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Fig. 8 in Reproduction of Brevoortia aurea (Spix & Agassiz, 1829) (Actinopterygii: Clupeidae) in the Mar Chiquita Coastal Lagoon, Buenos Aires, Argentina

Fig. 8. Proportion of mature individuals observed for each length classes of Brevoortia aurea. Females (black circles, dotted line) L 50 = 27.77 cm, N = 588. Males (white circles, solid line) L 50 = 26.59 cm, N = 293.

Fig. 2 in Reproduction of Brevoortia aurea (Spix & Agassiz, 1829) (Actinopterygii: Clupeidae) in the Mar Chiquita Coastal Lagoon, Buenos Aires, Argentina

Fig. 2. Captures per unite effort (CPUE kg/h), temperature (°C) and salinity (psu) obtained for Brevoortia aurea during sampled period in Mar Chiquita Coastal Lagoon.

Fig. 5. A in Reproduction of Brevoortia aurea (Spix & Agassiz, 1829) (Actinopterygii: Clupeidae) in the Mar Chiquita Coastal Lagoon, Buenos Aires, Argentina

Fig. 5. A: oogonias (arrow) and primary growth (p) oocytes; B: cortical alveoli stage oocyte (arrow); C: yolked oocytes; D: hydrated oocytes (arrow); E: details of a yolked oocyte (r: radiata zone; g: granulosa cells; t: teca cells); F: atresic follicle (arrow); G: post-ovulatory follicle "0" (arrow); H: post-ovulatory follicle "1" (arrow). Scale bars: A, E 25 μm; B, C, F, G, H, 100 μm; D, 250 μm.

Fig. 3 in Reproduction of Brevoortia aurea (Spix & Agassiz, 1829) (Actinopterygii: Clupeidae) in the Mar Chiquita Coastal Lagoon, Buenos Aires, Argentina

Fig. 3. Monthly variation of the gonadosomatic index (GSI) (females only), based on an annual cycle.

Fig. 6 in Reproduction of Brevoortia aurea (Spix & Agassiz, 1829) (Actinopterygii: Clupeidae) in the Mar Chiquita Coastal Lagoon, Buenos Aires, Argentina

Fig. 6. Frequency distribution of oocyte diameters (N = 6000 oocytes measured). From black bars to white bars: Primary growth oocyte, cortical alveoli, yolked oocytes and hydrated oocytes.

openmit-licenseAug 2024View details →

Estimating future climate change impacts on human mortality and crop yields via air pollution: supplemental files

Atmospheric chemistry model output and other gridded data sets necessary to estimate human mortality and crop yield losses associated with future climate change, as used in Murray et al. [PNAS, 2024] doi:10.1073/pnas.2400117121.

opencc-by-4.0Aug 2024View details →

Assets (code, scripts and datasets) for the manuscript "Correction of the Air-Sea Heat Fluxes in Ocean General Circulation Models Using Neural Networks"

This dataset contains all relevant software and data related to the manuscript "Correction of the Air-Sea Heat Fluxes in Ocean General Circulation Models Using Neural Networks", submitted to AGU journals.

TSSCXG-17: Global Gridded Dataset of Surface Ocean pCO2 and Air-Sea CO2 Flux (1993-2020)

This dataset presents a global gridded reconstruction of the partial pressure of CO2 (pCO2) in the surface ocean and the corresponding air-sea CO2 flux, covering the period from 1993 to 2020. Developed to enhance understanding of climate change and the global carbon cycle, this dataset addresses gaps in oceanic carbon flux data through innovative machine learning techniques. The reconstruction process integrates in situ observations, satellite data, and reanalysis products, employing a three-step algorithm involving dimensionality reduction, clustering, and regression.

Raw data for High-speed shear mixing: a versatile energy-efficient ultra-fast strategy for solvent-free amine-functionalised solid CO2 adsorbents for direct air capture

Specification of affiliations: <ul> <li>Pavol Suly - Centre of Polymer Systems</li> <li>Barbora Hanulikova - Centre of Polymer Systems</li> <li>Abdulkadir Bozarslan - Centre of Polymer Systems</li> <li>Milan Masar - Centre of Polymer Systems</li> <li>Michal Urbanek - Centre of Polymer Systems</li> <li>Eva Domincova Bergerova - Centre of Polymer Systems</li> <li>Michal Machovsky - Centre of Polymer Systems</li> <li>Ivo Kuritka - Centre of Polymer Systems</li> </ul>   Raw data for the research paper. Information on the data collection are described in the manuscript.

Data from multi-sensor devices and reference station to monitoring urban air quality

Data from electrochemical and optical sensors. <h3>Files names</h3> <ul> <li>ECT01, ECT02, ECT06, ECT07 = device name</li> <li>ISSEP = reference station <ul> <li>"c" = calibration data</li> <li>"v" = validation data</li> </ul> </li> </ul> <h3>Variable description</h3> <table> <tbody> <tr> <td>Electrochemical sensor</td> <td>Optical sensor</td> <td>Probe</td> <td>Reference</td> </tr> <tr> <td> AE = auxiliary electrode (mV) WE = working electrode (mV) N = temperature correction  <ul> <li>ch0 = CO sensor</li> <li>ch1 = OX sensor</li> <li>ch2 = NO2 sensor</li> <li>ch3 = NO sensor</li> </ul> </td> <td> PM1, PM2.5 and PM10 in µg/m³ </td> <td> Prs_mbar = pressure (mbar) Temp = temperature (°C) RH = relative humidity (%) </td> <td> DV30 = wind direction @ 30m (°) HR = relative humidity (%) NO, NO2, O3, PM10 and PM2.5 (µg/m³) Precipita = precipitation (mm) TC3 = temperature @ 3m (°C) VV30 = wind speed @ 30m (m/s) </td> </tr> </tbody> </table>

Measurement database and line parameter database for air- and self-broadened H2O in the 5970-6575 cm-1 region

Air-broadened H2O measurements at 294 K, total pressure range 250-1000 mbar, and self-broadened measurements at 294 K, 10 and 20 mbar, and analysis were carried out within the ESA-funded project ISOGG (Improved Spectroscopy for satellite measurements Of Greenhouse Gases). The measurement database is in “H2O_self-_and_air-broadened_measurements_5970-6575cm-1.zip” and the line parameter database in “H2O_self-_and_air-broadening_line_parameters_5970-6575cm-1.zip”. The readme file is “Measurement database and line parameter database for air- and self-broadened H2O in the 5970-6575 cm-1 region - Readme.docx”. Definitions and units of line parameters are in accordance with the HITRAN database and can be found in the readme file in <a href="https://zenodo.org/records/1009126">ESA SEOM-IAS – Spectroscopic parameters database 2.3 µm region (zenodo.org)</a>. The systematic line position uncertainty is 1.7x10-5 cm-1.

DiY Sensor Dataset for Air Pollution Monitoring

The research engaged students from a private university in Bilbao in designing and implementing a project that involved young students in collecting and analyzing air quality data using air meters they assembled. An interesting aspect of the project was the development of an image processing technique for analyzing dust captured on petroleum jelly, enhancing the quantification of particulate matter and providing insights into air quality at various school locations. Additionally, the study introduced a synthetic data generation algorithm designed to simulate air quality data for educational purposes, which allowed students to engage with data analysis and interpretation, thereby enriching their learning experience and understanding of air pollution dynamics.

Seefeld Cold-Air Pool Experiment (SEECAP): Meteorological Measurement Data

The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. Six automatic weather stations and 41 unventilated temperature sensors were distributed within the valley to gain insight into the spatial structure of the cold-air pool in Seefeld. The study site as well as locations and instrumentation of each station are described in Rudolph (2022) and Rauchöcker et al. (2024d). This upload contains meteorological measurement data associated with SEECAP. WRF simulations were performed for two nights, representing an ideal evolution of the cold-air pool (January 12 and January 13 2020) and a disrupted evolution (January 16 and January 17 2020), respectively. The output data of simulations with snow cover for the night between January 16 and January 17 2020 are published in Rauchöcker et al. (2024a) and in Rauchöcker et al. (2024c) for the night between January 12 and January 13 2020. Simulation output without snow cover is available for the night between January 16 and January 17 2020  in Rauchöcker et al. (2024b). <h3>Automatic Weather Stations</h3> Measurement data of the six automatic weather stations can be found in momaa.zip. The folder includes one file for each station (MOMAA02.dat, MOMAA03.dat, MOMAA04.dat, MOMAA07.dat, MOMAA08.dat and MOMAA10.dat). These stations measured temperature, pressure, humidity, net radiation, wind speed and wind direction at 1-min intervals. A figure showing the location of the different stations is included as well (Stations.pdf); the station names of the automatic weather stations are abreviated in the legend of that figure (e.g. M04 instead of MOMAA04). Incoming and outgoing longwave and shortwave radiation, latent heat flux and sensible heat flux were measured at MOMAA04 and MOMAA08. The radiation data can be found in MOMAA04_rad.dat and MOMAA08_rad.dat and eddy covariance data in MOMAA04_turb.csv and MOMAA08_turb.csv, respectively. <h3>Temperature Sensors</h3> Data from the unventilated temperature sensors can be found in hobos.zip, which contains a file for each sensor and the file names refer to the naming convention in Stations.pdf. Most sensors were located along the valley floor and along a ski jump on its southeastern slope. At nine locations, temperature sensors were mounted at two heights (1 m and 2 m above the ground). File names reflect that height by adding _1m or _2m to the file name (e.g. A_1m.txt and  A_2m.txt). Locations that had only one sensor were named according to the station name (e.g. M.txt). The remaining sensors were used for vertical profiles at 3 different levels of a walk-up tower (TOWER_2m.txt, TOWER_2ndfloor.txt and TOWER_top.txt) and at a bridge (VP; labeled from VP_050.txt at 0.5m above the ground to VP_630.txt at 6.3m). A pseudo-vertical profile for the sensors along the slope of the valley, at the walk-up tower down to the lowest station in the upper basin (top to bottom, M03, S4, S3, S2, S1, M10, G, H, M04) can be found in PseudoProfile_basin.mat.

Urban Air Pollution and Children's Brain Development: A Systematic Bibliometric Review

The dataset for this research was compiled through an advanced PubMed search targeting publications from a one-year period. Keywords focused on air pollution, neurodevelopment, and associated disorders. From an initial pool of 450 publications, filtering based on co-occurrence of relevant keywords reduced this to approximately 50 papers. VOSviewer was employed to analyze co-occurrences and generate a visual map of relationships between air pollution and child neurodevelopment. The thesaurus was applied to standardize terminology, refining the final network for detailed analysis of keyword clusters and their interactions.

opencc-by-4.0Jul 2024View details →

Supplementary Dataset: Air quality modeling intercomparison and multi-scale ensemble chain for Latin America

The Supplementary dataset of the manuscript titled "Air quality modeling intercomparison and multi-scale ensemble chain for Latin America" can be downloaded via this link: https://swiftbrowser.dkrz.de/public/dkrz_3ab03fbe-db0a-42e8-8b19-caf61d10634d/PAPILA/ The data repository contains the model data used in the model intercomparison with six global and regional chemical-transport model over Latin America and the observation datasets used in the model intercomparison.  This work presents the first model intercomparison and ensemble construction for Latin America, which was assembled under the Prediction of Air Pollutants in Latin America (PAPILA project (https://papila-h2020.eu/papila).

opencc-zeroJan 2024View details →

Linked collectors and determiners for: Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires Coleccion BAFC de Hongos.

Natural history specimen data linked to collectors and determiners held within, "Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires Coleccion BAFC de Hongos". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/96e30d99-3042-4d07-899f-9f01c3ca3fbf">https://bionomia.net/dataset/96e30d99-3042-4d07-899f-9f01c3ca3fbf</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/96e30d99-3042-4d07-899f-9f01c3ca3fbf">https://gbif.org/dataset/96e30d99-3042-4d07-899f-9f01c3ca3fbf</a>. Formatted as a Frictionless Data package.

opencc-zeroJan 2024View details →

Linked collectors and determiners for: Herbario Gaspar Xuarez (BAA) - Facultad de Agronomía - Universidad de Buenos Aires.

Natural history specimen data linked to collectors and determiners held within, "Herbario Gaspar Xuarez (BAA) - Facultad de Agronomía - Universidad de Buenos Aires". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/3cc9ba97-17dc-4a4a-8a12-bb36038021c4">https://bionomia.net/dataset/3cc9ba97-17dc-4a4a-8a12-bb36038021c4</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/3cc9ba97-17dc-4a4a-8a12-bb36038021c4">https://gbif.org/dataset/3cc9ba97-17dc-4a4a-8a12-bb36038021c4</a>. Formatted as a Frictionless Data package.

Knowledge graph for air quality simulations in the Region of Murcia from 2024-12-26 to 2024-12-29

This resource provides the knowledge graphs with the information of the simulation done from 2023-12-26 to 2023-12-26 in the Region of Murcia with the CHIMERE-WRF model (https://www.lmd.polytechnique.fr/chimere/) and transformed into knowledge graph thanks to the SWIT framework (http://sele.inf.um.es/swit/about.html). It also links this data to the github repository including the code and the ontology generated in the work "Representation of  chemistry transport models simulations using knowledge graphs".

West Antarctic Peninsula (WAP) MITgcm-REcoMv2 outputs for 1991 (T, S, DIC, TA, DIN, Chl, air-sea CO2 flux)

This dataset contains the netCDF files for the year 1991 of dissolved inorganic nitrogen (DIN), dissolved inorganic carbo (DIC), total alkalinity (TA), diatom chlorophyll concentration (DiaPhyto), small phytoplankton chlorophyll (SmPhyto), air-sea CO2 fluxes (CO2flx), ocean temperature (Temp) and salinity (Salt) for the MITgcm-REcoMv2 ocean circulation and biogeochemistry model implemented for the West Antarctic Peninsula. Latitude and longitude for all variables are contained in the grid.glob.nc file. Files names start with the variable name, followed by the month and year. They are saved either as monthly means, as 10 day means, or 5 day means. The units for each variable are as follows: DIN: mmol/m3 DIC: mmol/m3 TA: mmol/m3 DiaPhyto: mg/m3 SmPhyto: mg/m3 CO2flx: mmol/s Temp: ˚C Results from this simulation are published in: SCHULTZ, C., DONEY, S.C., ZHANG, W.G., REGAN, H.C., HOLLAND, P., MEREDITH, M., STAMMERJOHN, S. Modeling of the Influence of Sea Ice Cycle and Langmuir Circulation on the Upper Ocean Mixed Layer Depth and Freshwater distribution at the West Antarctic Peninsula. Journal of Geophysics Research Oceans, 125, 8, 2020. doi:10.1029/2020JC016109 SCHULTZ, C., DONEY, S.C., HAUCK, J., KAVANAUGH, M.T., SCHOFIELD, O. Modeling Phytoplankton Blooms and Inorganic Carbon Responses to Sea-Ice Variability in the West Antarctic Peninsula. Journal of Geophysical Research Biogeosciences, 126, 4, 2021. doi: 10.1029/2020JG006227

Dissemination of mass and volume by double weighing in air - measurement data

All data related to the measurement. <ul> <li>Files "1kg - ...", "500 g - ..." etc. include measurement reports from the mass comparators</li> <li>Files "podminky_1kg" etc. include dtaa for temperature, pressure and humidity from dataloggers</li> <li>File "Inputs.ods" - all input data including measurements with AT1006</li> <li>File "Inputs-noat1006.xlsx" - all input data without measurements from AT1006</li> <li>File "MCOutput.csv" - Output data from Monte Carlo simulation</li> <li>File "Output.xlsx" - Output data with covariances and correlations</li> <li>File "Output_withAT1006.xlsx" - Output data where AT1006 measurements were used</li> </ul>