Find research datasets worth reusing

opencc-by-4.0Jun 2020View details →

MAOn: A Data-driven Methodology to Generate Living Ontologies

This repository presents the MAnto Lite ontology created with our MAOn methodology in the context of transport and public and the accessibility it provides. Besides, a set of annotated data with the ontology as a validation method is presented. The MAOn methodology is characterized by being data-based, by creating live ontologies and by a thorough evaluation process of the created ontology.

opencc-by-4.0Feb 2015View details →

Figure 4 in Two new free-living nematode species of Setosabatieria (Comesomatidea) from the East China Sea and the Chukchi Sea

Figure 4. Setosabatieria major sp. nov. (A) lateral view of male head end, showing cervical setae; (B) lateral view of female head end, showing female amphidial fovea; (C) lateral view of female vulva region, showing vulva and eggs; (D) lateral view of male tail region. Scale bars: A = 25 µm; B = 10 µm; C, D = 50 µm.

opencc-by-4.0Oct 2020View details →

ROB 2 assessments_Convalescent plasma or hyperimmune immunoglobulin for people with COVID-19_a living systematic review

Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review: Convalescent plasma or hyperimmune immunoglobulin for people with COVID-19:a living systematic review (Version 3).

opencc-by-4.0Sep 2020View details →

Figs 106–111. Chibchea Huber, 2000, live specimens. 106–107. C in On Venezuelan pholcid spiders (Araneae, Pholcidae)

Figs 106–111. Chibchea Huber, 2000, live specimens. 106–107. C. thunbergae Huber sp. nov.; male and female from Lara, between Coro and Barquisimeto. 108–109. C. danielae Huber sp. nov.; male and female from Mérida, Mesa Bolívar. 110–111. C. tunebo Huber, 2000; male and female from Táchira, La Trampa.

opencc-by-4.0Oct 2020View details →

Marine amphipods as a new live prey for ornamental aquaculture: exploring the potential of Parhyale hawaiensis and Elasmopus pectenicrus

Supplementary data from the scientific paper contribution " Marine amphipods as a new live prey for ornamental aquaculture: exploring the potential of Parhyale hawaiensis and Elasmopus pectenicrus".   Marine amphipods are gaining attention in aquaculture as a natural live food alternative to traditional preys such as Artemia, as they are rich in essential nutrients such as the lipids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are an important natural diet of many marine fish of commercial interest, and are relatively easy to culture in large numbers. However, there are no established culture techniques and a deeper knowledge on the reproductive biology, nutritional profiles and culture methodologies is still needed to potentiate the optimization of mass production. The present study assessed for the first time the aquaculture potential of Parhyale hawaiensis and Elasmopus pectenicrus, two cosmopolitan marine gammarids (as per traditional schemes of classification) that naturally proliferate in the wild and in aquaculture facilities. For that purpose, aspects of the population and reproductive biology of wild populations were characterized and then a series of laboratory-scale experiments were conducted to determine the amphipod productivity, the time needed to reach sexual maturity by the newborns (generation time), cannibalism degree, the effects of sex ratio on fecundity and the effects of diet (shrimp diet, plant-based diet and commercial fish diet) on fecundity and the juvenile growth. P. hawaiensis, unlike E. pectenicrus, was easily kept and propagated in laboratory conditions, performing exceedingly better than E. pectenicrus. P. hawaiensis showed a higher total length (9.3 ± 1.3 mm), wet weight (14.4 ± 6.2 mg), dry weight (10.5 ± 4.4 mg), females/males sex ratio in the wild (2.24), fecundity (12.8 ± 5.7 embryos per female), and gross energy content (16.71 ± 0.67 kJ g-1) with respect to E. pectenicrus. Although the P. hawaiensis juvenile growth was slightly reduced (marginally significant) by the use of a plant-based diet compared to a commercial shrimp and fish diet, fecundity was not affected, supporting the possible use of inexpensive diets to mass produce amphipods as live or frozen food. Possible limitations identified were their quite long generation times (50.9 ± 5.8 days) and relatively low fecundity levels (12.8 ± 5.7 embryos per female). With an observed productivity rate of 0.36 ± 0.08 juveniles per amphipod couple per day, P. hawaiensis could become a specialty feed for species that cannot easily transition to a formulated diet such as seahorses and other highly-priced marine ornamental species. Future studies should assess the nutritional value and to explore optimized medium- and large-scale production as well as self-producing biofloc systems taking advantage of the great dietary plasticity and environmental tolerance of the species.

opencc-by-4.0Nov 2020View details →

VR-Together Pilot 3: Geometry of living room, bedroom. Materials, textures and illumination

VR-Together Pilot 3 geometry of living room and bedroom, materials, textures and illumination. This dataset contains de material created for the <a href="https://vrtogether.eu/about-vr-together/pilots/pilot3/">Pilot 3 of the VR-Together project</a>. In particular, it contains the following material: <ul> <li>Geometry of the living room</li> <li>Geometry of the bedroom</li> <li>Objects</li> <li>Textures</li> <li>Illumination scheme</li> </ul> All the previous material has been created in Unity. VR-Together has been funded by the European Commission as part of the H2020 program, under the grant agreement 762111.

Seasonal Effects in Gastrointestinal Parasite Prevalence, Richness and Intensity in Vervet Monkeys Living in a Semi-Arid Environment

Data and R Notebook for Seasonal Effects in Gastrointestinal Parasite Prevalence, Richness and Intensity in Vervet Monkeys Living in a Semi-Arid Environment

Fig. 2 in Two new species of the aphid genus Uroleucon (Hemiptera: Aphididae) living on Grindelia in the USA

Fig. 2. Uroleucon (Lambersius) robinsoni sp. nov., holotype, apterous viviparous female (NHMUK 010121495). a. ANT III. b. Secondary rhinaria on ANT III. c. ANT VI. d. Ultimate rostral (III–V) segments. e. Siphunculus. f. Cauda.

Fig. 1. New species ofUroleucon Mordvilko, 1914 in Two new species of the aphid genus Uroleucon (Hemiptera: Aphididae) living on Grindelia in the USA

Fig. 1. New species ofUroleucon Mordvilko, 1914feeding on Grindelia Willd. a. Uroleucon (Lambersius) robinsoni sp. nov., holotype, apterous viviparous female (NHMUK 010121495) b. U. (L.) grindeliae sp. nov., holotype, apterous viviparous female (NHMUK 010121473) c. U. (L.) grindeliae sp. nov., paratype, alate viviparous female (NHMUK 010121477).

Fig. 3 in Two new species of the aphid genus Uroleucon (Hemiptera: Aphididae) living on Grindelia in the USA

Fig. 3. Uroleucon (Lambersius) grindeliae sp. nov., paratype, apterous viviparous female (NHMUK 010121474). a. ANT III. b. Secondary rhinaria on ANT III. c. ANT VI. d. Ultimate rostral (III–V) segments. e. Siphunculus. f. Cauda.

opencc-zeroJan 2021View details →

dryad40/100

Effects of plant hydraulic traits on the flammability of live fine canopy fuels in 62 Australian plant species

<ol> <li>Plant species vary in how they regulate moisture and this has implications for their flammability during wildfires. We explored how fuel moisture is shaped by variation within six hydraulic traits: saturated moisture content, cell wall rigidity, cell solute potential, symplastic water fraction and tissue capacitance.</li> <li>Using pressure-volume curves, we measured these hydraulic traits distal shoots (i.e. twigs + leaves) in 62 plant species across four wooded communities in south-eastern Australia. For a subset of 30 of those species, we also measured hydraulic traits of twigs using moisture-release curves. Moisture content of fine fuels was then estimated for circumstances typical of fire weather. These projections were made assuming that under the hot, dry, windy conditions typical of large wildfires, leaves and fine twigs would function at internal water pressures close to wilting point (i.e. turgor loss point, TLP). The effect of different moisture contents at TLP on ignition time was then modelled using a fully mechanistic, finite element model of biomass ignition based on standard principles of physical chemistry.</li> <li>We also measured predawn water potential, an indication of plant access to soil water that is influenced by root architecture. These data were used to model how root traits influence fuel moisture and ignition time.</li> </ol>

opencc-by-4.0Aug 2019View details →

Wrist-mounted IMU data towards the investigation of free-living human eating behavior - the Free-living Food Intake Cycle (FreeFIC) dataset

Introduction The Free-living Food Intake Cycle (FreeFIC) dataset was created by the <a href="http://mug.ee.auth.gr">Multimedia Understanding Group</a> towards the investigation of in-the-wild eating behavior. This is achieved by recording the subjects’ meals as a small part part of their everyday life, unscripted, activities. The FreeFIC dataset contains the $3D$ acceleration and orientation velocity signals ($6$ DoF) from $22$ in-the-wild sessions provided by $12$ unique subjects. All sessions were recorded using a commercial smartwatch ($6$ using the Huawei Watch 2™ and the MobVoi TicWatch™ for the rest) while the participants performed their everyday activities. In addition, FreeFIC also contains the start and end moments of each meal session as reported by the participants. Description FreeFIC includes $22$ in-the-wild sessions that belong to $12$ unique subjects. Participants were instructed to wear the smartwatch to the hand of their preference well ahead before any meal and continue to wear it throughout the day until the battery is depleted. In addition, we followed a self-report labeling model, meaning that the ground truth is provided from the participant by documenting the start and end moments of their meals to the best of their abilities as well as the hand they wear the smartwatch on. The total duration of the $22$ recordings sums up to $112.71$ hours, with a mean duration of $5.12$ hours. Additional data statistics can be obtained by executing the provided python script stats_dataset.py. Furthermore, the accompanying python script viz_dataset.py will visualize the IMU signals and ground truth intervals for each of the recordings. Information on how to execute the Python scripts can be found below. <pre><code># The script(s) and the pickle file must be located in the same directory. # Tested with Python 3.6.4 # Requirements: Numpy, Pickle and Matplotlib # Calculate and echo dataset statistics $ python stats_dataset.py # Visualize signals and ground truth $ python viz_dataset.py</code></pre> FreeFIC is also tightly related to Food Intake Cycle (FIC), a dataset we created in order to investigate the in-meal eating behavior. More information about FIC can be found <a href="https://zenodo.org/record/4421861">here</a> and <a href="https://mug.ee.auth.gr/intake-cycle-detection/">here</a>. Publications If you plan to use the FreeFIC dataset or any of the resources found in this page, please cite our work: <pre><code>@article{kyritsis2020data, title={A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, journal={IEEE Journal of Biomedical and Health Informatics}, year={2020}, publisher={IEEE}}</code></pre> <pre><code>@inproceedings{kyritsis2017automated, title={Detecting Meals In the Wild Using the Inertial Data of a Typical Smartwatch}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, booktitle={2019 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, year={2019}, organization={IEEE}} </code></pre> Technical details We provide the FreeFIC dataset as a <a href="https://docs.python.org/3/library/pickle.html">pickle</a>. The file can be loaded using Python in the following way: <pre><code class="language-python">import pickle as pkl import numpy as np with open('./FreeFIC_FreeFIC-heldout.pkl','rb') as fh: dataset = pkl.load(fh)</code></pre> The dataset variable in the snipet above is a dictionary with $5$ keys. Namely: <ul> <li>'subject_id'</li> <li>'session_id'</li> <li>'signals_raw'</li> <li>'signals_proc'</li> <li>'meal_gt'</li> </ul> The contents under a specific key can be obtained by: <pre><code class="language-python">sub = dataset['subject_id'] # for the subject id ses = dataset['session_id'] # for the session id raw = dataset['signals_raw'] # for the raw IMU signals proc = dataset['signals_proc'] # for the processed IMU signals gt = dataset['meal_gt'] # for the meal ground truth </code></pre> The sub, ses, raw, proc and gt variables in the snipet above are lists with a length equal to $22$. Elements across all lists are aligned; e.g., the $3$rd element of the list under the 'session_id' key corresponds to the $3$rd element of the list under the 'signals_proc' key. sub: list Each element of the sub list is a scalar (integer) that corresponds to the unique identifier of the subject that can take the following values: $[1, 2, 3, 4, 13, 14, 15, 16, 17, 18, 19, 20]$. It should be emphasized that the subjects with ids $15, 16, 17, 18, 19$ and $20$ belong to the held-out part of the FreeFIC dataset (more information can be found in the publication titled "A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches" by Kyritsis et al). Moreover, the subject identifier in FreeFIC is in-line with the subject identifier in the FIC dataset (more info <a href="https://zenodo.org/record/4421861">here</a> and <a href="https://mug.ee.auth.gr/intake-cycle-detection/">here</a>); i.e., FIC’s subject with id equal to $2$  is the same person as FreeFIC’s subject with id equal to $2$. ses: list Each element of this list is a scalar (integer) that corresponds to the unique identifier of the session that can range between $1$ and $5$. It should be noted that not all subjects have the same number of sessions. raw: list Each element of this list is dictionary with the 'acc' and 'gyr' keys. The data under the 'acc' key is a $N_{acc} \times 4$ numpy.ndarray that contains the timestamps in seconds (first column) and the $3D$ raw accelerometer measurements in $g$ (second, third and forth columns - representing the $x, y $ and $z$ axis, respectively). The data under the 'gyr' key is a $N_{gyr} \times 4$ numpy.ndarray that contains the timestamps in seconds (first column) and the $3D$ raw gyroscope measurements in ${degrees}/{second}$(second, third and forth columns - representing the $x, y $ and $z$ axis, respectively). All sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FIC dataset (more info <a href="https://zenodo.org/record/4421861">here</a> and <a href="https://mug.ee.auth.gr/intake-cycle-detection/">here</a>). Finally, the length of the raw accelerometer and gyroscope numpy.ndarrays is different $(N_{acc} \neq N_{gyr})$. This behavior is predictable and is caused by the Android platform. proc: list Each element of this list is an $M\times7$  numpy.ndarray that contains the timestamps, $3D$ accelerometer and gyroscope measurements for each meal. Specifically, the first column contains the timestamps in seconds, the second, third and forth columns contain the $x,y$ and $z$ accelerometer values in $g$ and the fifth, sixth and seventh columns contain the $x,y$ and $z$ gyroscope values in ${degrees}/{second}$. Unlike elements in the raw list, processed measurements (in the proc list) have a constant sampling rate of $100$ Hz and the accelerometer/gyroscope measurements are aligned with each other. In addition, all sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FIC dataset (more info <a href="https://zenodo.org/record/4421861">here</a> and <a href="https://mug.ee.auth.gr/intake-cycle-detection/">here</a>). No other preprocessing is performed on the data; e.g., the acceleration component due to the Earth's gravitational field is present at the processed acceleration measurements. The potential researcher can consult the article "A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches" by Kyritsis et al. on how to further preprocess the IMU signals (i.e., smooth and remove the gravitational component). meal_gt: list Each element of this list is a $K\times2$ matrix. Each row represents the meal intervals for the specific in-the-wild session. The first column contains the timestamps of the meal start moments whereas the second one the timestamps of the meal end moments. All timestamps are in seconds. The number of meals $K$ varies across recordings (e.g., a recording exist where a participant consumed two meals). Ethics and funding Informed consent, including permission for third-party access to anonymised data, was obtained from all subjects prior to their engagement in the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 727688 - <a href="https://bigoprogram.eu/">BigO: Big data against childhood obesity</a>. Contact Any inquiries regarding the FreeFIC dataset should be addressed to: Dr. Konstantinos KYRITSIS Multimedia Understanding Group (MUG) Department of Electrical & Computer Engineering Aristotle University of Thessaloniki University Campus, Building C, 3rd floor Thessaloniki, Greece, GR54124 Tel: +30 2310 996359, 996365  Fax: +30 2310 996398 E-mail: kokirits [at] mug [dot] ee [dot] auth [dot] gr

Fig. 5 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator

Fig. 5 Lateral view of Electrophorus electricus. National Museum of Natural History, NMNH 225670, 520 mm TL. Corantijn River, Suriname

Fig. 4 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator

Fig. 4 Ecological Niche Model and electric organ discharges for species of Electrophorus. Species niche models generated by MaxEnt for Greater Amazonia: a Electrophorus electricus (red); b E. varii (yellow); and c E. voltai (blue). d Measurements of voltage of high-voltage EODs, low-voltage EODs waveforms from Sach's organ, and posterior one-third of Hunter's organ (grey lines = individually recorded fish, black lines = averaged waveform for each species). e Nearest-neighbor hierarchical clustering of prominent time-frequency features of the low-voltage Sach's organ EOD from seven individuals of Electrophorus

Fig. 3 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator

Fig. 3 Electrophorus tree of life and time of species diversification. Time-calibrated genealogy of Electrophorus based on a maximum clade credibility (MCC) species tree derived from *BEAST2.4 analyses of 10 genes (colored lines) and 94 specimens of Electrophorus (relaxed molecular clock and uncorrelated lognormal model implemented). Purple bars represent 95% highest posterior density distributions for the estimated divergence time of each major node. Voltage measurements made by us are reported below E. electricus (National Museum of Natural History, NMNH 225670, 520 mm TL, Corantijn River, Suriname), E. voltai (Museu Paraense Emílio Goeldi, MPEG 15529; holotype, 1290 mm TL), and E. varii (MPEG 25422; holotype, 1000 mm TL)

Fig. 1 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator

Fig. 1 Sampling localities and gene trees for the three species of Electrophorus. a Map of northern South America showing distributions of sampled records and type localities (indicated by numbers) for three electric eel species: Electrophorus electricus (red dots, 1 = Suriname River, Suriname); E. voltai (blue dots, 2 = Rio Ipitinga, Brazil); and E. varii (yellow dots, 3 = Rio Goiapi, Brazil). Bicolor dots (blue/yellow) indicate sympatric co-occurrence of E. voltai and E. varii. The map was created in ArcGIS (https://www.arcgis.com) with images available at Shuttle Radar Topography Mission, Global Multi-resolution Terrain Elevation Data, and HydroSHEDS database. b *BEAST2.4 species tree (top cladogram; 94 specimens: 15 E. electricus, 41 E. voltai, 38 E. varii) based on 5 mitochondrial (trees 1–5; 107 specimens: 19 E. electricus, 43 E. voltai, 45 E. varii) and 5 nuclear genes (6–10; 94 specimens). Higher shading densities represent areas where the majority of trees agree in topology and branch lengths (posterior probabilities>0.99), while lower densities represent areas of uncertainty (Supplementary Data 1)

Fig. 2 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator

Fig. 2 Key morphological features to recognize the three species of Electrophorus. Top, radiographs of lateral view of the anterior portion of body (skull and pectoral girdle highlighted red). The cleithrum lies between the fifth and sixth vertebrae (v) in Electrophorus electricus (a) and E. voltai (b) versus first and second vertebrae in E. varii (c). Bottom, illustrations of ventral view of the head, showing key features listed in Diagnoses. a top: National Museum of Natural History, NMNH 403765, 300 mm TL, Cuyuni River, Guyana; bottom: NMNH 225576, 1000 mm TL, Corantijn River, Suriname. b top: Instituto Nacional de Pesquisas de Amazônia, INPA 39009, 450 mm TL, Teles Pires River, Brazil; bottom: Academy of Natural Sciences of Drexel University, ANSP 197583 (t3539), 1280 mm TL, Xingu River, Brazil. c top: NMNH 306677, 450 mm TL, Lago Janauari, Amazon River, Brazil; bottom: NMNH 196634, 1220 mm TL, Amazon River, Brazil

Fig. 7 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator

Fig. 7 Lateral view of Electrophorus voltai sp. nov. Holotype, Museu Paraense Emílio Goeldi MPEG 15529, 1290 mm TL. Ipitinga River, Brazil

Fig. 6 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator

Fig. 6 Lateral view of Electrophorus varii sp. nov. Holotype, Museu Paraense Emílio Goeldi MPEG 25422, 1000 mm TL. Goiapi River, Brazil

Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes - Database of Physical gene-gene Interactions in young adult C.elegans.

This repository contains Supplementary Information for manuscript Suriyalaksh et al Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive  of novel ageing genes corresponding to the curation of physical gene-gene interactions for young adult C elegans worms  We manually curated 239,001 regulatory interactions from 289 young adult wild-type (WT) C.elegans datasets, consisting of 126 genes and 495 unique transcription factors (see TableS1_datasets_for_prior.csv for references).  This repository contains 3 different files: TableS1_datasets_for_prior.csv - contains datasets used as sources for physical gene-gene or TF-gene interactions TableS2_physical_priors.xlsx - contains three tabs: ChIPATAC - contains physical TF-gene interactions from 115 L4 or young-adult ChIP-seq datasets from modERN (Kudron et al., 2018) +  ChIP-seq datasets (GSE28350, GSE81521) from  (Hochbaum et. al, 2011, Li et. al, 2016). eY1HATAC- contains  3,501 TF-gene interactions from eY1H assay by Fuxman Bass et al. (2016). motifATAC - contains 202 unique TF DNA recognition motifs using “direct evidence” option from CiS-BP motif database (Weirauch et al., 2014), obtained through RTFBSDB R package (Wang et al., 2016) - see TableS1 TableS3_WT_functional_priors.csv - contains functional knockdown data that we use as gold standard to validate inferred networks in Suriyalaksh et al. (see TableS1_datasets_for_prior.csv for sources) --- Description of methodology to obtain regulatory interactions in TableS2: Regulatory sequences for each gene were acquired from ENSEMBL (Aken et al., 2017), obtained using biomaRt R package (accessed on 31st Oct 2017). This study used WBcel235/ce11 version of the C. elegans genome, and WormBase WS260 genome annotations. For motifs, TFs whose motifs overlapped with an open ATAC-seq region by at least one base pair were kept. For ChIP-seq, TF binding sites that overlapped with an open ATAC-seq region by at least one base pair were kept using bedtools intersect and bedtools merge commands. An interaction from a TF to a gene was inferred by aligning transcription start sites (TSS) using bedtools window commands with 1000 bp window size to the TF-binding locations from ChIP-seq and motifs. For eY1H data, an interaction is included if the TSS site of the target gene overlaps with an open ATAC-seq region by at least one base pair. For gene-gene interactions, of the 298 studies compiled in WormExp v1.0 database (Yang et al, 2016, updated 27/07/16), 98 studies were included in the database spanning 126 different genes (see Table S1 in this repository).