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3,105 results for “Cellular”
Data in support of 'Mechanistic insights into plant community responses to environmental variables: genome size, cellular nutrient investments, and metabolic trade-offs.'
Data was collected to examine whether and how the plant genome size (GS) influences traits (stomata size, stomata density, cellular and tissue level carbon (C), nitrogen (N), and phosphorus (P) contents) and metabolic-tradeoffs (of photosynthesis, evapotranspiration, water-use, efficiency) of plants in treatment plots in which nothing, N, P, or NP had been annually added. Data was collected from ~500 plants from seven grassland sites that are all part of the Nutrient Network (https://nutnet.org), a globally distributed experiment in which plots have different nutrient amendment treatments that are administered identically to allow cross-site comparisons of the effects of nutrients on biodiversity patterning. The sites chosen varied along a North-South latitude, longitude, mean annual precipitation (MAP) and mean annual temperature (MAT) gradient.
Network Measurements while Uploading 5.6 KB Files from Moving Buses to Cellular Networks in Varmland, Sweden.
<p>The dataset and the collection methodology are described and used in the following papers:</p> <ul> <li>Ben Abdesslem, Fehmi, Henrik Abrahamsson, and Bengt Ahlgren.<br> "<em><strong>Measuring Mobile Network Multi-Access for Time-Critical C-ITS Applications" </strong></em><br> Network Traffic Measurement and Analysis Conference (TMA'18), Vienna, Austria (2018).</li> <li>Ben Abdesslem, Fehmi, Henrik Abrahamsson, and Bengt Ahlgren.<br> "<em><strong>Cellular Network Multi-Access Measurements on the Roads of Värmland, Sweden.</strong></em>" <br> <em>arXiv preprint arXiv:1805.06814</em> (2018).</li> <li>Henrik Abrahamsson, Ben Abdesslem, Fehmi, Bengt Ahlgren, Anna Brunstrom, Ian Marsh and Mats Björkman.<br> "<em><strong>Connected Vehicles in Cellular Networks: Multi-access versus Single-access Performance</strong></em>" <br> 2nd Workshop on Mobile Network Measurement (MNM’18), Vienna, Austria (2018).</li> </ul> <p>The CSV file has the following columns:</p> <ul> <li>Index: Unique number for the transaction</li> <li>Timestamp: Time and date of the transaction</li> <li>Interface: Interface used by the transaction [op0, op1 or op2]</li> <li>TransactionTime: Duration of the transaction (in sec)</li> <li>Status: Result of the transaction [failed, senderror, timeout, or number of received bytes acknowledged]</li> <li>GpsTimestamp: Time and date of the GPS coordinates</li> <li>GpsLatitude: Last GPS latitude known</li> <li>GpsLongitude: Last GPS longitude known</li> <li>ModemTimestamp: Time and date of the modem properties</li> <li>ModemOperator: Name of the operator [op0, op1, op2]. The original names (Telia, Telenor, 3) have been replaced in a different order.</li> <li>ModemRSSI: RSSI (in dBm)</li> <li>ModemCID: Cell ID</li> <li>ModemDeviceMode: <ul> <li>UNKNOWN (0).</li> <li>DISCONNECTED (1).</li> <li>NO_SERVICE (2).</li> <li>2G (3).</li> <li>3G (4).</li> <li>LTE (5).</li> </ul> </li> <li>ModemDeviceSubmode: <ul> <li>UNKNOWN (0).</li> <li>UMTS (1).</li> <li>WCDMA (2).</li> <li>EVDO (3).</li> <li>HSPA (4).</li> <li>HSPA+ (5).</li> <li>DC HSPA (6).</li> <li>DC HSPA+ (7).</li> <li>HSDPA (8).</li> <li>HSUPA (9).</li> <li>HSDPA+HSUPA (10).</li> <li>HSDPA+ (11).</li> <li>HSDPA+HSUPA (12).</li> <li>DC HSDPA+ (13).</li> <li>DC HSDPA + HSUPA (14).</li> </ul> </li> <li>ModemLAC: Location Area Code</li> <li>ModemRSRP: RSRP (in dBm)</li> <li>ModemFrequency: Frequency in Mhz</li> <li>ModemRSRQ: RSRQ (in dBm)</li> <li>ModemBand: LTE band</li> <li>ModemPCI: LTE Physical Cell ID</li> <li>ModemECIO: Ec/Io</li> <li>ModemENODEBID: eNodeB ID</li> <li>ModemRSCP: RSCP (in dBm)</li> <li>bus: Bus number (head node number in Monroe)</li> <li>country: Country of operation [Sweden]</li> <li>protocol: protocol used [UDP, TCP or HTTPS]</li> <li>experiment: Experiment ID (one hour experiments)</li> <li>diff: Max time difference between the three simultaneous uploads (in ms)</li> <li>TransactionTime200: Transaction duration if timeout=200ms</li> <li>TransactionTime1000:Transaction duration if timeout=1000ms</li> <li>TransactionTime6000: Transaction duration if timeout=6000ms</li> <li>bestAvailability: Best availability over the whole experiment ID (%)</li> <li>bestAvailability200: Best availability over the whole experiment ID (%) if timeout=200ms</li> <li>bestAvailability1000: Best availability over the whole experiment ID (%) if timeout=1000ms</li> <li>best: Best duration (in sec)</li> <li>best1000: Best duration (in sec) if timeout=1000ms</li> <li>availability: Availability over the whole experiment ID (%)</li> <li>availability200: Availability over the whole experiment ID (%) if timeout=200ms</li> <li>availability1000: Availability over the whole experiment ID (%) if timeout=1000ms</li> <li>DayOfWeek: Day of the Week [Monday, ..., Sunday]</li> </ul>
Scanning electron microscope images of spruce needle homogenate and scanning electron microscope images of isolated small cellular particles from spruce needle homogenate
<p>Scanning electron microscope images of spruce needle homogenate and of isolated small cellular particles from spruce needle homogenate are presented. Each image is supplemented by description of the preparation of the sample and the data on the imaging technique and equipment. The data are curated by Veronika Kralj-Iglic and University of Ljubljana, Faculty of Health Sciences, Laboratory of Clinical Biophysics, and Anna Romolo, presently at University of Ljubljana, Faculty of Electrical Engineering, Laboratory of Physics, Ljubljana, Slovenia. Present address of Marko Jeran is: Department of Inorganic Chemistry and Technology, “Jožef Stefan” Institute, Ljubljana, Slovenia.</p>
3D model of antenna system embedded into building envelope for improved cellular signal transmission through load-bearing walls
<p>The purpose of this dataset is to supplement the data presented in our journal publication "Electromagnetic–Thermal Analyses of Distributed Antennas Embedded Into a Load-Bearing Wall" (see <a href="https://ieeexplore.ieee.org/document/10151683">https://ieeexplore.ieee.org/document/10151683</a>).</p> <p>This dataset contains the 3-D discretized model, without the internal numerical mesh, of the unit cell of the spiral antenna system embedded in a load bearing wall. The 3D model is in .STP format (see ISO 10303-21:2016), which can be imported into most commercial computer-aided design (CAD) software. The wall's dielectric properties are calculated using the model described in ITU-R P.2040-2 (<a href="https://www.itu.int/rec/R-REC-P.2040/en">https://www.itu.int/rec/R-REC-P.2040/en</a>, material parameter and calculation model are on pages 22-23). Materials used in the antenna system and their electrical and thermal parameters are given in the file materials.txt</p>
Ergolide Mediates Anti-Cancer Effects on Metastatic Uveal Melanoma Cells and Modulates their Cellular and Extracellular Vesicle proteomes
<p>Underlying dataset and extended dataset of the results described in the article "Ergolide Mediates Anti-Cancer Effects on Metastatic Uveal Melanoma Cells and Modulates their Cellular and Extracellular Vesicle proteomes".</p>
DUCC - Dataset for UAS Cellular Communications
<p><strong>Motivation</strong><br>The Dataset for Unmanned Aircraft System (UAS) Cellular Communications, short DUCC, was created with the aim of advancing communications for Beyond Visual Line of Sight (BVLOS) operations. With this objective in mind, datasets were generated to analyse the behaviour of cellular communications for UAS operations.</p> <p><strong>Measurement</strong><br>A measurement setup was implemented to execute the measurements. Two Sierra Wireless EM9191 modems possessing both LTE and 5G capabilities were utilized in order to establish a connection to the cellular network and measure the physical parameters of the air-link. Every modem was equipped with four Taoglas antennas, two of type TG 35.8113 and two of type TG 45.8113. To capture the measurements a Raspberry Pi 4B is used. All hardware components were integrated into a box and attached to a DJI Matrice 300 RTK. A connection to the drone controller has been established to obtain location, speed and attitude. To measure end-to-end network parameters, dummy data was exchanged bidirectionally between the Raspberry Pi and a server. Both the server as well as the Raspberry Pi are synchronized with the GPS time in order to measure the one-way packet delay. For this purpose, we utilised Iperf3 and customised it to suit our requirements. To ensure precise positioning of the drone a Real Time Kinematik (RTK) station was placed on the ground during the measurements.</p> <p>The measurements were performed at three distinct rural locations. Waypoint flights were undertaken with the points arranged in a cuboid formation maximizing the coverage of the air volume. Thereby, the campaigns were conducted with varying drone speeds. Moreover, for location A, different flight routes with rotated grids were implemented to reduce bias. Finally, a validation dataset is provided for location A, where the waypoints were calculated according to Quality of Service (QoS) based path-planning.</p> <p><strong>Dataset Structure and Usage</strong><br>The dataset's structure consists of:<br>-- Dataset<br> |-- LocationX<br> |-- RouteX (in case different routes at LocationX were created)<br> |-- LocXRouteX.kml (file containing the waypoints in the kml format)<br> |-- SpeedXMeterPerSecond (folder containing the datasets recorded with a specific drone speed)<br> |-- YYYY-MM-DD hh_mm_ss.s.pkl.gz (Dataset file)<br> |-- RouteY<br> |-- ...<br> |-- ...</p> <p>The dataset files can be loaded using the pandas module in python3. The file "load.py" provides a sample script for loading a dataset as well as the corresponding .kml file which contains the predefined waypoints. In the file "Parameter_Description.csv" each parameter measured is further explained.</p> <p><strong>License</strong><br>All datasets are copyright by us and published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International. This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license. This dataset is made available for academic use only. However, we take your privacy seriously! If you find yourself or personal belongings in this dataset and feel unwell about it, please contact us at automotive@oth-aw.de and we will immediately remove the respective data from our server.</p> <p><strong>Achnowledgement</strong><br>The authors gratefully acknowledge the following European Union H2020 -- ECSEL Joint Undertaking project for financial support including funding by the German Federal Ministry for Education and Research (BMBF): ADACORSA (Grant Agreement No. 876019, funding code 16MEE0039).</p>
Fourier-transform Infrared (FT-IR) spectroscopy fingerprints subpopulations of extracellular vesicles of different sizes and cellular origin
<p>Atomic Force Microscopy images of Large (LEV), Medium (MEV) and Small (SEV) Extrzcellular vesicles (EVs) from murine cell line B16 (B16-F10, ATCC CRL-647; Mus musculus, mouse; tissue: melanoma skin). Image size 8.3 x 8.3 um. Analysis mode: Tapping mode in air as described in Paolini et al. https://doi.org/10.1080/20013078.2020.1741174</p>
Combined unsupervised and semi-automated supervised analysis of flow cytometry data reveals cellular fingerprint associated with newly diagnosed pediatric type 1 diabetes
<p>Type 1 diabetes is a chronic autoimmune disease resulting in an immune-mediated loss of pancreatic β-cells; however, an unbiased and reproducible profiling of type 1 diabetes-specific circulating immunome at disease onset has yet to be explored. In this study, fresh whole blood was collected from a pediatric cohort of 107 patients with new-onset type 1 diabetes, 85 relatives of patients with type 1 diabetes with 0-1 islet autoantibodies, 58 patients with celiac disease or autoimmune thyroiditis and 76 healthy controls. Up to 6 mL of blood was collected from each subject into a VACUETTE® TUBE 6 ml ACD-B (Greiner). Fresh whole blood underwent red blood cell lysis, was washed and stained with specific monoclonal antibodies. Fresh whole blood samples were stained with five panels of antibodies labelled as T cells, T&NK cells, B cells, Tregs and DCs/monos encompassing main subsets of T cells, NK cells, B cells, Tregs, DCs and monocytes detected using 26 surface markers and the intracellular marker forkhead box P3 (FoxP3); for the Treg panel, intracellular staining was performed after fixation and permeabilization. Cells were acquired on a BD FACSCanto-II flow cytometer equipped with FACSDiva software (Becton Dickinson, Franklin Lakes, NJ). </p>
Cellular and Humoral Immune Responses after Immunisation with Low Virulent African Swine Fever Virus in the Large White Inbred Babraham Line and Outbred Domestic Pigs
<p>Raw data for manuscript. Contains temperature, clinical scores, qPCR, blood cell numbers and immune responses over time for two groups of pigs immunised with low virulent African swine fever virus and challenged with highly virulent virus. Data for each panel or figure is displayed on a separate worksheet in the file. The readme worksheet contains a brief description of each figure. The majority of data is displayed in an XY table format, with the number of days post immunisation with low virulent virus indicated.</p>
A cellular hierarchy in melanoma uncouples growth and metastasis
<p>Although melanoma is notorious for its high degree of heterogeneity and plasticity<sup>1,2</sup>, the origin and magnitude of cell state diversity remains poorly understood. Equally, it is not known whether melanoma growth and metastatic dissemination are supported by overlapping or distinct melanoma subpopulations. By combining mouse genetics, unbiased lineage tracing and quantitative modelling, single-cell and spatial transcriptomics, we provide evidence of a hierarchical model of tumour growth that mirrors the cellular and molecular logic underlying embryonic neural crest cell fate specification and differentiation. Our findings indicate that tumorigenic competence is associated with a spatially localized perivascular niche environment, a phenotype acquired through a NOTCH3-dependent intercellular communication pathway established by endothelial cells. Consistent with a model in which only a fraction of melanoma cells is fated to fuel growth, temporal single-cell tracing of a population of melanoma cells harbouring a mesenchymal-like state revealed that these cells do not contribute to primary tumour growth but, instead, constitutes a pool of metastatic-initiating cells that can switch cell identity while disseminating to secondary organs. Our data provide a spatially and temporally resolved map of the diversity and trajectories of cancer cell states within the evolving melanoma ecosystem and suggest that the ability to support growth and metastasis are limited to distinct pools of melanoma cells. The observation that these phenotypic competencies can be dynamically acquired upon exposure to specific niche signals warrant the development of therapeutic strategies that interfere with the cancer cell reprogramming activity of such microenvironmental cues.</p>
Dataset of "Comparison of Localization Methods for Internet of Things in 5G Cellular Networks: A Wide-scale Assessment"
<p>As the 3rd generation partnership project (3GPP) organization pushes out new releases,<br>positioning in heterogeneous mobile networks enables the achievement of the accuracy required<br>in the majority of industrial applications without dependence on global navigation<br>satellite systems (GNSS). This study presents the results gathered during an extensive measurement<br>campaign related to the practical applicability of localization in next-generation<br>heterogeneous networks. We present an accuracy comparison of basic timing advance (TA)<br>localization with the k-nearest neighbor (KNN), decision tree-based random forest (RF),<br>extreme gradient boosting (XGBoost), and long short-term memory (LSTM) recurrent neural<br>network. Our results demonstrate that TA cannot be considered an optimal solution<br>from the perspective of localization accuracy because the error roughly corresponds to the<br>average separation distance from the base station (BS) to the end device (ED). In addition,<br>we found that the LSTM approach is not optimal for the outdoor localization of moving<br>ED because of the combination of multiple factors, with sparse deployment being the most<br>important. The median value of the location error of the LSTM was more than 200m higher<br>than that of the TA for the self-validation dataset. However, a simple KNN regression shows<br>solid results for 5G New Radio (NR) operating in the non-standalone (NSA) mode. KNN<br>provided the most accurate results of all methods, with median error values of approximately<br>12 (k=3) and 82 (k=5) m for the self-validated and cross-validated datasets, respectively.</p>
Results of the DYNAMO (Dynamic MEC Orchestration of Cellular Networks) experiment in the Fed4FIRE+ testbeds
<p>The main objective of the DYNAMO Fed4FIRE+ experiment was to perform Network Function Virtualization (NFV) Management and Network Orchestration (MANO) of a cellular network on top of cloud infrastructures, exploring one of the key enabling technologies for 5G systems and beyond. DYNAMO used cloud and radio access facilities at the IRIS testbed and cloud facilities at the University of Vigo (UVIGO) to deploy an end-to-end (E2E) cellular network and perform elastic changes on it if needed. The geographic distance in between facilitated the setup of a realistic Multi-Access Edge Computing (MEC) use case, where the virtual Evolved Packet Core (vEPC) was deployed at UVIGO (Spain) and the access network, i.e., the User Equipment (UE), the e-Node-B (eNB) and edge cloud, were implemented on IRIS testbed (Ireland).<br> <br> While the initial deployment of the E2E cellular network may be considered as static, DYNAMO showcases the elasticity that an E2E cellular network may need in runtime. Hence, we presented a use case consisting of a latency sensitive E2E cellular network (network slice), where the endpoint of the UE connection was initially located in the core (UVIGO) but then migrated to the edge (IRIS), in case the UE's latency ranges were unacceptable.<br> <br> In this regard, the UE reported the experienced latency to Open Network Automation Platform (ONAP), which is responsible to trigger specific policy-driven control actions if a predefined Service-Level Agreement (SLA) is violated.<strong> <em>This datased includes the reports provided by the UE to ONAP.</em></strong><br> <br> As a result, the endpoint of the data plane of the UE is automatically moved to the access network (IRIS) thus reducing significantly the latency for the UE. For the access part of the network, we implemented one srsLTE e-Node-B (eNB), one srsLTE User Equipment (UE) and a Devstack (Edge Cloud) in virtual machines on IRIS testbed. In addition, we also implemented an SDN switch controlled by an ONOS SDN controller. For the core part of the network, we considered a disaggregated vEPC from Open Air Interface (OAI) on a Devstack (Core Cloud) at UVIGO.<br> <br> DYNAMO has succeeded in the integration of a broad set of network elements and technologies between the two different domains (UVIGO and IRIS testbed) and fulfilled all initial objectives: (i) establishing communication between ONAP and IRIS testbed to deploy generic VNFs on core and edge clouds, (ii), deployment of an E2E cellular network with UE and eNB in IRIS and the vEPC at UVIGO, (iii), sending telemetry of the UE to ONAP and (iv) designing and testing closed-loop control actions in ONAP to migrate the data plane of the UE to the edge in case of unsatisfactorily SLA. DYNAMO paves the way to a broad set of future 5G experiments that will require resource orchestration, such as the deployment of network slices or the automatic scheduling of services in the limited resources of Edge Clouds.</p> <p>This repository contains the information sent from the UE to ONAP, in order to decide if the latency between the UE and the PGW is OK or if an action has to be considered to reduce such latency.<br> </p> <p> </p>
Cellularity & Growth Form: Old Version
<p>Cellularity & growth form data derived from the following sources: </p> <p>Abdullin, S.R., Bagmet, V.B., Nikulin, A.Y., Nikulin, V.Y., Gorpenchenko, T.Y., Grishin, S.Y., Allaguvatova, R.Z. and Gontcharov, A.A., 2022. Emended description of the genus Eremochloris (Trebouxiophyceae, Chlorophyta), with Eremochloris kamchatica sp. nov. from Kamchatka, Russia. Phycologia, 61(2), pp.175-183. <a href="<p></p>https://doi.org/10.1080/00318884.2021.2024710"></a></p><p></p><a href="<p></p>https://doi.org/10.1080/00318884.2021.2024710">https://doi.org/10.1080/00318884.2021.2024710</a><p></p> <p>Adl, S.M., Bass, D., Lane, C.E., Lukeš, J., Schoch, C.L., Smirnov, A., Agatha, S., Berney, C., Brown, M.W., Burki, F., Cárdenas, P., Čepička, I., Chistyakova, L., Campo, J. del, Dunthorn, M., Edvardsen, B., Eglit, Y., Guillou, L., Hampl, V., Heiss, A.A., Hoppenrath, M., James, T.Y., Karnkowska, A., Karpov, S., Kim, E., Kolisko, M., Kudryavtsev, A., Lahr, D.J.G., Lara, E., Gall, L.L., Lynn, D.H., Mann, D.G., Massana, R., Mitchell, E.A.D., Morrow, C., Park, J.S., Pawlowski, J.W., Powell, M.J., Richter, D.J., Rueckert, S., Shadwick, L., Shimano, S., Spiegel, F.W., Torruella, G., Youssef, N., Zlatogursky, V., Zhang, Q., 2019. Revisions to the Classification, Nomenclature, and Diversity of Eukaryotes. Journal of Eukaryotic Microbiology 66, 4–119. <a href="<p></p>https://doi.org/10.1111/jeu.12691"></a></p><p></p><a href="<p></p>https://doi.org/10.1111/jeu.12691">https://doi.org/10.1111/jeu.12691</a><p></p> <p>Alberghina, J.S., Vigna, M.S., Confalonieri, V.A., 2006. Phylogenetic position of the Oedogoniales within the green algae (Chlorophyta) and the evolution of the absolute orientation of the flagellar apparatus. Plant Syst. Evol. 261, 151–163. <a href="<p></p>https://doi.org/10.1007/s00606-006-0449-2"></a></p><p></p><a href="<p></p>https://doi.org/10.1007/s00606-006-0449-2">https://doi.org/10.1007/s00606-006-0449-2</a><p></p> <p>Amaral, R., Fawley, K.P., Němcová, Y., Ševčíková, T., Lukešová, A., Fawley, M.W., Santos, L.M. and Eliáš, M., 2020. Toward Modern Classification of Eustigmatophytes, Including the Description of Neomonodaceae Fam. Nov. and Three New Genera. Journal of Phycology, 56(3), pp.630-648. <a href="<p></p>https://doi.org/10.1111/jpy.12980"></a></p><p></p><a href="<p></p>https://doi.org/10.1111/jpy.12980">https://doi.org/10.1111/jpy.12980</a><p></p> <p>Andersen, R.A., 1987. Synurophyceae classis nov., a new class of algae. American journal of botany, 74(3), pp.337-353. <a href="<p></p>https://doi.org/10.2307/2443810"></a></p><p></p><a href="<p></p>https://doi.org/10.2307/2443810">https://doi.org/10.2307/2443810</a><p></p> <p>Antonio, M., Schulze-Makuch, D., 2012. Toward a New Understanding of Multicellularity. Hypotheses in the Life Sciences 2, 4–14. <a href="<p></p>http://www.hy-ls.org/index.php/hyls/article/view/89/0"></a></p><p></p><a href="<p></p>http://www.hy-ls.org/index.php/hyls/article/view/89/0">http://www.hy-ls.org/index.php/hyls/article/view/89/0</a><p></p> <p>Arneson, R.D., 1973. Pseudotetracystis, a new chlorosarcinean alga. Journal of Phycology, 9(1), pp.10-14. <a href="<p></p>https://doi.org/10.1111/j.1529-8817.1973.tb04056.x"></a></p><p></p><a href="<p></p>https://doi.org/10.1111/j.1529-8817.1973.tb04056.x">https://doi.org/10.1111/j.1529-8817.1973.tb04056.x</a><p></p> <p>Aslam, Z., Shin, W., Kim, M.K., Im, W.T. and Lee, S.T., 2007. Marinichlorella kaistiae gen. et sp. nov.(Trebouxiophyceae, Chlorophyta) based on polyphasic taxonomy. Journal of Phycology, 43(3), pp.576-584. <a href="<p></p>https://doi.org/10.1111/j.1529-8817.2007.00345.x"></a></p><p></p><a href="<p></p>https://doi.org/10.1111/j.1529-8817.2007.00345.x">https://doi.org/10.1111/j.1529-8817.2007.00345.x</a><p></p> <p>Badewitz, H. (2004). The genus Microcorycia Cockerell, 1911 (Testacealobosia, Rhizopoda, Protozoa). A critical monograph of the genus including a first description of a new species: Microcorycia scutella n. sp. Lauterbornia 50: 111-146. </p> <p>Bakker, M.E., De Jong, Y.S. and Lokhorst, G.M., 1997. The flagellar apparatus ultrastructure in Leptosira erumpens (Deason & Bold) lukesová and its contribution to the understanding of phylogenese relationships within the microthamniales (chlorophyta). Archiv für Protistenkunde, 148(1-2), pp.17-31. <a href="<p></p>https://doi.org/10.1016/S0003-9365(97)80033-4"></a></p><p></p><a href="<p></p>https://doi.org/10.1016/S0003-9365(97)80033-4">https://doi.org/10.1016/S0003-9365(97)80033-4</a><p></p> <p>Barcytė, D., Hodač, L. and Nedbalová, L., 2017. Lunachloris lukesovae gen. et sp. nov.(Trebouxiophyceae, Chlorophyta), a novel coccoid green alga isolated from soil in South Bohemia, Czech Republic. European Journal of Phycology, 52(3), pp.281-291. <a href="<p></p>https://doi.org/10.1080/09670262.2017.1283541"></a></p><p></p><a href="<p></p>https://doi.org/10.1080/09670262.2017.1283541">https://doi.org/10.1080/09670262.2017.1283541</a><p></p> <p>Barsanti, L., Frassanito, A.M., Passarelli, V., Evangelista, V., Etebari, M., Paccagnini, E., Lupetti, P., Lenzi, P., Verni, F. and Gualtieri, P., 2013. Tetraflagellochloris mauritanica gen. et sp. nov.(Chlorophyceae), a new flagellated alga from the mauritanian desert: morphology, ultrastructure, and phylogenetic framing. Journal of Phycology, 49(1), pp.178-193. <a href="<p></p>https://doi.org/10.1111/j.1529-8817.2012.01232.x"></a></p><p></p><a href="<p></p>https://doi.org/10.1111/j.1529-8817.2012.01232.x">https://doi.org/10.1111/j.1529-8817.2012.01232.x</a><p></p> <p>Bass, D., Chao, E.E.-Y., Nikolaev, S., Yabuki, A., Ishida, K., Berney, C., Pakzad, U., Wylezich, C., Cavalier-Smith, T., 2009. Phylogeny of Novel Naked Filose and Reticulose Cercozoa: Granofilosea cl. n. and Proteomyxidea Revised. Protist 160, 75–109. <a href="<p></p>https://doi.org/10.1016/j.protis.2008.07.002"></a></p><p></p><a href="<p></p>https://doi.org/10.1016/j.protis.2008.07.002">https://doi.org/10.1016/j.protis.2008.07.002</a><p></p> <p>Becker, B., Marin, B., 2009. Streptophyte algae and the origin of embryophytes. Annals of Botany 103, 999–1004. <a href="<p></p>https://doi.org/10.1093/aob/mcp044"></a></p><p></p><a href="<p></p>https://doi.org/10.1093/aob/mcp044">https://doi.org/10.1093/aob/mcp044</a><p></p> <p>Bernard, Catherine, Alastair G. B. Simpson & David J. Patterson (2000) Some free-living flagellates (protista) from anoxic habitats Ophelia 52(2):113-142. <a href="<p></p>https://doi.org/10.1080/00785236.1999.10409422"></a></p><p></p><a href="<p></p>https://doi.org/10.1080/00785236.1999.10409422">https://doi.org/10.1080/00785236.1999.10409422</a><p></p> <p>Berney, C., Geisen, S., Van Wichelen, J., Nitsche, F., Vanormelingen, P., Bonkowski, M. and Bass, D., 2015. Expansion of the __reticulosphere__: diversity of novel branching and network-forming amoebae helps to define Variosea (Amoebozoa). Protist, 166(2):271-295. <a href="<p></p>https://doi.org/10.1016/j.protis.2015.04.001"></a></p><p></p><a href="<p></p>https://doi.org/10.1016/j.protis.2015.04.001">https://doi.org/10.1016/j.protis.2015.04.001</a><p></p> <p>Biard, T., 2022. Diversity and ecology of Radiolaria in modern oceans. 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Cellularity & Growth Form Data Version April 2023
<p>Cellularity & growth form data derived from the following sources: </p> <p>Abdullin, S.R., Bagmet, V.B., Nikulin, A.Y., Nikulin, V.Y., Gorpenchenko, T.Y., Grishin, S.Y., Allaguvatova, R.Z. and Gontcharov, A.A., 2022. Emended description of the genus Eremochloris (Trebouxiophyceae, Chlorophyta), with Eremochloris kamchatica sp. nov. from Kamchatka, Russia. 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The genus Microcorycia Cockerell, 1911 (Testacealobosia, Rhizopoda, Protozoa). A critical monograph of the genus including a first description of a new species: Microcorycia scutella n. sp. Lauterbornia 50: 111-146. </p> <p>Bakker, M.E., De Jong, Y.S. and Lokhorst, G.M., 1997. The flagellar apparatus ultrastructure in Leptosira erumpens (Deason & Bold) lukesová and its contribution to the understanding of phylogenese relationships within the microthamniales (chlorophyta). Archiv für Protistenkunde, 148(1-2), pp.17-31. <a href="https://doi.org/10.1016/S0003-9365(97)80033-4">https://doi.org/10.1016/S0003-9365(97)80033-4</a></p> <p>Barcytė, D., Hodač, L. and Nedbalová, L., 2017. Lunachloris lukesovae gen. et sp. nov.(Trebouxiophyceae, Chlorophyta), a novel coccoid green alga isolated from soil in South Bohemia, Czech Republic. 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Department of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service. </p> <p>Bovee, E.C., 1953. Oscillosignum nov. gen. proboscidium nov. sp., type form of its genus, family Mayorellidae, order Amoebida. Transactions of the American Microscopical Society, 72(4):328-332. <a href="https://doi.org/10.2307/3223477">https://doi.org/10.2307/3223477</a></p> <p>Bovee, E.C., 1985. The lobose amebas: III. Descriptions of nine new conopodous amebas of the genus Vexillifera Schaeffek, 1926, emd. Bovee 1951, 1970, with comments on the genus. Archiv für Protistenkunde, 129(1-4):101-118. <a href="https://doi.org/10.1016/S0003-9365(85)80013-0">https://doi.org/10.1016/S0003-9365(85)80013-0</a></p> <p>Brown, M.W., Silberman, J.D. (2013). The Non-dictyostelid Sorocarpic Amoebae. In: Romeralo, M., Baldauf, S., Escalante, R. (eds) Dictyostelids. 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Plant Systematics and Evolution, 230(3), pp.161-171. <a href="https://doi.org/10.1007/s006060200002">https://doi.org/10.1007/s006060200002</a></p> <p>Wujek, D.E. The first occurrence of the coccoid green alga Borodinella polytetras. Miller in North America (Michigan). Great Lakes Bot. 59(3-4):94-98. <a href="http://hdl.handle.net/2027/spo.0497763.0058.107">http://hdl.handle.net/2027/spo.0497763.0058.107</a></p> <p>Wujek, D.E., 2016. The chlorococcalean Green Alga Hydrianum Rabenhorst from North America (Kansas and Michigan). Transactions of the Kansas Academy of Science, 119(1), pp.105-108. <a href="https://www.jstor.org/stable/24887848">https://www.jstor.org/stable/24887848</a></p> <p>Yoon H.S. et al. (2016) Rhodophyta. In: Archibald J. et al. (eds) Handbook of the Protists. Springer, Cham. <a href="https://doi.org/10.1007/978-3-319-32669-6_33-1">https://doi.org/10.1007/978-3-319-32669-6_33-1</a></p>
Plasmodesmata Act as Unconventional Membrane Contact Sites Regulating Inter-Cellular Molecular Exchange in Plants.
<p>This table contains peaks aera values from LC-MS for lipidomic quantification of PIP and PIP2. These data were used for Pérez-Sancho, Jessica and Smokvarska, Marija and Glavier, Marie and Sritharan, Sujith and Dubois, Gwennogan and Dietrich, Victor and Platre, Matthieu and Li, Ziqiang Patrick and Paterlini, Andrea and Moreau, Hortense and Fouillen, Laetitia and Grison, Magali S. and Cana-Quijada, Pepe and Moraes, Tatiana Sousa and Immel, Françoise and Wattelet, Valerie and Ducros, Mathieu and Brocard, Lysiane and Chambaud, Clément and Zabrady, Matej and Luo, Yongming and Busch, Wolfgang and Tilsner, Jens and Helariutta, Yrjö and Russinova, Jenny and Taly, Antoine and Jaillais, Yvon and Bayer, Emmanuelle, Plasmodesmata Act as Unconventional Membrane Contact Sites Regulating Inter-Cellular Molecular Exchange in Plants. </p>
PhasAGE Expert Seminar- Inhibition of age-associated genomic instability: emerging strategy to delay cellular senescence and aging
<p>The PhasAGE <strong>Expert Seminars </strong>consist of a series of talks with speakers from PhasAGE partner’s institutions to promote a successful transfer of knowledge about PhasAGE topics – biomolecular phase separation, aging and age-related diseases.</p>
Volumetric imaging of cellular dynamics with deep learning enhanced bioluminescence microscopy
<p>The low photon emission of known luciferases, currently limit their widespread use as contrast agents in live cell microscopy because they demand long exposure times that are prohibitive for imaging fast biological dynamics. To increase the versatility of bioluminescence microscopy as an alternative for fluorescence microscopy, we present an improved low-light microscope in combination with deep learning methods to image extremely photon-starved samples enabling subsecond exposures for timelapse and volumetric imaging. Here, we leverage a versatile training data set for deep learning based bioluminescence microscopy including paired images of noisy and ground thruth fluorescence data of body wall muscle labeled <em>Caenorhabditis elegans</em> animals. These data include light-field images and their ground truth reconstructions for training a CNN for fast light fiel deconvolution.</p>
Received Signal Srength (RSS) urban measurements from GSM and UMTS networks for cellular-based positioning
<p>GSM (2G) and UMTS (3G) urban measurement data for cellular-based positioning. The data has been collected in Tampere city, Finland with a mobile phone with proprietary software. The data is given in Matlab *mat format and it contains a cell variable called BS_grid* which shows the GPS coordinates (x,y,z) (converted in local coordinates, in meter values) and the collected RSS value (in dB) per transmitter (i.e., BS or Node B). Each cell contains a N x 4 matrix, whose rows are [x y z RSS]. N is the number of measurements points in which the corresponding Base Station were heard. The size of the BS_grid* cells is equal to the number of heard Base Stations in the measured area. </p> <p>Example of research results based on these measurements can be found for example in:</p> <ul> <li>H. Nurminen, J. Talvitie, S. Ali-Loytty, P. Muller, E.S. Lohan, R. Piche, M. Renfors, "Statistical path loss parameter estimation and positioning using RSS measurements", Journal of Global Positioning Systems, vol. 12(1), 2013, ISSN 1446-3156.</li> </ul>
Cryo-4D-STEM datasets on cells and cellular organelles for demonstrating a dose-Efficient cryo-EM technique: tilt-Corrected Scanning Transmission Electron Microscopy
<p>This upload contains three 4D-STEM datasets in .raw format for demonstrating a dose-efficient cryo-EM technique for thick samples: tilt-corrected Scanning Transmission Electron Microscopy (tcBF-STEM). The dataset dimension is 128130256*256. Data were acquired on vitrified intact E.coli cells and isolated human cell organelles. This upload also contains the EFTEM images in .mrc acqired in the same ROI as the 4D-STEM dataset. </p> <p>It also contains analysis of the manuscript's Fig 3 and Ext. data fig 8. </p>
RAN and NWDAF combined data from Cellular Network
<p><span>Dataset containing various RAN and UEs metrics collected from 4 BSs deployed at Piazza D'Uomo, Catania. Metrics can be used for machine learning-based studies for physical resource block (PRBs) allocation in the context of O-RAN to maximize various KPMs, such as throughput or delay.</span></p>
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.