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713 results for “towns”
FIG. 8 in Early Permian insects from Saar-Nahe Basin of Odernheim town site, Rheinland-Pfalz in Germany (Insecta, Grylloblattida, Blattinopseida)
FIG. 8. — Blattinopsidae Bolton, 1925, gen. et sp. indet., photograph of forewing venation (MNHN.F.A31048). Scale bar: 3 mm.
FIG. 7 in Early Permian insects from Saar-Nahe Basin of Odernheim town site, Rheinland-Pfalz in Germany (Insecta, Grylloblattida, Blattinopseida)
FIG. 7. — Blattinopsidae Bolton, 1925, gen. et sp. indet., drawing of forewing venation (MNHN.F.A31048). Scale bar: 3 mm.
FIG. 5 in Early Permian insects from Saar-Nahe Basin of Odernheim town site, Rheinland-Pfalz in Germany (Insecta, Grylloblattida, Blattinopseida)
FIG. 5. — Blattinopsidae Bolton, 1925, gen. et sp. indet., drawing of forewing venation (MNHN.F.A31047). Scale bar: 3 mm.
FIG. 4. — Oborella brauckmanni n in Early Permian insects from Saar-Nahe Basin of Odernheim town site, Rheinland-Pfalz in Germany (Insecta, Grylloblattida, Blattinopseida)
FIG. 4. — Oborella brauckmanni n. sp., photograph of forewing venation (holotype MNHN.F.A31044). Scale bar: 3 mm.
FIG. 3. — Oborella brauckmanni n in Early Permian insects from Saar-Nahe Basin of Odernheim town site, Rheinland-Pfalz in Germany (Insecta, Grylloblattida, Blattinopseida)
FIG. 3. — Oborella brauckmanni n. sp., drawing of forewing venation (holotype MNHN.F.A31044). Scale bar: 3 mm.
FIG. 6 in Early Permian insects from Saar-Nahe Basin of Odernheim town site, Rheinland-Pfalz in Germany (Insecta, Grylloblattida, Blattinopseida)
FIG. 6. — Blattinopsidae Bolton, 1925, gen. et sp. indet., photograph of forewing venation (MNHN.F.A31047). Scale bar: 3 mm.
Text-fig. 1. Sampling areas in Çankırı province: the village of Sakarcaören near to the town of Orta (green circle) in the east of GVP, and the other sites (yellow circles), volcanic centers (red circles) and the border of GVP. The sites marked as yellow circles: ELM, Elmali village; SOG, Soguksu National Park; BUG, Bugralar village; INO, Inozu Valley South Side; INL, Inozu Valley North Side; KAR, Karasar village; MEN, Menceler Plateau; KIR, Kiraluc Site near Nuhhoca village; AGU, Asagiguney village; KUZ, Kuzca village (Bayam et al. 2018); PEL, Pelitcik village (Akkemik et al. 2009); GUD, Gudul (Akkemik et al. 2017); HOC, Hoçaş village and KOZ, Kozyaka village (Akkemik et al. 2016). The sites located in the western part (INO, INL, KAR, MEN, KIR, AGU, KUZ, HOC and KUZ) are from early – middle Burdigalian and Hancili Formation (Altun et al. 2002, Akbaş et al. 2002). The sites in the central part (GUD, BUG, ELM, PEL and SOG) are from middle – late Burdigalian, Pazar Formation (Kazancı 2012, Sen et al. 2017), and finally the fossil site in the east part of GVP is the late Miocene, Hüyükköy Formation (Sengüler 2007). in The First Glyptostroboxylon And Taxodioxylon Descriptions From The Late Miocene Of Turkey And Palaeoclimatological Evaluation
Text-fig. 1. Sampling areas in Çankırı province: the village of Sakarcaören near to the town of Orta (green circle) in the east of GVP, and the other sites (yellow circles), volcanic centers (red circles) and the border of GVP. The sites marked as yellow circles: ELM, Elmali village; SOG, Soguksu National Park; BUG, Bugralar village; INO, Inozu Valley South Side; INL, Inozu Valley North Side; KAR, Karasar village; MEN, Menceler Plateau; KIR, Kiraluc Site near Nuhhoca village; AGU, Asagiguney village; KUZ, Kuzca village (Bayam et al. 2018); PEL, Pelitcik village (Akkemik et al. 2009); GUD, Gudul (Akkemik et al. 2017); HOC, Hoçaş village and KOZ, Kozyaka village (Akkemik et al. 2016). The sites located in the western part (INO, INL, KAR, MEN, KIR, AGU, KUZ, HOC and KUZ) are from early – middle Burdigalian and Hancili Formation (Altun et al. 2002, Akbaş et al. 2002). The sites in the central part (GUD, BUG, ELM, PEL and SOG) are from middle – late Burdigalian, Pazar Formation (Kazancı 2012, Sen et al. 2017), and finally the fossil site in the east part of GVP is the late Miocene, Hüyükköy Formation (Sengüler 2007).
Vegetation cover fraction in each town block across Japan
<p>Although the percent of green space is the most frequently used index for quantifying urban green, it is currently unavailable in most cities in Japan. Here we provide an open dataset of the index for each town block across the country using Sentinel-2 satellite images in Google Earth Engine. The dataset is calibrated and validated with airborne datasets obtained in nine cities in Tokyo.</p> <p> </p> <p><strong>日本全国の町丁目別の緑被率の公開</strong></p> <ul> <li>都市の緑を定量化する指標としては、緑被率が最もよく使われていますが、現在、日本のほとんどの都市では緑被率を利用することができません。ここでは、Sentinel-2の衛星画像をGoogle Earth Engineを用いて、日本全国の町丁目別の緑被率をオープンデータとして提供しています。このデータセットは、東京都内の9区の航空機データ(各区のWEBサイトで公開)を用いて校正・検証されています。</li> <li>ライセンスに関してデータは<a href="https://creativecommons.org/licenses/by/4.0/deed.ja">CC BY 4.0</a>で公開しています。</li> <li>校正・検証に関しての詳細は、<a href="https://doi.org/10.3130/aijt.28.521">日本建築学会技術報告集</a>に掲載しています。 </li> <li>「FRAC_VEG」が緑被率です。</li> <li>境界データは「平成27年国勢調査」を用いています。</li> </ul> <p> </p> <p><strong>データ概要</strong></p> <p>年:2020</p> <p>空間分解能:町丁目(平成27年国勢調査)</p> <p> </p> <p><strong>引用方法</strong></p> <p>・データの利用のみの場合</p> <p>Kiyono Tomoki, Fujiwara Kunihiko, & Tsurumi Ryuta. (2021). Vegetation cover fraction in each town block across Japan (1.0.1) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5553516">https://doi.org/10.5281/zenodo.5553516</a></p> <p>・上記以外の場合(この取り組みそのものや,アルゴリズム等への言及)</p> <p>清野友規, 藤原邦彦, 鶴見隆太. Google Earth Engineを用いた町丁目別緑被率オープンデータ(全国版)の作成と評価, 日本建築学会技術報告集, 2022, 28 巻, 68 号, p. 521-526, https://doi.org/10.3130/aijt.28.521</p> <p> </p> <p><strong>問い合わせ</strong></p> <p>意見・要望・感想などお気軽にお問い合わせください。</p> <p>鶴見隆太(日建設計総合研究所) tsurumi.ryuta@nikken.jp</p> <p> </p> <p><strong>更新情報</strong></p> <p>v1.0.1 2021/10/7</p> <p>・エクセルでの文字化けを解消(文字コード:BOM付UTF-8)</p> <p>・町丁目のユニーク識別子であるKEY_CODE(11桁)を追加</p> <p>v0.1.0</p> <p>・初期バージョンをリリース</p> <p> </p> <p><strong>PI</strong></p> <p>Kiyono Tomoki</p>
Tiny Towns Scorer dataset
<p>This is the dataset and model used for <a href="https://github.com/MagicShoebox/vt-cs4664-tiny-towns-scorer">Tiny Towns Scorer</a>, a computer vision project completed as part of CS 4664: Data-Centric Computing Capstone at Virginia Tech. The goal of the project was to calculate player scores in the board game <a href="https://www.alderac.com/tiny-towns/">Tiny Towns</a>.</p> <p>The dataset consists of 226 images and associated annotations, intended for object detection. The images are photographs of players' game boards over the course of a game of Tiny Towns, as well as photos of individual game pieces taken after the game. Photos were taken using hand-held smartphones. Images are in JPG and PNG formats. The annotations are provided in TFRecord 1.0 and CVAT for Images 1.1 formats.</p> <p>The weights for the trained RetinaNet-portion of the model are also provided.</p>
Sky Over the Town of Nindirí, in Masaya, Nicaragua
<p>Honourable mention in the 2022 IAU OAE Astrophotography Contest, category Still images of celestial patterns.</p> <p> </p> <p>This image was taken in Tisma, Nicaragua, in April 2022 during a night illuminated by the Moon. Still, some constellations are visible, but the Milky Way, which runs through the image with some of its brightest regions, hides in the celestial background, partially obscured by terrestrial clouds.</p> <p>The Scorpion, one of the constellations of the Zodiac, is seen on the left side. We can first spot the brightest star, Antares, in the middle of a group of three in an asterism that looks like the stem of a flower growing from the clouds upwards. Its petals are the head of the scorpion. In the brightness of moonlight, the orange colour of Antares is not clearly visible. The three stars that comprise the head of the scorpion are called Acrab, Dschubba and Fang. They were recently named by the IAU in order to show the global diversity of constellations. Acrab is the Arabic term for the scorpion and Fang (The Room) is the name of the Chinese constellation made out of these stars. The Scorpion’s tail extends to the bottom of the image with the sting just above the horizon in the gap between clouds.</p> <p>The constellation Centaurus is visible in the middle of this image. We can first recognise Crux and the two pointer stars, Alpha and Beta Centauri right above the clouds. The pointers are the front hooves of the centaur, while the stars of Crux were originally (before Christanity, in antiquity) considered one of the rear legs of the centaur. The stars in the front hooves are Rigil Kentaurus (mixed Arabic and Latin meaning The Foot of the Centaur), to the left, and Hadar, to the right. With Crux at the rear they comprise the legs of the mythological creature’s horse body. The humanoid torso is represented by two bright stars on the shoulders and three fainter stars for the head. The star tale says that this figure represents Chiron, the only decent one among the centaurs who was well educated and the teacher of all great heroes.</p> <p>The Christian navigators who sailed to the New World around 1500 used these asterisms, although they had never seen them before in Europe. These included explorer Amerigo Vespucci whose name was given to the continent America. His shipmate Andrea Corsali, who came from Florence, where the poet Dante Alighieri was famous for his Divine Comedy written two centuries earlier, was reminded of Dante's poem by the four bright stars of Crux. It was probably in this context that they happened to invent the constellation Crux. Originally, the navigators only used the asterism for practical purposes but some time later, in the period of Christian religious wars between several new churches, celestial cartographers used the cross as a symbol of unity and against war.</p> <p>Credit: René Antonio Urroz Álvarez/IAU OAE (<a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC BY 4.0</a>)</p>
L-TOWN simulated measurement without faults or cyber-attacks for scenarios with masking
<p>Additional resources for repository <a href="https://github.com/asztyber/wdn-simulation">asztyber/wdn-simulation</a></p> <p>Required to run scenarios with masking.</p>
Ocean Data Hours - Ocean Race - Ocean Village - Genova Pavilllion - Cape Town 23.02.2023
<p>The ocean plays a significant role in the Earth's system, and understanding its importance is crucial for ensuring sustainable management and exploitation. Ocean observation should not be a task for experts and scientists alone.</p> <p>Citizen science is a form of scientific collaboration where members of the public participate in scientific research projects, providing data and observations that can be analyzed by researchers.</p> <p>This workshop presents a series of CS initiatives exploiting low cost technologies for ocean data collection by showing and discussing their maturity level and how scientists can already use these data.</p> <p>By supporting citizen science initiatives, policymakers can democratize marine observation science, creating a new type of self-driven, sustainable, and cost-efficient observatory concept, and at the same time, providing for the making of informed decisions based on the best available information.</p> <p>Ocean Data Hours is a series of workshops and talks organized in the Genova Pavillion at the Ocean Village - Ocean Race 2023. </p>
C-Town dataset generated with DHALSIM
<p>Simulations of the C-Town water distribution system under normal operating conditions, and disruptive network anomalies, and cyber attacks. These simulations were run using the DHALSIM simulator. The dataset includes two types of physical data: ground truth and SCADA information. In addition, the dataset includes captures of all network packets seen by the PLCs and SCADA server during the simulation. DHALSIM is a co-simulation environment for Water Distribution Systems that combines EPANET and MiniCPS to generate more realistic simulations of water distribution systems.</p> <p> </p> <p>The dataset includes all configuration files required to replicate these results, using the DHALSIM simulator.</p> <p>DHALSIM Github link:https://github.com/afmurillo/DHALSIM/tree/master/dhalsim</p> <p>If you use this dataset, please cite the paper that was written alongside this dataset: https://ascelibrary.org/doi/abs/10.1061/JWRMD5.WRENG-5854 </p>
[ELMI2023] BioImage Town (BIT) FAIR Data Metro Map
<p>Figures created collaboratively by the presenters of the Data Management and Analysis session of ELMI2023 (https://elmi2023.eu/) for their presentations. They represent an idealized metro through which data ("the passengers") travel between various solutions ("the stops") within bioimaging ("BioImage Town"), but also connecting to IT solutions, metadata, and other areas, though of course the real situation is much more complicated. Working together, we should be able to the improve the number of easy-to-use, performant, and complete solutions through BioImage Town for the benefit of the community.</p>
The experience base of the Institute of Soil Science, Agrotechnologies and Plant Protection in the town of Bozhurishte, region Sofia - on an area of 7.2 decares - First experiment
<p>A first field experiment was carried out in the Experimental field Bozhurishte on Leached Smolnitsa with corn for grain (Zea mays, L.) in 2 crop rotations in 2022 under TUdi project. The paper presents the results and short analysis. </p>
[HCB] BioImage Town (BIT) FAIR Data Metro Map
<p>Figures created collaboratively by the presenters of the Data Management and Analysis session of ELMI2023 (https://elmi2023.eu/) for their presentations. They represent an idealized metro through which data ("the passengers") travel between various solutions ("the stops") within bioimaging ("BioImage Town"), but also connecting to IT solutions, metadata, and other areas, though of course the real situation is much more complicated. Working together, we should be able to the improve the number of easy-to-use, performant, and complete solutions through BioImage Town for the benefit of the community.</p> <p>See previous version at https://zenodo.org/record/8019760</p> <p> </p>
Comparing travel behavior and opportunities to increase transportation sustainability in small cities, towns, and rural communities
<p>The vast majority of travel behavior and sustainable transportation research has focused on urban areas. A rural perspective is lacking. In this study, we aim to dive deeper into understanding how people travel and their perceptions and opinions about various components of travel in a majority rural state. By speaking directly with Vermonters through in-person interviews, we obtain uniquely personal points of view and analyze them for commonalities and differences between urban, suburban, and rural Vermonters. We ask questions on day-to-day challenges of traveling, suggestions for reducing greenhouse gas (GHG) emissions, responses to fuel prices, and opinions on electric vehicles. Some of our key findings include that rural areas struggle most with traveling long distances to reach services, urban areas are more concerned with traffic, and opinions on electric vehicle (EV) ownership are consistent across the state, with people being likely to consider owning an EV if costs were to decrease. Our interviews identify additional questions that should be evaluated further to help states develop practical and effective policies aimed at reducing GHG emissions in rural areas. We also recommend further in-depth survey research to provide a more complete picture of the potential to shift travel behavior, particularly in rural areas. This research adds to the body of knowledge in a historically understudied population, enabling the research community to better understand and work more closely with small and rural communities to address climate change and achieve deeper GHG emission reductions.</p>
Comparing travel behavior and opportunities to increase transportation sustainability in small cities, towns, and rural communities
Open the record for dataset details and reuse information.
Firescars with distance from towns in Interior Alaska (1988-1993)
Buffers around settlements in Alaska in 5 km bins to a maximum distance of 45 km overlaid with 1988 - 1993 firescars, clipped to Interior Alaska.
Firescars with distance from towns in Interior Alaska (1994-1999)
Buffers around settlements in Alaska in 5 km bins to a maximum distance of 45 km overlaid with 1994 - 1999 firescars, clipped to Interior Alaska.
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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.