variability Modeling of Rainfall, Deforestation, and Incidence of American Tegumentary Leishmaniasis in Orán, Argentina, 1985-2007
Figure 1. - Map showing Faial, within the Azores Archipelago, NE Atlantic, and collection Gaidropsarus guttatus sites.
Figure 6 in Les engins et techniques de pêche utilisés dans la baie de Loango, République du Congo, et leurs incidences sur les prises accessoires
Figure 6. - Courbe réponse de la proportion de tortues retrouvées mortes par événement de pêche en fonction du temps de calée (en heures). L'intervalle de confiance à 95% apparaît en pointillés. +: valeurs observées. [Response curve of the proportion of sea turtles found dead per fishing event according to soaking time (in hours). The 95% confidence interval appears in dotted lines. +: observed values.]
Figure 5 in Les engins et techniques de pêche utilisés dans la baie de Loango, République du Congo, et leurs incidences sur les prises accessoires
Figure 5. - Courbe réponse du nombre de tortues capturées par événement de pêche en fonction de la taille des mailles (cm, maille étirée). L'intervalle de confiance à 95% apparaît en pointillés. +: valeurs observées. [Response curve of the number of sea turtles caught by fishing event according to mesh size (in cm mesh stretched). The 95% confidence interval appears in dotted lines. +: observed values.]
Figure 2 in Les engins et techniques de pêche utilisés dans la baie de Loango, République du Congo, et leurs incidences sur les prises accessoires
Figure 2. - Courbe réponse de la masse de poissons et crustacés (kg) en fonction de la taille des mailles (cm, maille étirée). L'intervalle de confiance à 95% apparaît en pointillés. +: valeurs observées. [Response curve of fish weight (kg) by fishing event according to mesh size (in cm mesh stretched). The 95% confidence interval appears in dotted lines. +: observed values.]
Figure 1 in Les engins et techniques de pêche utilisés dans la baie de Loango, République du Congo, et leurs incidences sur les prises accessoires
Figure 1. - Carte du littoral congolais (République du Congo) et de la zone d'étude: la baie de Loango. [Map of the Congo coastline (Republic of Congo) and of the study area: Loango Bay.]
Fig. 1 in Incidence of Delia platura (Meigen) (Diptera: Anthomyiidae) in onion and scallion crops in Mexico
Fig. 1. Delia platura. (A) Female and male, lateral view; (B) anterior segments of larva, lateral view, as = anterior spiracle; (C) larva terminalia, dorsal view, a = tubercle "a", x = tubercle "x" (according to the nomenclature of Savage et al. 2016); (D) larvae of different instars feeding on onion bulb; (E) developed larvae feeding on bulb and under the leaf tissue.
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>
LLM Service Outages and Incident Reports
<p>This dataset provides outage data and incident data of 3 LLM providers (OpenAI, Anthropic, Character.AI) across 8 LLM services (OpenAI API, ChatGPT, DALLE, Playground, Anthropic API, Claude, Console, Character.AI) collected until 2024-08-31.<br><br>Data sources:</p> <ul> <li>Outage: https://status.{service_provider}.com/uptime</li> <li>Incident Reports: https://status.{service_provider}.com/history</li> </ul> <p>service_provider = [openai, anthropic, character.ai]</p> <p>Documents:</p> <ul> <li>./raw_data/*: Raw data for outages and incidents, stored by service.</li> <li>./clean_data/*: Clean-up data for characterization, aggregated of all services.</li> </ul> <p> </p>
Human contact network analytics and COVID-19 hospital incidence in France
<p>This data set contains COVID-19 hospital incidence, temperature and human mobility and contact data recorded between 2020-03-24 and 2021-03-30 used in the paper:</p> <p>Selinger et al. 2021: Predicting COVID-19 incidence in French hospitals using human contact network analytics. 10.1016/j.ijid.2021.08.029</p> <p>See methods in the article for detailed descriptions and the data curation process.</p> <p> </p> <p><strong>1) cov_mob_tst_national.csv contains national-level data</strong><br> </p> <p>The columns comprise:</p> <p>incid_hosp: hospital admission incidence </p> <p>incid_rea: ICU admission incidence</p> <p>incid_dc: hospital death incidence </p> <p>incid_rad: incidence of those returned home</p> <p>within_departement_colocation_X%: X%-quantile of colocation probabilities with départements</p> <p>between_departement_colocation_X%: X%-quantile of colocation probabilities between départements</p> <p>fb_population_coverage_X%: X%-quantile of ratio of fb_population over census population in département</p> <p>null_links_X%: X%-quantile of null links across départements</p> <p>clustering_X%: X%-quantile of clustering coefficients across départements</p> <p>ricci_X%: X%-quantile of curvature across départements</p> <p>ricci_min_X%: X%-quantile of minimum curvature across départements</p> <p>ricci_mean_X%: X%-quantile of average curvature across départements</p> <p>ricci_max_X%: X%-quantile of maximum curvature across départements</p> <p>strength_X%: X%-quantile of network strengths across départements</p> <p>betweenness_centrality_X%: X%-quantile of betweenness_centrality scores across départements</p> <p>positive_test_ratio_weekly: ratio of weekly cumulated positive tested over weekly cumulated tests</p> <p>retail_and_recreation_percent_change_from_baseline: Google Mobility Reports</p> <p>grocery_and_pharmacy_percent_change_from_baseline: Google Mobility Reports</p> <p>parks_percent_change_from_baseline: Google Mobility Reports </p> <p>transit_stations_percent_change_from_baseline: Google Mobility Reports</p> <p>workplaces_percent_change_from_baseline: Google Mobility Reports</p> <p>residential_percent_change_from_baseline: Google Mobility Reports</p> <p>mean_temperature_X%: X% quantile of mean daily temperatures averaged over the week across départements</p> <p>min_temperature_X%: X% quantile of minimum daily temperatures averaged over the week across départements</p> <p>max_temperature_X%: X% quantile of maximum daily temperatures averaged over the week across départements</p> <p> </p> <p> </p> <p><strong>2) cov_mob_dep.csv contains département-level data</strong></p> <p>The columns comprise:</p> <p>dep: département code</p> <p>incid_hosp: hospital admission incidence </p> <p>incid_rea: ICU admission incidence</p> <p>incid_dc: hospital death incidence </p> <p>incid_rad: incidence of those returned home</p> <p>week: week (matched to colocation data recording usually on Tuesdays)</p> <p>dep_name: name of the département</p> <p>null_links: number of null links</p> <p>betweenness_centrality: betweenness centrality</p> <p>clustering: clustering coefficient</p> <p>strength: network strength</p> <p>ricci_mean: minimum curvature among all edges incident to a département</p> <p>ricci_min: mean curvature across all edges incident to a département </p> <p>ricci_X%: X%-quantile curvature among all edges incident to a département</p> <p>fb_population: number of facebook users </p> <p>facebook_colocation_within_dep: colocation probability within département</p> <p>fb_population_coverage: ratio of fb_population over census population in département</p> <p>facebook_colocation_between_dep_X%: X%-quantile of facebook colocation among all edges incident to the département</p> <p>min_temperature: minimum daily temperature averaged over the week</p> <p>max_temperature: maximum daily temperature averaged over the week</p> <p>mean_temperature: mean daily temperature averaged over the week</p> <p>incid_hosp_Y: incidence of hospital admission from Ynd most colocated département</p> <p>incid_rea_Y: incidence of ICU admission from Ynd most colocated département</p> <p>incid_dc_Y: incidence of hospital deaths from Ynd most colocated département</p> <p>incid_rad_Y: incidence of returned home from Ynd most colocated département</p> <p> </p>
Global dataset of co-incident TLS-derived and harvested tree biomass
<p>This dataset was used to producee the figures and statistics of the publication "Estimating forest aboveground biomass with terrestrial laser scanning: current status and future directions".</p> <p>This dataset contains 391 entries. Each entry is a tree that was terrestrial laser scanned and consecutively harvested to assess its aboveground biomass (AGB). AGB was also obtained from allometric scaling equations. Several ancillary tree properties such as stem diameter, foliage conditions,... and scan metadata (type of scanner, pattern) are included. We refer to the tab 'headers' for an explanation and units of the respective columns. Elaborate method descriptions can be found in the publication or in the following original publications:</p> <ul> <li>Burt, A., Boni Vicari, M., da Costa, A. C. L., Coughlin, I., Meir, P., Rowland, L., et al. (2021). New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar. Royal Society Open Science 8, 201458. doi:10.1098/rsos.201458</li> <li>Calders, K., Newnham, G., Burt, A., Murphy, S., Raumonen, P., Herold, M., et al. (2015). Nondestructive estimates of above-ground biomass using terrestrial laser scanning. Methods in Ecology and Evolution 6, 198–208. doi:10.1111/2041-210X.12301</li> <li>Demol, M., Calders, K., Krishna Moorthy, S. M., Van den Bulcke, J., Verbeeck, H., and Gielen, B. (2021). Consequences of vertical basic wood density variation on the estimation of aboveground biomass with terrestrial laser scanning. Trees 35, 671–684. doi:10.1007/s00468-020-02067-7</li> <li>Gonzalez de Tanago, J., Lau, A., Bartholomeus, H., Herold, M., Avitabile, V., Raumonen, P., et al. (2018). Estimation of above-ground biomass of large tropical trees with terrestrial LiDAR. Methods in Ecology and Evolution 9, 223–234. doi:10.1111/2041-210X.12904</li> <li>Hackenberg, J., Wassenberg, M., Spiecker, H., and Sun, D. (2015). Non destructive method for biomass prediction combining TLS derived tree volume and wood density. Forests 6, 1274–1300. doi:10.3390/ f6041274</li> <li>Kükenbrink, D., Gardi, O., Morsdorf, F., Thürig, E., Schellenberger, A., and Mathys, L. (2021). Aboveground biomass references for urban trees from terrestrial laser scanning data. Annals of Botany, 1–16doi:10.1093/aob/mcab002</li> <li>Lau, A., Calders, K., Bartholomeus, H., Martius, C., Raumonen, P., Herold, M., et al. (2019). Tree Biomass Equations from Terrestrial LiDAR: A Case Study in Guyana. Forests 10, 527. doi:10.3390/f10060527</li> <li>Momo Takoudjou, S., Ploton, P., Sonke, B., Hackenberg, J., Griffon, S., Coligny, F., et al. (2018). Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: A comparison with traditional destructive approach. Methods in Ecology and Evolution 9, 905–916.<br> doi:10.1111/2041-210X.12933</li> <li>Stovall, A. E., Vorster, A. G., Anderson, R. S., Evangelista, P. H., and Shugart, H. H. (2017). Non-destructive aboveground biomass estimation of coniferous trees using terrestrial LiDAR. Remote Sensing of Environment 200, 31–42. doi:10.1016/j.rse.2017.08.013<br> </li> </ul> <p><br> </p>
Medication and condition codes used to develop a computable phenotype for Crohn's Disease incident cases
<p>Lists of medication and condition concepts used for Crohn's disease incident case phenotyping as described in my Master's Thesis "Machine Learning Based Prediction of Incident Cases of Crohn’s Disease Using Electronic Health Records From a Large Integrated Health System".</p> <ul> <li>ibd_medication.csv contains medication names, OMOP Concept IDs, RxNorm codes and a flag indicating whether the medication is IBD-specific (i.e., antibiotics and glucocorticoides are marked as unspecific)</li> <li>ibd_conditions.csv contains condition names, OMOP Concept IDs, SNOMED CT codes and a categorical column indicating whether the condition refers to Crohn's Disease (CD), Ulcerative Colitis (UC), or Inflammatory bowel disease unclassified (IBD-U)</li> <li>ibd_symptoms.csv contains symptom names, OMOP Concept IDs, containing symptom group categories, and a flag indicating whether the symptom was added because it is a SNOMED CT descendent code of another code on the list. The list was created based on the IBD symptoms Read Code list provided by Blackwell et. al, 2021, doi:10.1093/ecco-jcc/jjaa146</li> </ul> <p>IBD, Inflammatory Bowel Disease; OMOP, Observational Medical Outcomes Partnership; SNOMED CT, Systematized Nomenclature of Medicine Clinial Terms.</p>
Low incidence of sibling cannibalism among brood parasitic cuckoo catfish embryos
<p>Brood parasites have demanding needs of host resources. Brood parasitic offspring are highly competitive and frequently cause the failure of host broods and the survival of a single parasitic offspring. Accordingly, virulent brood parasites lay single eggs in host nests to minimize multiple parasitism and ensuing sibling competition. In the cuckoo catfish (<em>Synodontis multipunctatus</em>), which parasitise mouthbrooding cichlid fishes in Lake Tanganyika, the modes of host and parasite oviposition lead to frequent cases of multiple parasitism. We experimentally tested the prediction that multiple parasitism leads to frequent siblicide. Cuckoo catfish embryos prey upon host offspring to obtain nourishment during their 3-week development in the host buccal cavity and may also consume conspecific siblings. The potential benefits of siblicide in the system are, therefore, twofold: to decrease competition for limited resources (i.e. host brood with rich yolk sacs) and to directly obtain nourishment by consuming rivals. We found that sibling cannibalism indeed provided measurable benefits in terms of increased growth of the cannibals, but sibling cannibalism was rare and typically occurred only when all host offspring had been consumed. This implies that cannibalism in the cuckoo catfish embryos emerges to mitigate starvation rather than eliminate sibling competition. </p>
Photon Showers in a High Granularity Calorimeter with Varying Incident Energy and Angle
<p>Dataset of photon showers in the Si-W ILD Electromagnetic calorimeter, consisting of 30 layers<br> of active silicon sensors sandwiched between tungsten absorber layers. The incident energies vary uniformly in the range of 10-100 GeV, along with the incident angle which varies in the range of 90-30 degrees from the axis orthogonal to the calorimeter face. The incident point to the calorimeter face is fixed.</p> <p>The cells are projected to a regular grid of shape (z, x, y) = (30, 30, 60), where the z axis points into the calorimeter face, giving a total of 54k channels.</p> <p>In total, the file contains approximately 500k showers. The structure of the file is as follows:</p> <ul> <li>Group name <em><strong>ecal</strong></em> <ul> <li><em><strong>energy</strong></em> : Dataset{500k, 1}</li> <li><strong><em>theta</em> </strong>: Dataset{500k, 1}</li> <li><em><strong>layers</strong></em> : Dataset{500k, 30, 30, 60}</li> </ul> </li> </ul> <p>The <strong><em>energy</em></strong> is the energy of the initial incident photon in units of GeV, <strong><em>theta</em></strong> is the incident angle of the incoming photon in units of radians and <strong><em>layers</em></strong> is the energy deposited in each cell in units of MeV</p> <p> </p> <p> </p>
Supplemental data for: A bibliometric assessment of the incidence of amyloid-Eszett (Aß), a false positive of amyloid-beta (Aβ), in the neurodegenerative disease literature
<p>One claimed reason for the development of Alzheimer’s disease (AD), a prominent neurodegenerative disease, is the extracellular aggregation of amyloid-beta (Aβ). A linguistic or formatting error has resulted in the misrepresentation of the Greek letter β with the German letter Eszett (ß), resulting in the formation of a non-existent compound, amyloid-Eszett (Aß). These datasets offer a quantified appreciation of the AD-related literature, carrying a mention of this false positive in the title, abstract and keywords of papers indexed in the Web of Science Core Collection and Scopus. Also, as a curiosity given the popularity of this large language model, we asked the questions to ChatGPT. This AI chatbot developed by OpenAI was able to recognize Eszett as a linguistic or typographic error, within this context, recognizing Aß and Aβ as equals. This erroneous substitution of a Greek letter (in Aβ) by a German one (Aß), despite giving a non-existent compound, will likely not change the underlying scientific conclusions of the affected papers, although errata might be useful to enlighten others, including metadata managers and journal copyeditors, so as not to repeat the same mistake.</p>
A Trial to Compare Antibacterial vs. Placebo Mouthwash to Reduce the Incidence of Sexually Transmitted Infections (STIs) in Men Who Have Sex With Men (MSM) Taking HIV Pre-Exposure Prophylaxis (PrEP)
ClinicalTrials.gov study NCT03881007. IPD Sharing: YES. Countries: 1. Publications: 2.
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.