Find research datasets worth reusing

opencc-by-4.0Nov 2021View details →

Supplementary material

the mRNAs expression differences in the BPD group and the exosome intervention group by RNA-sequence technology, and found that a total of 88 mRNAs showed significant differences, including 24 up-regulated and 64 down-regulated

IPDPS'22 Rebuttal Supplement Materials for DINTE

We have included DRL-TE(INFOCOM'19) in the experiment and compared more recent work with DINTE.

IPDPS'22 Rebuttal Supplement Materials for DINTE

We have included DRL-TE(INFOCOM'19) in the experiment and compared more recent work with DINTE.

opennotspecifiedMar 2021View details →

FIGURE.4 in Nomenclatural notes on type materials of eight names in Embelia (Primulaceae) from Peninsular India

FIGURE.4. Lectotype of Embelia villosa Wall. (K, K001115624) © copyright of the Board of Trustees of the Royal Botanic Gardens, Kew.

opennotspecifiedMar 2021View details →

FIGURE. 1 in Nomenclatural notes on type materials of eight names in Embelia (Primulaceae) from Peninsular India

FIGURE. 1. Lectotype of Embelia adnata Bedd. ex Clarke (K, K000756499) © copyright of the Board of Trustees of the Royal Botanic Gardens, Kew.

opennotspecifiedMar 2021View details →

FIGURE.5 in Nomenclatural notes on type materials of eight names in Embelia (Primulaceae) from Peninsular India

FIGURE.5. Lectotype of Embelia viridiflora Scheff. (L, L.2635885) © Naturalis Biodiversity Center, Leiden

Figure 5 in A new species of Paratrigona Schwarz, 1938 from northeastern Brazil, with notes on the type material of Melipona lineata Lepeletier, 1836 (Hymenoptera: Anthophila: Apidae)

Figure 5. Specimen of Paratrigona lineata (Lepeletier, 1836) from Januária (Minas Gerais State, Brazil), deposited at the BIOSIS, Institute of Biology, Federal University of Bahia, Brazil: (a). Frontal view of head; (b). Lateral view of body; (c). Dorsolateral view of mesosoma; (d). Labels. Arrows indicates presence of setae.

Figure 6 in A new species of Paratrigona Schwarz, 1938 from northeastern Brazil, with notes on the type material of Melipona lineata Lepeletier, 1836 (Hymenoptera: Anthophila: Apidae)

Figure 6. Paratype of Paratrigona lineata glabella Camargo & Moure, 1994, deposited at the Prof. J.M.F. Camargo Collection from the RPSP, University of São Paulo, Ribeirão Preto, Brazil: (a). Frontal view of head; (b). Lateral view of body; (c). Lateral view of mesepisternum; (d). Dorsal view of mesoscutellum; (e). Dorsal view of mesosoma; (f). Labels. Arrows indicate absence or presence of setae. Ownership rights to these images and their copyright belong to RPSP (Coleção Entomológica 'Prof. J.M.F. Camargo'). Photo by E.A.B. Almeida.

Figure 7 in A new species of Paratrigona Schwarz, 1938 from northeastern Brazil, with notes on the type material of Melipona lineata Lepeletier, 1836 (Hymenoptera: Anthophila: Apidae)

Figure 7. Holotype of Paratrigona incerta Camargo & Moure, 1994, deposited at the Prof. J.M.F. Camargo Collection from the RPSP, University of São Paulo, Ribeirão Preto, Brazil: (a). Frontal view of head; (b). Lateral view of body; (c). Lateral view of mesepisternum; (d). Dorsal view of mesosoma; (e). Labels. Arrows indicate presence or absence of setae. Ownership rights to these images and their copyright belong to RPSP (Coleção Entomológica 'Prof. J.M.F. Camargo'). Photo by E.A.B. Almeida.

Figure 3 in A new species of Paratrigona Schwarz, 1938 from northeastern Brazil, with notes on the type material of Melipona lineata Lepeletier, 1836 (Hymenoptera: Anthophila: Apidae)

Figure 3. Lectotype of Melipona lineata Lepeletier, 1836 deposited at the MNHP, Paris, France: (a). Frontal view of head; (b). Lateral view of body; (c, d). Dorsolateral view of mesoscutum and mesoscutellum; (e). Metatibia; (f). Labels of lectotype. Arrows indicate presence of setae.

Figure 4 in A new species of Paratrigona Schwarz, 1938 from northeastern Brazil, with notes on the type material of Melipona lineata Lepeletier, 1836 (Hymenoptera: Anthophila: Apidae)

Figure 4. Paralectotype of Melipona lineata Lepeletier, 1836 deposited at the MNHP, Paris, France: (a). Frontal view of head; (b). Lateral view of head; (c). Lateral view of body; (d). Dorsolateral view of mesosoma; (e). Metatibia; (f). Labels of paralectotype. Arrows indicate absence of setae.

Figure 1 in A new species of Paratrigona Schwarz, 1938 from northeastern Brazil, with notes on the type material of Melipona lineata Lepeletier, 1836 (Hymenoptera: Anthophila: Apidae)

Figure 1. Holotype female (worker) of Paratrigona intermedia Oliveira, Madella-Auricchio & Freitas sp. nov., deposited at the MHNBA, Salvador, Bahia State, Brazil: (a). Frontal view of head; (b). Lateral view of body; (c). Mesepisternum in profile, and iridescence on wing; (d). Dorsolateral view of mesoscutum and mesoscutellum. Arrows indicate presence or absence of setae.

Figure 2 in A new species of Paratrigona Schwarz, 1938 from northeastern Brazil, with notes on the type material of Melipona lineata Lepeletier, 1836 (Hymenoptera: Anthophila: Apidae)

Figure 2. Holotype female of Paratrigona intermedia Oliveira, Madella-Auricchio & Freitas sp. nov., deposited at the MHNBA, Salvador, Bahia State, Brazil: (a). Mesepisternum in profile; (b). Metatibia; (c). Labels. Arrow indicates whitish setae/pilosity.

opencc-zeroDec 2021View details →

Supplementary material 1 from: Duran M (2021) An annotated checklist of the amphibians and reptiles of North Padre Island, Texas, USA, with comparisons to adjacent barrier island and mainland herpetofauna. ZooKeys 1073: 119-175. https://doi.org/10.3897/zookeys.1073.57241

Museum and iNaturalist records for Aransas, Cameron, Kenedy, Kleberg, Nueces, San Patricio, and Willacy counties, Texas, and iNaturalist records for North and South Padre and Mustang islands, Texas

dryad32/100

An efficient method for higher heating value estimation of municipal solid wastes electronic supplementary material

To facilitate the disposal of municipal solid wastes (MSWs) via thermo-chemical approaches, the accurate measurement of MSWs' higher heating value (HHV) plays a key role. This study aims to forecast the HHV of MSWs using the optimized multi-variate grey model (OBGM (1, N)) due to its high accuracy under the condition of scanty data. Results show that POBGM (1, 5) with proximate analysis data is the most accurate model with the least error of 5.41% MAPE (mean absolute percentage error). This model also illustrates that ash is the most important factor affecting HHV due to its significant fraction in MSWs, followed by volatiles, fixed carbon and water contents, respectively. With prediction interval (PI) method, most of the actual data can be included by using the 95% confidence intervals.

opencc-zeroDec 2021View details →

opencc-zeroDec 2021View details →

Supplementary material 1 from: Li Y-D, Huang D-Y, Cai C (2021) New species of Omma Newman from mid-Cretaceous Burmese amber (Coleoptera, Archostemata, Ommatidae). Deutsche Entomologische Zeitschrift 68(2): 341-348. https://doi.org/10.3897/dez.68.74174

Habitus of Omma forte and O. manukyani, with the relative positions of confocal images in Figs 4 and 6 labeled

Suplemental data for material radiopurity control in the XENONnT experiment

The selection of low-radioactive construction materials is of the utmost importance for rare-event searches and thus critical to the XENONnT experiment. Results of an extensive radioassay program are reported, in which material samples have been screened with gamma-ray spectroscopy and mass spectrometry. Several live years of data, aggregated across all instruments, were acquired throughout the radioassay program of XENONnT. Relevant measurements for detector construction are covered in the manuscript 'Material radiopurity control in the XENONnT experiment' (arxiv:2112.05629v1) . Supplemental data is available in here. NOTE: Current screening results are only preliminary, and contain a small set of the overall screened dataset.

openmit-licenseAug 2017View details →

Materials in Vessels Dataset, Annotated images of materials in transparent vessels for semantic segmentation

Data set of materials in vessels The handling of materials in glassware vessels is the main task in chemistry laboratory research as well as a large number of other activities. Visual recognition of the physical phase of the materials is essential for many methods ranging from a simple task such as fill-level evaluation to the identification of more complex properties such as solvation, precipitation, crystallization and phase separation. To help train neural nets for this task, a new data set was created. The data set contains a thousand images of materials, in different phases and involved in different chemical processes, in a laboratory setting. Each pixel in each image is labeled according to several layers of classification, as given below: a. Vessel/Background: For each pixel assign value of one if it is part of the vessel and zero otherwise. This annotation was used as the ROI map for the valve filter method. b. Filled/Empty: This is similar to the above, but also distinguishes between the filled and empty regions of the vessel. For each pixel, one of the following three values is assigned:0 (background); 1 (empty vessel); or 2 (filled vessel). c. Phase type: This is similar to the above but distinguishes between liquid and solid regions of the filled vessel. For each pixel, one of the following four values: 0 (background); 1 (empty vessel); 2 (liquid); or 3 (solid). d. Fine-grained physical phase type: This is similar to the above but distinguishes between specific classes of physical phase. For each pixel, one of 15 values is assigned: 1 (background); 2 (empty vessel); 3 (liquid); 4 (liquid phase two, in the case where more than one phase of the liquid appears in the vessel); 5 (suspension); 6 (emulsion); 7 (foam); 8 (solid); 9 (gel); 10 (powder); 11 (granular); 12 (bulk); 13 (solid-liquid mixture); 14 (solid phase two, in the case where more than one phase of solid exists in the vessel): and 15 (vapor). The annotations are given as images of the size of the original image, where the pixel value is the class number. The annotation of the vessel region (a) is used in the ROI input for the valve filter net . 4.1. Validation/testing set The data set is divided into training and testing sets. The testing set is itself divided into two subsets; one contains images extracted from the same YouTube channels as the training set, and therefore was taken under similar conditions as the training images. The second subset contains images extracted from YouTube channels not included in the training set, and hence contains images taken under different conditions from those used to train the net. 4.2. Creating the data set The creation of a large number of images with a variety of chemical processes and settings could have been a daunting task. Luckily, several YouTube channels dedicated to chemical experiments exist which offer high-quality footage of chemistry experiments. Thanks to these channels, including NurdRage, NileRed, ChemPlayer, it was possible to collect a large number of high-quality images in a short time. Pixel-wise annotation of these images was another challenging task, and was performed by Alexandra Emanuel and Mor Bismuth. For more details see:  <a href="https://arxiv.org/pdf/1708.08711.pdf">Setting attention region for convolutional neural  networks using region selective features, for  recognition of materials within glass vessels</a> This dataset was first published in 2017.8 For newer and Bigger datasets see https://zenodo.org/record/4736111#.YbG-RrtyZH4 https://zenodo.org/record/3697452#.YbG-TLtyZH4

Materials for "Shifts between cooperation and antagonism driven by individual variation: A systematic synthesis review"

This contains a permanent record of dataset and analysis code for the study: Moran, N.P., Caspers, B.A., Chakarov, N., Ernst, U.R., Fricke, C., Kurtz, J., Lilie, N.D., Lo, L.K., Müller, C., Takola, E. and Trimmer, P.C., 2021. Shifts between cooperation and antagonism driven by individual variation: A systematic synthesis review. Oikos. Full data analysis records are available on: https://osf.io/9kfpc/ This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 836937 (NPM). The Centre for Ocean Life is a VKR center of excellence supported by the Villum foundation. This research was funded by the German Research Foundation (DFG) as part of the SFB TRR 212 (NC3; project no. – 316099922, 396777092, 396777467, 396777869, 396779914, 396780003, 396780988, 396782445, 396782288, 396782608).