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opencc-by-4.0Jun 2024View details →

Data and code for "Air pollution impacts from warehousing in the United States uncovered with satellite data"

<div> <div> <p>This repository (ver 2024.06.11) contains data and code supporting the analyses and visualizations in the manuscript "Air pollution impacts from warehousing in the United States uncovered with satellite data" in the journal <em>Nature </em><em>Communications</em> (DOI forthcoming). A description of the data files and code can be found in the README. </p> </div> </div>

opencc-by-4.0May 2024View details →

Supporting data for "Granularity of model input data impacts estimates of carbon storage in soils"

<p>The exchange of carbon between the soil and the atmosphere is an important factor in climate change.  Soil organic carbon (SOC) storage is sensitive to land management, soil properties, and climatic conditions, and these data serve as key inputs to computer models projecting SOC change.  Farmland has been identified as a sink for atmospheric carbon, and we have previously estimated the potential for SOC sequestration in agricultural soils in Vermont, USA using the Rothamsted Carbon Model.  However, fine spatial-scale (high granularity) input data are not always available, which can limit the skill of SOC projections.  For example, climate projections are often only available at scales of 10s to 100s of km2.  To overcome this, we use a climate projection dataset downscaled to <1 km2  (~18,000 cells).  We compare SOC from runs forced by high granularity input data to runs forced by aggregated data averaged over the 11,690 km2 study region.  We spin up and run the model individually for each cell in the fine-scale runs and for the region in the aggregated runs factorially over three agricultural land uses and four Global Climate Models.  </p> <p>In this repository are the downscaled climate input data that drive the RothC model, as well as the model outputs for each GCM.</p>

opencc-by-4.0Jun 2024View details →

COVID-19 pandemic impact on student achievements

<div>The COVID-19 pandemic created a natural experiment for comparisons in performance during in-person versus synchronous online and hybrid learning mode. We tracked changes in student achievements across the first two years of their engineering studies. The inquiry was conducted on 787 students.</div> <div> </div> <div><strong>Variable information</strong></div> <ol> <li>group (year of study commencement):  0 - 2019/2020, 1 - 2020/2021, 2 - 2021/2022</li> <li>nation: 1 - native (Poland), 0 - non-native</li> <li>gender: 1 - male, 0 - female</li> <li>studyoption: 1 - full-time studies, 0 - part-time studies</li> <li> <div>ICS - Introduction to Computer Science test results, test included 20 questions worth 20 points </div> <div>ICS is the first semester course</div> </li> <li> <div>NAA - Numerical Analysis Algorithms test results, test included 20 questions worth 20 points</div> <div>NAA is the third semester course</div> </li> <li>firstyear: the year in which the data were collected from the first semester course (ICS)</li> <li>secondyear: the year in which the data were collected from the third semester course (NAA)</li> </ol>

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Data from: Reconstructing 120 years of climate change impacts on Joshua tree flowering

<p>Quantifying how global change impacts wild populations remains challenging, especially for species poorly represented by systematic datasets. Here, we infer climate change effects on masting by Joshua trees (<em>Yucca brevifolia</em> and <em>Y. jaegeriana</em>), keystone perennials of the Mojave Desert, from 15 years of crowdsourced observations. We annotated phenophase in 10,212 geo-referenced images of Joshua trees on the iNaturalist crowdsourcing platform, and used them to train machine learning models predicting flowering from annual weather records. Hindcasting to 1900 with a trained model successfully recovers flowering events in independent historical records, and reveals slightly rising frequency of conditions supporting flowering since the early 20th Century. This reflects increased variation in annual precipitation, which drives masting events in wet years — but also increasing temperatures and drought stress, which may have net negative impacts on recruitment. Our findings reaffirm the value of crowdsourcing for understanding climate change impacts on biodiversity.</p>

opencc-zeroJun 2024View details →

opencc-by-4.0Jun 2024View details →

Dataset for the publication: Impact of unintentionally formed CH2O in oxygenated fuel exhausts on DeNOx-SCR at different NO2/NOx ratios under close to real conditions

<p>The dataset covers the research data of the publication in Catalysis Science & Technology with the title "Impact of unintentionally formed CH2O in oxygenated fuel exhausts on DeNOx-SCR at different NO2/NOx ratios under close to real conditions" (DOI: 10.1039/d2cy01935c).</p>

Fig. 8 in Impact of increasing morphological information by micro-CT scanning on the phylogenetic placement of Darwin wasps (Hymenoptera, Ichneumonidae) in amber

Fig. 8 Holotype of Rhyssa gulliveri sp. nov. A Habitus of specimen, lateral view. B Rugae dorsally on mesoscutum. C Face, anterior view, partially hidden by spider inclusion and milky coatings. D Face, more laterally with visible mandibles. E First tergite on metasoma, lateral view. F Head and mesoscutum, dorsal view. G Interpretative drawing with an additional drawing of the propodeum and T1 in dorsal view, where photos and micro-CT scan were used as templates. Scale bar A: 2 mm, B and C: 1 mm, D: upper 1 mm, lower 2 mm

Fig. 7 in Impact of increasing morphological information by micro-CT scanning on the phylogenetic placement of Darwin wasps (Hymenoptera, Ichneumonidae) in amber

Fig. 7 Holotype of Firkantus freddykruegeri gen. et sp. nov. A Habitus of specimen, lateral view. B Anterior view of face, right side with facial structures indicated. C Fore wing with folds indicating wing venation. D Anterior part of metasoma, dorsal view. E Posterior part of metasoma, with parameters and aedeagus. F Interpretative drawing with an additional drawing of the propodeum and T1 in dorsal view, where photos and micro-CT scan were used as templates. Scale bar A: 1 mm, B: 0.5 mm, F: lower 1 mm, right 0.5 mm

Fig. 3 in Impact of increasing morphological information by micro-CT scanning on the phylogenetic placement of Darwin wasps (Hymenoptera, Ichneumonidae) in amber

Fig. 3 RoguePlot placement of Pimplinae fossil Firkantus freddykruegeri gen. et sp. nov. before and after micro-CT scanning. The plots include all branches from the majority-rule consensus tree where the attachment probability was higher than 1%. A Firkantus freddykruegeri gen. et sp. nov. with colours indicating newly revealed body characteristics. Blue colouration represents newly added measurements; orange highlights either newly coded characters or characters where states could be reduced after the CT scan. B Placement before CT scanning. C Placement after CT scanning

Fig. 6 in Impact of increasing morphological information by micro-CT scanning on the phylogenetic placement of Darwin wasps (Hymenoptera, Ichneumonidae) in amber

Fig. 6 Holotype of Triclistus levii sp. nov. A Partial fore wing. B Metasoma, posterior end with the parameres. C Habitus of specimen, lateral view. D Head and mesoscutum, dorsal view. E Head. F Interpretative drawing with an additional drawing of the propodeum and T1, in dorsal view, where photos and micro-CT scan were used as templates. Scale bars A: 1 mm, B: 0.5 mm C: 1 mm F: bottom 1 mm, top right 0.5 mm

Fig. 9 in Impact of increasing morphological information by micro-CT scanning on the phylogenetic placement of Darwin wasps (Hymenoptera, Ichneumonidae) in amber

Fig. 9 Holotype Magnocula sarcophaga gen. et sp. nov. A Habitus of specimen, ventral view. B Habitus of holotype, lateral view. CT scan of C head and mesoscutum in dorsal view, D face in anterior view, and E last tergites with ovipositor and sheaths. F Photo of a partial fore wing, in top left is T2 with its rugopunctate to striate structure. G Interpretative drawing with an additional drawing of the propodeum and T1 in dorsal view, where photos and micro-CT scan were used as templates. Scale bar A: 1 mm, F: 0.5 mm G: lower 1 mm, right 0.5 mm

Fig. 2 in Impact of increasing morphological information by micro-CT scanning on the phylogenetic placement of Darwin wasps (Hymenoptera, Ichneumonidae) in amber

Fig. 2 RoguePlot placement of Metopiinae fossil Triclistus levii sp. nov. before and after micro-CT scanning. The plots include all branches from the majority-rule consensus tree where the attachment probability was higher than 1%. A Triclistus levii sp. nov. with colours indicating newly revealed body characteristics after the CT scan. Blue colouration represents newly added measurements; orange highlights either newly coded characters or characters where states could be reduced after the CT scan. B Placement before CT scanning. C Placement after CT scanning

Fig. 5 in Impact of increasing morphological information by micro-CT scanning on the phylogenetic placement of Darwin wasps (Hymenoptera, Ichneumonidae) in amber

Fig. 5 RoguePlot placement of Phygadeuontinae fossil Magnocula sarcophaga gen. et sp. nov. before and after micro-CT scanning. The plots include all branches from the majority-rule consensus tree where the attachment probability was higher than 1%. A Magnocula sarcophaga gen. et sp. nov. with colours indicating newly revealed body characteristics. Blue colouration represents newly added measurements; orange highlights either newly coded characters or characters where states could be reduced after the CT scan. B Placement before CT scanning. C Placement after CT scanning

Fig. 4 in Impact of increasing morphological information by micro-CT scanning on the phylogenetic placement of Darwin wasps (Hymenoptera, Ichneumonidae) in amber

Fig. 4 RoguePlot placement of Rhyssinae fossil Rhyssa gulliveri sp. nov. before and after micro-CT scanning. The plots include all branches from the majority-rule consensus tree where the attachment probability was higher than 1%. A Rhyssa guliveri sp. nov. with colours indicating newly revealed body characteristics. Blue colouration represents newly added measurements; orange highlights either newly coded characters or characters where states could be reduced after the CT scan. B Placement before CT scanning. C Placement after CT scanning

Fig. 4 in The dual impact of Ostreopsis cf. ovata on Mytilus galloprovincialis and Paracentrotus lividus: Toxin accumulation and pathological aspects Abstract

Fig. 4: Health status of sea urchins (avg±SDV, n = 4 animals) exposed to Ostreopsis cf. ovata (strain D483) for five days at different cell densities. Health index 1 corresponds to all four sea urchins alive after five days of exposure, 0 to all sea urchins dead in four days, intermediate values to different degrees of damage such as spine folded, partial and total spine loss and death in five days.

Fig. 6 in The dual impact of Ostreopsis cf. ovata on Mytilus galloprovincialis and Paracentrotus lividus: Toxin accumulation and pathological aspects Abstract

Fig. 6: Sea-urchin health status (avg±SDV, n = 4) after exposure to whole Ostreopsis cf. ovata cultures (strain 00APS0810-S1) or toxins extracted from cultures of the same cell density.

Fig. 2 in The dual impact of Ostreopsis cf. ovata on Mytilus galloprovincialis and Paracentrotus lividus: Toxin accumulation and pathological aspects Abstract

Fig. 2: Feeding of Mytilus galloprovincialis on Ostreopsis cf. ovata in a 72 h experiment. Weight-normalised ingested cells (avg±SDV) at different time intervals. Fresh microalgal cultures (2.17±0.23·103 cells ml-1) were provided every 24 h. Of the 10 animals of each replicate, 7-8 died at the beginning of the second day while the survivors were toxic (Table 1).

Fig. 5 in The dual impact of Ostreopsis cf. ovata on Mytilus galloprovincialis and Paracentrotus lividus: Toxin accumulation and pathological aspects Abstract

Fig. 5: Sea urchin health status (avg±SDV, n = 4) upon exposure to entire or sonicated Ostreopsis cf. ovata cultures (strain D483) of the same initial cell density.

Fig. 3 in The dual impact of Ostreopsis cf. ovata on Mytilus galloprovincialis and Paracentrotus lividus: Toxin accumulation and pathological aspects Abstract

Fig. 3: Feeding of Paracentrotus lividus (avg±SDV, n = 15) on Ostreopsis cf. ovata epiphytic on the red alga Asparagopsis taxiformis in five experiments lasting five days each. Experiment 4b was performed with the same animals as 4a, which were given a second stock of seaweeds after a two- day interval. Macroalgae (55-131 g) were completely eaten in all cases. Four additional experiments at low epiphytic cell density (<3.4·103 cells g-1) are not represented. Asterisks indicate the experiments in which sea urchins were weakly toxic at the mouse bioassay (Supplementary Material, Table S2).

Fig. 1 in The dual impact of Ostreopsis cf. ovata on Mytilus galloprovincialis and Paracentrotus lividus: Toxin accumulation and pathological aspects Abstract

Fig. 1: Feeding of Mytilus galloprovincialis in six 24 h experiments with animals of different sizes exposed to different Ostreopsis cf. ovata cell concentrations. A) Mussel wet weight (WW) and O. cf. ovata cell density at the beginning of each experiment (avg±SDV). B) Weight-normalised ingested cells (avg±SDV). The asterisks indicate experiments in which some or all replicates were toxic to the mouse bioassay (Supplementary Material, Table S1).