Find research datasets worth reusing

Figure 8 in Systematics, host plants, and life histories of three new Phyllocnistis species from the central highlands of Costa Rica (Lepidoptera, Gracillariidae, Phyllocnistinae)

Figure 8. Phyllocnistis maxberryi sp. n., pupa. A Ventral view of head B ventral view of cocoon-cutter C frons D lateral view of head E lateral view of cocoon-cutter F dorsal of sixth abdominal tergum G spines on sixth abdominal tergum H lateral view of spines on seventh abdominal tergum I view of abdominal tip Į dorsal view of A9–10 K lateral seta on sixth abdominal tergum L ventral view of A9–10. Scale bars 100 µm.

Figure 6 in Systematics, host plants, and life histories of three new Phyllocnistis species from the central highlands of Costa Rica (Lepidoptera, Gracillariidae, Phyllocnistinae)

Figure 6. Phyllocnistis tropaeolicola sp. n., genitalia. A Male, ventral view B right valva, mesal view C aedeagus D female, lateral view E ventral view of terminal segments. (Scale bar 0.5 mm except for figure B, 0.25 mm.)

Figure 2 in Systematics, host plants, and life histories of three new Phyllocnistis species from the central highlands of Costa Rica (Lepidoptera, Gracillariidae, Phyllocnistinae)

Figure 2. Adults of three new Phyllocnistis species from Costa Rica. A Phyllocnistis drimiphaga sp. n., holotype female B P. maxberryi sp. n., holotype female (abdomen removed for dissection) C P. tropaeolicola sp. n., holotype male.

Figure 10 in Systematics, host plants, and life histories of three new Phyllocnistis species from the central highlands of Costa Rica (Lepidoptera, Gracillariidae, Phyllocnistinae)

Figure 10. Life history of Phyllocnistis drimiphaga sp. n. A Leaf mines on abaxial side of leaf surface, white square enclosing early mine, arrow pointing to pupal cocoon fold B close-up view of early mine, arrow pointing to egg shell remains C same as figure B, but showing frass pattern (photo taken with sunlight projecting through the leaf from behind) D nearly mature old mine on adaxial side E nearly mature old mine on abaxial side (photo taken from adaxial side) F opened mine showing mature sapfeeding larva in situ G opened young pupal cocoon fold showing cocoon-spinning larva in situ H pupal cocoon fold on adaxial mine I opened pupal cocoon fold showing pupa in situ (dorsal view) Į protruded and attached pupal shell (arrow) on pupal cocoon fold of an abaxial leaf mine K opened pupal cocoon fold on adaxial mine showing Ageniaspis cocoons in situ.

Figure 1 in Systematics, host plants, and life histories of three new Phyllocnistis species from the central highlands of Costa Rica (Lepidoptera, Gracillariidae, Phyllocnistinae)

Figure 1. Habitats and larval host plants of Phyllocnistis species. A Cerro de la Muerte, Villa Mills region, 3000 m and below, in Cordillera de Talamanca B Volcán Barva, ALAS transect, 2000 m, in Braulio Carillo National Park C habitat of P. drimiphaga in Cerro de la Muerte, km 70 Pan-American Hwy, road to El Paraíso del Quetzal, 2700 m, arrow pointing to host plant where mines were found D young stem shoots and leaves of Drimys granadensis of C, growing from base of the tree E flowers and leaves of D. granadensis F habitat of P. maxberryi in Cerro de la Muerte, km 95 Pan-American Hwy, trail front of La Georgina in Villa Mills, 3100 m, arrow pointing to host plant where mines were found G young growth of Gaiadendron punctatum in front, and mature trees with yellow fruits in behind, at ALAS transect in Vara Blanca, 2000 m H habitat of P. tropaeolicola in Cerro de la Muerte, on km 95 Pan-American Hwy, near La Gegina in Mills, 3100 m, arrow pointing to host plant where mines were found I Tropaeolum emarginatum, details of host plants that are shown in H.

Figure 12 in Systematics, host plants, and life histories of three new Phyllocnistis species from the central highlands of Costa Rica (Lepidoptera, Gracillariidae, Phyllocnistinae)

Figure 12. Life history of Phyllocnistis tropaeolicola sp. n. A Leaf mines on a young leaf, arrows pointing at young to middle instar larvae B mature leaf mine with pupal cocoon fold (arrow), white square enclosing early stage mine region C mature sap-feeding larva in pre-cocoon chamber D detailed view of figure C E opened mine showing nearly mature sap-feeding larva in situ F opened young pupal cocoon fold showing cocoon-spinning instar in situ G pupal cocoon fold, arrow pointing to the slender exit H opened pupal cocoon fold showing pupa in situ, dorsolateral view.

Figure 11 in Systematics, host plants, and life histories of three new Phyllocnistis species from the central highlands of Costa Rica (Lepidoptera, Gracillariidae, Phyllocnistinae)

Figure 11. Life history of Phyllocnistis maxberryi sp. n. A Leaf mines on young growing Gaiadendron shoot B mature mine with pupal cocoon fold (arrow) C nearly mature mine and mature sap-feeding larva (left arrow), and oviposition location (right arrow) D close-up view of mature sap-feeding larva E opened mine showing mature sap-feeding larva in situ F opened young pupal cocoon fold showing cocoonspinning larva in situ G pupal cocoon fold, arrow pointing at thinner pupal exit H opened pupal cocoon fold showing pupa in situ, dorsal view I pupa in situ, lateral view.

opencc-by-4.0May 2020View details →

Dataset: Whole blood count, used in: "AIDeveloper: deep learning image classification in life science and beyond"

Real-time deformability cytometry (RT-DC) data of whole blood measurements. Data was used to train and validate a neural net to perform a blood count based on brightfield images of RT-DC. 01_Model: Contains the final model as well as an AIDeveloper meta-file that allows to reproduce the training procedure. The metafile preciesely defines which dataset was used for training and which for validation as well as all parameters that were set in AIDeveloper. The following folders contain data that was used for training (and validation): <ul> <li>Cambr</li> <li>KIK</li> <li>20190306_DextranBlood_AI_DataSet</li> <li>Gs_Blood_Train</li> </ul> Testing data is stored on figshare: https://figshare.com/articles/Krater_et_al_2020_Data_zip/9902636

opencc-by-4.0Mar 2020View details →

FIGURE 1. Iphimedia perplexa Myers & Costello 1987 in Taxonomy as the key to life

FIGURE 1. Iphimedia perplexa Myers & Costello 1987 is a rare amphipod crustacean first described from Lough Hyne, a marine reserve in Ireland. All that is known about the species is its morphological description and some geographic distribution records.

opencc-by-4.0Jun 2020View details →

Beneath the SURFace: An MRI-like View into the Life of a 21st Century Datacenter

This is a trace archive of metrics collected from the Lisa cluster at SURFsara associated with the article that will be published in USENIX;login: in July 2020. Github repository which contains documentation as well as scripts required to replicate the work from the login paper: <a href="https://github.com/sara-nl/SURFace">https://github.com/sara-nl/SURFace</a> Real-world data can be instrumental in answering detailed questions: How do we know which assumptions regarding large-scale systems are realistic? How do we know that the systems we build are practical? How do we know which metrics are important to assess when analyzing performance? To answer such questions, we need to collect and share operational traces containing real-world, detailed data. Not only is the presence of low-level metrics significant, but they also help avoid biases through their variety. To address variety, there exist several types of archives, such as the Parallel Workloads Archive, the Grid Workloads Archive, and the Google or Microsoft logs (the Appendix gives a multi-decade overview). However, such traces mostly focus on higher-level scheduling decisions and high-level, job-based resource utilization (e.g., consumed CPU and memory). Thus, they do not provide vital information to system administrators or researchers analyzing the full-stack or the OS-level operation of datacenters.  The traces we are sharing have the finest granularity of all other open-source traces published so far. In addition to scheduler-level logs, they contain over 100 low-level, server-based metrics, going to the granularity of page-faults or bytes transferred through a NIC.   The SURF archive Datacenters already exhibit unprecedented scale and are becoming increasingly more complex. Moreover, such computer systems have begun having a significant impact on the environment, for example, training some machine learning models has sizable carbon footprints. As our recent work on modern datacenter networks shows, low-level data is key to understanding full-stack operation, including high-level application behavior. We advocate it is time to start using such data more systematically, unlocking its potential in helping us understand how to make (datacenter) systems more efficient. We advocate that our data can contribute to a more holistic approach, looking at how the multitude of these systems work together in a large-scale datacenter.    This archive contains data from the Dutch National Infrastructure, Lisa.   <a href="https://userinfo.surfsara.nl/systems/lisa/description">Description of the Lisa system</a>   <a href="https://userinfo.surfsara.nl/systems/cartesius/description">Description of the Cartesius system</a>   We gather metrics, at 15-second intervals, from several data sources: <ul> <li> Slurm: all job, task, and scheduler related data, such as running time, queueing time, failures, servers involved in the execution, organization in partitions, and scheduling policies. </li> <li> NVIDIA Management Library (NVML): per GPU, data such as power metrics, temperature, fan speed, or used memory. </li> <li> IPMI: per server, data such as power metrics and temperature. </li> <li> OS-level: from either procfs, sockstat, or netstat data: low-level OS metrics, regarding the state of each server, including CPU, disk, memory, network utilization, context switches, and interrupts.  </li> </ul>   We also release other kinds of novel information, related to datacenter topology and organization.   The audience we envision using these metrics is composed of systems researchers, infrastructure developers and designers, system administrators, and software developers for large-scale infrastructure. The frequency of collecting data is uniquely high for open-source data, which could allow these experts unprecedented views into the operation of a real datacenter. * Note: For the GPU metrics a number of nodes were introduced to the system in late Feb/start of March and as such these specific nodes have no data available in January and February which may cause irregularities. The github will contain code snippets that will show how to filter this data such that this is not a problem and how to graph the parquet data (this is pending update in the next few days.

opencc-by-4.0Aug 2020View details →

Remaining useful life estimation of bearings: Meta-analysis of Experimental Procedure

The file analysis.xlsx contains the data and statistics obtained from a survey that analyzed the machine learning procedures used for estimating the remaining useful life (RUL) of bearings. The goal is to evaluate the extent to which proper protocol is adhered to for RUL estimation in the domain of predictive maintenance. We surveyed 3 knowledge bases with keywords targeting this specific field, sampled the research and recorded the current practices. Below we present the details of the various spreadsheets where the collected data is registered and analyzed.

opencc-by-4.0Jul 2015View details →

Figure 3 in Early life history of the sheepnose (Plethobasus cyphyus) (Mollusca: Bivalvia: Unionoida)

Figure 3. Scanning electron micrographs of glochidia from Chippewa River mussel species similar in size to Plethobasus cyphyus glochidia. (A) Obliquaria reflexa; (B) Amblema plicata; (C) Elliptio dilatata; (D) Plethobasus cyphyus; (E) juvenile Plethobasus cyphyus (valve height = 217 µm) from a naturally infested Notropis volucellus; (F) Toxolasma parvus; (G) Fusconaia flava; (H) Pleurobema sintoxia. The scale bar is 100 µm.

opencc-by-4.0Jul 2015View details →

Figure 1 in Early life history of the sheepnose (Plethobasus cyphyus) (Mollusca: Bivalvia: Unionoida)

Figure 1. Plethobasus cyphyus brooding periods in 2009 and 2011. Numbers in parentheses indicate number of mussels examined. Sample dates prior to and after the brooding period are not shown.

opencc-by-4.0Sep 2015View details →

Figure 7 in Diversity and life-history traits of wild bees (Insecta: Hymenoptera) in intensive agricultural landscapes in the Rolling Pampa, Argentina

Figure 7. Mean number of (a) above-ground nesting bee individuals, (b) floral specialist bee individuals, (c) oligolectic bee individuals and (d) oil-collecting bee individuals in cropped area (n = 28 points) and semi-natural area (n = 11 points). ns indicates a non-significant result. Asterisks indicate that means are significantly different (Wilcoxon rank sum test, ** = P <0.01). Bars show SEs.

opencc-by-4.0Sep 2015View details →

Figure 2 in Diversity and life-history traits of wild bees (Insecta: Hymenoptera) in intensive agricultural landscapes in the Rolling Pampa, Argentina

Figure 2. Semi-natural area of the study site: (a) semi-natural grassland; (b) the stream 'Arroyo Dulce' and its banks (Photos: Violette Le Féon).

Figure 10. Spheniopsis brasiliensis. A in The organs of prey capture and digestion in the miniature predatory bivalve Spheniopsis brasiliensis (Anomalodesmata: Cuspidarioidea: Spheniopsidae) expose a novel life-history trait

Figure 10. Spheniopsis brasiliensis. A transverse section through the heart. AM, Amoebocyte; AU, auricle; PE, pericardium; PEG, pericardial gland; R, rectum; SM, suspensory membrane; V, ventricle.

Figure 3. Spheniopsis brasiliensis. A in The organs of prey capture and digestion in the miniature predatory bivalve Spheniopsis brasiliensis (Anomalodesmata: Cuspidarioidea: Spheniopsidae) expose a novel life-history trait

Figure 3. Spheniopsis brasiliensis. A ventral view of the septum, foot and mouth. BG, Byssal groove; F, foot; F(T), 'toe' of foot; M, mouth; SE, septum; SEM, margin of septal membrane; SEP(1),(2),(3),(4), septal pores.

Figure 1 in The organs of prey capture and digestion in the miniature predatory bivalve Spheniopsis brasiliensis (Anomalodesmata: Cuspidarioidea: Spheniopsidae) expose a novel life-history trait

Figure 1. Spheniopsis brasiliensis. SEM views of the siphonal apparatus. (A) Posterior view of the exhalant and inhalant siphons, with three and four siphonal papillae, respectively. (B) Higher magnification view of a single siphonal papilla with a terminal array of sensory cilia. CI, Cilia; ES, exhalant siphon; IS, Inhalant siphon; SP, sensory papilla; SPB, base of sensory papillae.

Figure 9. Spheniopsis brasiliensis. A in The organs of prey capture and digestion in the miniature predatory bivalve Spheniopsis brasiliensis (Anomalodesmata: Cuspidarioidea: Spheniopsidae) expose a novel life-history trait

Figure 9. Spheniopsis brasiliensis. A transverse section through the pedal ganglia and the statocysts. PEGA, Pedal ganglia; STAT, statocyst; STL, statolith.

Figure 5 in The organs of prey capture and digestion in the miniature predatory bivalve Spheniopsis brasiliensis (Anomalodesmata: Cuspidarioidea: Spheniopsidae) expose a novel life-history trait

Figure 5. Spheniopsis brasiliensis. Transverse sections through the (A) oesophagous; (B) crystalline style sac; (C) mid gut; (D) hind gut; and (E) rectum, all drawn to the same scale. CC, Collagen coat; CS, crystalline style.