Find research datasets worth reusing

opencc-by-4.0May 2020View details →

Supplementary data for the manuscript "Technical note: Estimating aqueous solubilities and activity coefficients of mono- and α,ω-dicarboxylic acids using COSMO-RS-DARE"

.cosmo files (BP-TZVPD-FINE) of dicarboxylic acids (C2-C8), dimers and monohydrates of mono- (C1-C6) and dicarboxylic acids, and water dimer.

opencc-by-4.0Apr 2020View details →

Comparing Perturbation Modes for Evaluating Instabilities in Neuroimaging: Processed NKI-RS Subset (08/2019)

The processed subset of the NKI-RS dataset for evaluation of various perturbation modes when studying instabilities. Linked to the <a href="https://arxiv.org/abs/1908.10922">pre-print found here</a>, can be visualized using the <a href="https://github.com/gkiar/stability-mca/blob/master/code/dipy_exploratory/mca_dipy_exploratory_analysis.ipynb">plotting code here</a>, and generated with various <a href="https://github.com/gkiar/stability/tree/master/code/experiments/paper0_comparing_perturbation_modes">scripts and launch configurations found here</a>.

openapache2.0Jan 2024View details →

Using RS-DAT to study continental-scale phenology at high-spatial resolution

This repository includes the notebooks employed to calculate and analyze a gridded phenological model, as computed from a set of daily meteorological variables.

opencc-by-4.0Jan 2022View details →

The Three Rs: Resolving Respiration Robotically in Shelf Seas

Ocean gliders were deployed to conduct 'virtual mooring’ profiles at a study site in the seasonally stratified central Celtic Sea (station CCS, 49° 24’ N, 8° 36’ W) (see Fig. 1) during spring 2015 (6th April to- 28th April, decimal day 95 to 117) and summer 2015 (15th July and- 2nd August, decimal day 195 to 213). The integrated approach adopted in this study, combining ship based and glider measurements enabled estimates of spatial gradients while also minimising tidal aliasing that would likely be introduced by long spatial transects with the glider. A Slocum (Teledyne Webb Research, Falmouth, USA) Ocean Microstructure Glider (OMG, see Palmer et al., 2015 for full details) was equipped with a MicroRider microstructure package (Rockland Scientific International) to measure turbulentthe microstructure of velocity shear, a Seabird SBE42 CTD sensor to measure temperature, salinity and pressure, and an Aanderaa 4831 oxygen optode to measure O2 (precision 0.2 µmol kg-1). Measurements were taken within 5 m of the bed and 2 m of the surface on most dives, with each yo-yo profile taking approximately 20 minutes. Glider salinity data was corrected for thermal inertia following Palmer et al. (2015). The glider AA4831 optode is known to experience severe lag across strong oxygen gradients, and therefore oxygen data was corrected where possible for optode membrane lag following Bittig et al. (2014). Where optode lag across the oxycline was too great and so not correctable using this method, it was omitted and oxygen data from coinciding CTDs was used. In comparison to other oxygen optodes, the AA4831 has been documented by various scientific studies as being an extremely stable optode with low detectable drift ( <0.5% yr-1) and high precision of <0.2 µmol kg-1 (Kortzinger et al., 2004; Nicholson et al., 2008; Johnson et al., 2010; Champenois & Borges, 2012). Optode drift was calculated in this study by comparing discrete Winkler-analysed samples taken at deployment and recovery of the gliders, identifying a downward drift of 0.001% d-1, in close agreement with quoted manufacturer values. Glider sensors (temperature, salinity and ) were calibrated against nearby ship CTD profiles (CTD calibrated 1 month prior to cruise, SBE 43 precision = 2% of  saturation) and discrete water samples collected within 3 hours and 2 km of glider deployment and recovery times and glider position, respectively, as part of the Shelf Sea Biogeochemistry programme (RRS Discovery, DY029 and DY033). Error estimates for the total change in  (µmol kg-1) were calculated as the sum of the optode precision (0.2 µmol kg-1) and drift over the entire respective deployments (<0.1 µmol kg-1). Currents, tides, salinity and temperature were monitored throughout the glider deployments by a mooring at the CCS study site, which was equipped with an acoustic current profiler (ADCP), salinometer and thermistors that provided near-continuous data (Wihsgott et al., 2019; Ruiz-Castello et al., 2019).

opencc-by-4.0Aug 2022View details →

Modelling dataset of the São Sepé River watershed, RS, Brazil

This dataset provides maps (30m resolution) and time series suited for hydrological modelling of the São Sepé watershed, RS, Brazil. Reference period is from 01-01-2010 to 01-01-2020. This is a test. Details of the files can be found in the <code>readme.csv</code> file <ul> <li><code>saosepe_dem.zip</code> -- full-extension DEM-based raster maps;</li> <li><code>saosepe_lulc.zip</code> -- full-period and full-extension yearly-based land use and land cover raster maps;</li> <li><code>saosepe_series_daily.txt</code> -- full period time series of daily precipitation, streamflow and temperature;</li> <li><code>saosepe_series_weekly.txt</code> -- full period time series of weekly precipitation, streamflow and temperature;</li> <li><code>saosepe_series_monthly.txt</code> -- full period time series of monthly precipitation, streamflow and temperature;</li> <li>saosepe_series_yearly.txt -- full period time series of yearly precipitation, streamflow and temperature;</li> <li><code>saosepe_window_et24h.zip</code> -- full-period inspection window of Evapotranspiration raster maps;</li> <li><code>saosepe_window_lstDem.zip</code> -- full-period inspection window of DEM-corrected Land Surface Temperature  raster maps;</li> <li><code>saosepe_window_ndvi.zip</code> -- full-period inspection window of Landsat 8 NDVI raster maps;</li> </ul>

opencc-by-4.0Aug 2024View details →

Banco de dados do projeto piloto de Pagamentos por Serviços Ambientais do Arroio Grande (Venâncio Aires, RS)

Banco de dados do projeto piloto de Pagamentos por Serviços Ambientais do Arroio Grande (Venâncio Aires, RS). Projeto em andamento.   <table> <tbody> <tr> <td>Arquivo</td> <td>Descrição</td> </tr> <tr> <td>castelhano_db.gpkg </td> <td>Banco de dados georreferenciado</td> </tr> <tr> <td>layout_pip.zip</td> <td>PDFs com mapas internos e externos do projeto</td> </tr> <tr> <td>grande_lulc.zip</td> <td>Análise de mundança de uso do solo na região de interesse</td> </tr> <tr> <td>tier2.zip</td> <td>Fotografias e mapas internos dos imóveis</td> </tr> </tbody> </table>

Relatório Técnico - Vulnerabilidade e risco: análise do desastre socioambiental em Santa Maria - RS (2024)

O presente estudo técnico é um dos produtos elaborados pelo Laboratório de Investigação Sociológica através do Projeto "Catástrofes climáticas: dimensões socioculturais do alerta e contenção de riscos" (Nº062294), contemplado no processo seletivo do Programa de Extensão “UFSM Solidária e Cidadã: esperança e reconstrução para o Rio Grande do Sul” e financiadas pela Pró-Reitoria de Extensão (PRE/UFSM). Esta produção é fundamentada na noção de crise socioambiental, reconhecendo a agência humana e os riscos que emergem neste contexto. Com a proposta de fornecer uma análise sociológica do desastre socioambiental de 2024, com foco no município de Santa Maria, o trabalho busca servir de subsidio às ações preventivas e de reparação para cenários futuro. Desta forma, a pesquisa se alinhada diretamente com o Objetivo de Desenvolvimento Sustentável 13 (ODS 13) da Organização das Nações Unidas (ONU), que busca tomar medidas urgentes para combater a mudança climática e seus impactos. Para alcançar tal entendimento, foi desenvolvida uma análise histórica dos eventos climatológicos, hidrológicos e meteorológicos ocorridos nas últimas três décadas, abarcando os níveis nacional, estadual e municipal. Abordagem essa que permitiu compreender os fenômenos tanto numa escala macro (Brasil) quanto micro (Santa Maria e seus bairros). Considerando a finalidade da pesquisa, na produção de indicadores que propiciem uma reflexão acerca dos processos de vulnerabilização ao risco de desastre ambiental, o estudo se concentrou especialmente em Santa Maria (RS). Por meio da transformação de dados censitários em mapas, foi possível traçar o perfil das populações atingidas pela tragédia. Assim, a proposta contribui também na orientação de uma gestão local mais eficaz, possibilitando uma melhor avaliação e gestão de riscos no município, servindo de orientação no desenvolvimento de políticas públicas para as áreas afetadas.

opencc-by-4.0Jan 2020View details →

Brainport, Automated valet parking, RS camera parking spot occupancy

Scenario description: RS Camera parking spot occupancy detection and publication of the iot message from type AutoPilot.ParkingSpotDetection to the PMS via IoT platforms Session description: A AD-car parks to the selected parking spot  the rs camera detect the car at the parking spot and publish the occupancy information to the PMS for parking management purpose Datasets descriptions: AUTOPILOT_BrainPort_AutomatedValetParking_DriverVehicleInteraction: Data extracted from the CAN of the vehicle Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle AUTOPILOT_BrainPort_AutomatedValetParking_DroneAvpCommand: Data sent from drone Dataset Description This dataset contains route information for a vehicle to a designated parking spot AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsAbsolute: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with absolute coordinates AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsRelative: Data extracted from the vehicle environment sensors Dataset Description This dataset contains information about detected object, with relative coordinates AUTOPILOT_BrainPort_AutomatedValetParking_IotVehicleMessage: Data sent between all devices, vehicles and services Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle. AUTOPILOT_BrainPort_AutomatedValetParking_ParkingSpotDetection: Data sent from drone to parkingService Dataset Description This dataset contains informaton about detected parking spots AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystem: Data from GPS on the vehicle Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystemResampled: Data from GPS on the vehicle Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds AUTOPILOT_BrainPort_AutomatedValetParking_Vehicle: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o temperature and battery state of the vehicles AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpCommand: Data sent from ParkingService to vehicle Dataset Description This dataset contains route to parkingspot, and some other environmental information AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus: Data sent from vehicle to ParkingService Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle AUTOPILOT_BrainPort_AutomatedValetParking_VehicleDynamics: Data from the CAN and sensors about the state of the vehicle Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors

openother-openJun 2022View details →

MateusRQ/Flora-ameacada-do-RS-compilada: Flora ameacada do RS compilada

Compilação das listas estadual (Dec. RS 52.109/2004), federal (Portaria MMA 148/2022, 443/2014, IN MMA 06/2008) e da IUCN das espécies vegetais ameaçadas de extinção no estado do Rio Grande do Sul, Brasil. Consta ainda a lista de plantas exóticas invasoras no RS (Portaria SEMA-RS 79/2013) A primeira versão desse arquivo foi elaborada em 2017/2018 em ocasião de estágio extracurricular na Divisão de Flora da Secretaria Estadual do Meio Ambiente do Rio Grande do Sul (SEMA-RS), sob orientação do analista ambiental Leonardo Marques Urruth. Desde então tem circulado internamente, sem estar em nenhuma base de dados de livre acesso, sendo bastante útil para pesquisas rápidas do status de ameaçada e outras informações das espécies vegetais ameaçadas de extinção no RS, compilando dados de listas oficiais dos diferentes níveis. Além da pesquisa por uma espécies em particular, os filtros do excel permitem a observação de dados a partir de diferentes agrupamentos, o que pode ser útil para diferentes objetivos. Em 2022, com a publicação da atualização da lista federal, pela Portaria do Ministério do Meio Ambiente (MMA) n° 148/2022, o documento foi atualizado e acrescido dos status de conservação listados pela IUCN, gerando esta versão "1.2.0". Conteúdo: Na primeira aba consta a compilação da lista estadual, da lista federal e da IUCN, com informações taxonômicas e de hábito de vida. A segunda aba apresenta a atual liste federal (MMA 148/2022) completa, com grifos nas espécies presentes na lista estadual ou com ocorrência registrada no RS. A terceira aba apresenta a lista federal anterior (MMA 443/2014) com os mesmos grifos e mais observações sobre a IN MMA 06/2008. Essas informações foram mantidas após a atualização do documento a título de curiosidade e comparação com a lista atual supracitada. A quarta aba apresenta a lista das espécies na redlist da IUCN que não foram registradas na lista estadual ou na federal. Na quinta e ultima aba acrescentei a lista de espécies vegetais exóticas invasoras no RS (Portaria SEMA-RS 79/2013). Todas as referências usadas constam no corpo do arquivo. Possíveis inconsistências no cruzamento de informações entre as listas podem se dever a mudanças na nomenclatura taxonômica, que foi observada e corrigida em muitos casos, mas provavelmente não em todos. Erros dessa e qualquer outra natureza, além de qualquer dúvida, podem ser reportados para mateusraguse@hotmail.com para serem corrigidos ou respondidas. Espero que o arquivo possa ser útil para todos interessados assim como tem sido para mim. Mateus Raguse-Quadros

opencc-by-4.0Apr 2022View details →

Raw Data to "Prediction of acid pKa values in the solvent acetone based on COSMO-RS"

This data is a supplement to the publication entitled "Prediction of Acid pKa Values in the Solvent Acetone based on COSMO-RS" in the Journal of Computational Chemistry (DOI:10.1002/jcc.26864). The data includes initial starting structures as inputs for conformer searches using COSMOconf (versions 2020 and 2021) in combination with TURBOMOLE (version 7.3). The corresponding output files serve as inputs for the calculation of Gibbs free energies using COSMO-RS as provided by COSMOtherm. Additional information on the file structure is given in the README file.

opencc-by-4.0May 2024View details →

Escolas RS - Nível Básico (Censo Escolar 2023 - INEP)

Localização georreferenciada (pontos) das Instituições de Ensino do Rio Grande do Sul - Nível Básico. Abrangência geográfica: Estado do Rio Grande do Sul. Data dos dados: referentes ao cadastro das instituições em 2023 (ref. Censo Escolar 2023). Atributos: variáveis selecionadas do Censo Escolar 2023 - INEP. O arquivo encontra-se no Sistema de Coordenadas Geográficas (2D), SRC:4326 WGS84 (GCS_WGS_1984) Datum horizontal: D_WGS_1984 Acompanha o pacote do arquivo vetorial (formato shapefile) o dicionário de variáveis. Origem dos dados:  Catálogo de Escolas Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira | Inep https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/inep-data/catalogo-de-escolas Último acesso: 22 mai 2024, 16h56 Microdados do Censo Escolar da Educação Básica 2023 Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira | Inep Disponível em: https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/censo-escolar Último acesso: 22 mai 2024, 16h58 Os dados originais foram tratados para disponibilização em arquivo vetorial por: Bárbara Giaccom; Alice Rauber Gonçalves; Natan Bregalda Porto Alegre, 22 de Maio de 2024.

opencc-by-4.0Jun 2024View details →

Municípios RS em Estado de Calamidade Pública e em Situação de Emergência - Decreto 57.646/2024

Municípios em Estado de Calamidade Pública ou Situação de Emergência no Rio Grande do Sul. Decreto nº 57.646, de 30/05/2024, do Governo do Estado do RS. O número de municípios afetados pelas intensas chuvas de maio de 2024 passou para 95 municípios em estado de calamidade pública e 323 em situação de emergência. <div>Decreto Estadual nº 57.614, de 13 de maio de 2024: <a href="https://www.diariooficial.rs.gov.br/materia?id=999537" target="_blank" rel="noopener">https://www.diariooficial.rs.gov.br/materia?id=999537</a></div> <div>Situação de emergência: 320 municípios</div> <div>Estado de calamidade pública: 46 municípios</div> <div> </div> <div>Decreto Estadual nº 57.626, de 21 de maio de 2024: <a href="https://www.diariooficial.rs.gov.br/materia?id=1000161" target="_blank" rel="noopener">https://www.diariooficial.rs.gov.br/materia?id=1000161</a></div> <div>Situação de emergência: 340 municípios</div> <div>Estado de calamidade pública: 78 municípios</div> <div> </div> <div>Decreto Estadual nº 57.646, de 30 de maio de 2024: <a href="https://www.diariooficial.rs.gov.br/materia?id=1002017" target="_blank" rel="noopener">https://www.diariooficial.rs.gov.br/materia?id=1002017</a></div> <div>Situação de emergência: 323 municípios</div> <div>Estado de calamidade pública: 95 municípios</div>

opencc-by-4.0Mar 2019View details →

#nowplaying-rs

The nowplaying-rs dataset features context- and content features of listening events. It contains 11.6 million music listening events of 139K users and 346K tracks collected from Twitter. The dataset comes with a rich set of item content features and user context features, as well as timestamps of the listening events. Moreover, some of the user context features imply the cultural origin of the users, and some others - like hashtags - give clues to the emotional state of a user underlying a listening event. The dataset contains three files: <ul> <li>user_track_hashtag_timestamp.csv contains basic information about each listening event. For each listening event, we provide an id, the user_id, track_id, hashtag, created_at </li> <li>context_content_features.csv: contains all context and content features. For each listening event, we provide the id of the event, user_id, track_id, artist_id, content features regarding the track mentioned in the event (instrumentalness, liveness, speechiness, danceability, valence, loudness, tempo, acousticness, energy, mode, key) and context features regarding the listening event (coordinates (as geoJSON), place (as geoJSON), geo (as geoJSON), tweet_language, created_at, user_lang, time_zone, entities contained in the tweet).</li> <li>sentiment_values.csv contains sentiment information for hashtags. It contains the hashtag itself and the sentiment values gathered via four different sentiment dictionaries: AFINN, Opinion Lexicon, Sentistrength Lexicon and vader. For each of these dictionaries we list the minimum, maximum, sum and average of all sentiments of the tokens of the hashtag (if available, else we list empty values). However, as most hashtags only consist of a single token, these values are equal in most cases. Please note that the lexica are rather diverse and therefore, are able to resolve very different terms against a score. Hence, the resulting csv is rather sparse. The file contains the following comma-separated values: <hashtag, vader_min, vader_max, vader_sum,vader_avg,  afinn_min, afinn_max, afinn_sum, afinn_avg, ol_min, ol_max, ol_sum, ol_avg, ss_min, ss_max, ss_sum, ss_avg >, where we abbreviate all scores gathered over the Opinion Lexicon with the prefix 'ol'. Similarly, 'ss' stands for SentiStrength. </li> </ul> Please note that user_track_hashtag_timestamp.csv and context_content_features.csv partly provide the same features. We deliberately chose to do so to be able to provide useable files that do not have to be matched and joined with each other to perform e.g., simple recommendation tasks. Please also find the training and test-splits for the dataset in this repo. Also, Asmita provides prototypical implementations of a context-aware recommender system based on the dataset at https://github.com/asmitapoddar/nowplaying-RS-Music-Reco-FM. If you make use of this dataset, please cite the following paper where we describe and experiment with the dataset: @inproceedings{smc18, title = {#nowplaying-RS: A New Benchmark Dataset for Building Context-Aware Music Recommender Systems}, author = {Asmita Poddar and Eva Zangerle and Yi-Hsuan Yang}, url = {http://mac.citi.sinica.edu.tw/~yang/pub/poddar18smc.pdf}, year = {2018}, date = {2018-07-04}, booktitle = {Proceedings of the 15th Sound & Music Computing Conference}, address = {Limassol, Cyprus}, note = {code at https://github.com/asmitapoddar/nowplaying-RS-Music-Reco-FM}, tppubtype = {inproceedings} }

opencc-by-4.0Jul 2018View details →

#nowplaying-RS: A New Benchmark Dataset for Building Context-Aware Music Recommender Systems

Music recommender systems can offer users personalized and contextualized recommendation and are therefore important for music information retrieval. An increasing number of datasets have been compiled to facilitate research on different topics, such as content-based, context-based or next-song recommendation. However, these topics are usually addressed separately using different datasets, due to the lack of a unified dataset that contains a large variety of feature types such as item features, user contexts, and timestamps. To address this issue, we propose a large-scale benchmark dataset called #nowplaying-RS, which contains 11.6 million music listening events (LEs) of 139K users and 346K tracks collected from Twitter. The dataset comes with a rich set of item content features and user context features, and the timestamps of the LEs. Moreover, some of the user context features imply the cultural origin of the users, and some others—like hashtags—give clues to the emotional state of a user underlying an LE. In this paper, we provide some statistics to give insight into the dataset, and some directions in which the dataset can be used for making music recommendation. We also provide standardized training and test sets for experimentation, and some baseline results obtained by using factorization machines. The dataset contains three files: <ul> <li>user_track_hashtag_timestamp.csv contains basic information about each listening event. For each listening event, we provide an id, the user_id, track_id, hashtag, created_at </li> <li>context_content_features.csv: contains all context and content features. For each listening event, we provide the id of the event, user_id, track_id, artist_id, content features regarding the track mentioned in the event (instrumentalness, liveness, speechiness, danceability, valence, loudness, tempo, acousticness, energy, mode, key) and context features regarding the listening event (coordinates (as geoJSON), place (as geoJSON), geo (as geoJSON), tweet_language, created_at, user_lang, time_zone, entities contained in the tweet).</li> <li>sentiment_values.csv contains sentiment information for hashtags. It contains the hashtag itself and the sentiment values gathered via four different sentiment dictionaries: AFINN, Opinion Lexicon, Sentistrength Lexicon and vader. For each of these dictionaries we list the minimum, maximum, sum and average of all sentiments of the tokens of the hashtag (if available, else we list empty values). However, as most hashtags only consist of a single token, these values are equal in most cases. Please note that the lexica are rather diverse and therefore, are able to resolve very different terms against a score. Hence, the resulting csv is rather sparse. The file contains the following comma-separated values: <hashtag, vader_min, vader_max, vader_sum,vader_avg,  afinn_min, afinn_max, afinn_sum, afinn_avg, ol_min, ol_max, ol_sum, ol_avg, ss_min, ss_max, ss_sum, ss_avg >, where we abbreviate all scores gathered over the Opinion Lexicon with the prefix 'ol'. Similarly, 'ss' stands for SentiStrength. </li> </ul> Please also find the training and test-splits for the dataset in this repo. Also, prototypical implementations of a context-aware recommender system based on the dataset can be found at  <a href="https://github.com/asmitapoddar/nowplaying-RS-Music-Reco-FM">https://github.com/asmitapoddar/nowplaying-RS-Music-Reco-FM</a>. If you make use of this dataset, please cite the following paper where we describe and experiment with the dataset: @inproceedings{smc18, title = {#nowplaying-RS: A New Benchmark Dataset for Building Context-Aware Music Recommender Systems}, author = {Asmita Poddar and Eva Zangerle and Yi-Hsuan Yang}, url = {http://mac.citi.sinica.edu.tw/~yang/pub/poddar18smc.pdf}, year = {2018}, date = {2018-07-04}, booktitle = {Proceedings of the 15th Sound & Music Computing Conference}, address = {Limassol, Cyprus}, note = {code at https://github.com/asmitapoddar/nowplaying-RS-Music-Reco-FM}, tppubtype = {inproceedings} }

opencc-by-4.0Dec 2017View details →

Рис. 21–32. Lixus pulverulentus, кукоΛка, внешний виΔ и хетотаксия. 21 – виΔ снизу; 22 – виΔ сверху; 23 – виΔ сбоку; 24 – хетотаксия гоΛовы; 25 – хетотаксия переΔнеспинки; 26 – хетотаксия среΔнеспинки; 27 – хетотаксия заΔнеспинки; 28 – хетотаксия брюшного сегмента I; 29 – хетотаксия брюшного сегмента II; 30 – хетотаксия брюшного сегмента VII; 31 – хетотаксия брюшного сегмента VIII; 32 –хетотаксия брюшного сегмента IX. Figs 21–32. Lixus pulverulentus, pupa, habitus and chaetotaxy. 21 – ventral view; 22 – dorsal view; 23 – lateral view; 24 – chaetotaxy of head; 25 – chaetotaxy of pronotum; 26 – chaetotaxy of mesonotum; 27 – chaetotaxy of metanotum; 28 – chaetotaxy of abdominal segment I; 29 – chaetotaxy of abdominal segment II; 30 – chaetotaxy of abdominal segment VII; 31 – chaetotaxy of abdominal segment VIII; 32 – chaetotaxy of abdominal segment IX. ps – pseudocerci, Th n –thoracic segments, Ab n –abdominal segments; setae: as – apical, d – dorsal, ds – discal, fs – femoral, ls – lateral, os – orbital, pas – postatennal, pls – posterolateral, rs – rostral, sos – super-orbital, sls – superlateral, v – ventral, vs – vertical. in Description of the preimaginal stages and biology of the weevil Lixus (Dilixellus) pulverulentus (Scopoli, 1763) (Coleoptera: Curculionidae: Lixini)

Рис. 21–32. Lixus pulverulentus, кукоΛка, внешний виΔ и хетотаксия. 21 – виΔ снизу; 22 – виΔ сверху; 23 – виΔ сбоку; 24 – хетотаксия гоΛовы; 25 – хетотаксия переΔнеспинки; 26 – хетотаксия среΔнеспинки; 27 – хетотаксия заΔнеспинки; 28 – хетотаксия брюшного сегмента I; 29 – хетотаксия брюшного сегмента II; 30 – хетотаксия брюшного сегмента VII; 31 – хетотаксия брюшного сегмента VIII; 32 –хетотаксия брюшного сегмента IX. Figs 21–32. Lixus pulverulentus, pupa, habitus and chaetotaxy. 21 – ventral view; 22 – dorsal view; 23 – lateral view; 24 – chaetotaxy of head; 25 – chaetotaxy of pronotum; 26 – chaetotaxy of mesonotum; 27 – chaetotaxy of metanotum; 28 – chaetotaxy of abdominal segment I; 29 – chaetotaxy of abdominal segment II; 30 – chaetotaxy of abdominal segment VII; 31 – chaetotaxy of abdominal segment VIII; 32 – chaetotaxy of abdominal segment IX. ps – pseudocerci, Th n –thoracic segments, Ab n –abdominal segments; setae: as – apical, d – dorsal, ds – discal, fs – femoral, ls – lateral, os – orbital, pas – postatennal, pls – posterolateral, rs – rostral, sos – super-orbital, sls – superlateral, v – ventral, vs – vertical.

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