cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5504944.v2
title: compress data directory for tcm_lib_search package

description: This compressed file contains the data directory for building the tcm_lib_search package from scratch. This data directory contains the SQLite database file with extracted formulas, variable transformation and logistic regression model for predicting herbal tokens. <br>After obtaining the source of tcm_lib_search package from https://github.com/wang-shuyu/tcm_lib_search/, download the tcm_lib_search-data-v1.0.tar.gz, and decompress them into the data directory. <br>cd tcm_lib_searchtar xfvz tcm_lib_search-data-v1.0.tar.gz
DOI: 10.6084/m9.figshare.5505157
title: Semiconductor SERS enhancement enabled by oxygen incorporation

description: Semiconductor-based
surface-enhanced Raman spectroscopy (SERS) substrates represent a new frontier
in the field of SERS. However, the application of
semiconductor materials as SERS substrates is still seriously impeded by their
low SERS enhancement and inferior detection sensitivity, especially for
non-metal-oxide semiconductor materials. Herein, we demonstrate a general oxygen-incorporation-assisted
strategy to magnify the semiconductor substrate–analyte molecule interaction,
leading to significant increase in SERS enhancement for non-metal-oxide
semiconductor materials. Oxygen incorporation in MoS<sub>2</sub> even with
trace concentrations can not only increase enhancement factors by up to 100,000
folds compared with oxygen-unincorporated samples, but also endow MoS<sub>2</sub>
with low limit of detection below 10<sup>-7</sup> M. Intriguingly,
combined with the findings in previous studies, our present results indicate
that both oxygen incorporation and extraction processes can result in SERS
enhancement, probably due to the enhanced charge-transfer resonance as
well as exciton resonance arising from
the judicious control of oxygen admission in semiconductor
substrate.
DOI: 10.6084/m9.figshare.5505157.v1
title: Semiconductor SERS enhancement enabled by oxygen incorporation

description: Semiconductor-based
surface-enhanced Raman spectroscopy (SERS) substrates represent a new frontier
in the field of SERS. However, the application of
semiconductor materials as SERS substrates is still seriously impeded by their
low SERS enhancement and inferior detection sensitivity, especially for
non-metal-oxide semiconductor materials. Herein, we demonstrate a general oxygen-incorporation-assisted
strategy to magnify the semiconductor substrate–analyte molecule interaction,
leading to significant increase in SERS enhancement for non-metal-oxide
semiconductor materials. Oxygen incorporation in MoS<sub>2</sub> even with
trace concentrations can not only increase enhancement factors by up to 100,000
folds compared with oxygen-unincorporated samples, but also endow MoS<sub>2</sub>
with low limit of detection below 10<sup>-7</sup> M. Intriguingly,
combined with the findings in previous studies, our present results indicate
that both oxygen incorporation and extraction processes can result in SERS
enhancement, probably due to the enhanced charge-transfer resonance as
well as exciton resonance arising from
the judicious control of oxygen admission in semiconductor
substrate.
DOI: 10.6084/m9.figshare.5505610
title: Semiconductor SERS enhancement enabled by oxygen incorporation

description: Semiconductor-based
surface-enhanced Raman spectroscopy (SERS) substrates represent a new frontier
in the field of SERS. However, the application of
semiconductor materials as SERS substrates is still seriously impeded by their
low SERS enhancement and inferior detection sensitivity, especially for
non-metal-oxide semiconductor materials. Herein, we demonstrate a general oxygen-incorporation-assisted
strategy to magnify the semiconductor substrate–analyte molecule interaction,
leading to significant increase in SERS enhancement for non-metal-oxide
semiconductor materials. Oxygen incorporation in MoS<sub>2</sub> even with
trace concentrations can not only increase enhancement factors by up to 100,000
folds compared with oxygen-unincorporated samples, but also endow MoS<sub>2</sub>
with low limit of detection below 10<sup>-7</sup> M. Intriguingly,
combined with the findings in previous studies, our present results indicate
that both oxygen incorporation and extraction processes can result in SERS
enhancement, probably due to the enhanced charge-transfer resonance as
well as exciton resonance arising from
the judicious control of oxygen admission in semiconductor
substrate.
DOI: 10.6084/m9.figshare.5505610.v1
title: Semiconductor SERS enhancement enabled by oxygen incorporation

description: Semiconductor-based
surface-enhanced Raman spectroscopy (SERS) substrates represent a new frontier
in the field of SERS. However, the application of
semiconductor materials as SERS substrates is still seriously impeded by their
low SERS enhancement and inferior detection sensitivity, especially for
non-metal-oxide semiconductor materials. Herein, we demonstrate a general oxygen-incorporation-assisted
strategy to magnify the semiconductor substrate–analyte molecule interaction,
leading to significant increase in SERS enhancement for non-metal-oxide
semiconductor materials. Oxygen incorporation in MoS<sub>2</sub> even with
trace concentrations can not only increase enhancement factors by up to 100,000
folds compared with oxygen-unincorporated samples, but also endow MoS<sub>2</sub>
with low limit of detection below 10<sup>-7</sup> M. Intriguingly,
combined with the findings in previous studies, our present results indicate
that both oxygen incorporation and extraction processes can result in SERS
enhancement, probably due to the enhanced charge-transfer resonance as
well as exciton resonance arising from
the judicious control of oxygen admission in semiconductor
substrate.
DOI: 10.6084/m9.figshare.5513563
title: ISWC 2017 full metadata

description: Metadata of the ISWC2017 conference (https://iswc2017.semanticweb.org), serialised into Turtle format. This data set covers information across all tracks (authors, papers, organisations, etc). For further information, please contact the authors via the following link: https://iswc2017.semanticweb.org/program/iswc-metadata/<br><br>The metadata is in a single compressed .gz file that can be opened with standard compression utilities, metadata is provided in Terse RDF Triple Language; .ttl that can be openly accessed via text edit software.<br><br>ISWC 2017, the 16th International Semantic Web Conference, taking place on 21–25 October 2017 in Vienna, Austria, is the premier international forum for the Semantic Web / Linked Data Community. As such, it is committed to publishing data in a reusable way.<br><br>Data for the ISWC 2017 conference proceedings are available at https://springernature.figshare.com/semweb. The conference proceedings papers themselves are available via https://dx.doi.org/10.1007/978-3-319-68288-4
DOI: 10.6084/m9.figshare.5513563.v1
title: ISWC 2017 full metadata

description: Metadata of the ISWC2017 conference (https://iswc2017.semanticweb.org), serialised into Turtle format. This data set covers information across all tracks (authors, papers, organisations, etc). For further information, please contact the authors via the following link: https://iswc2017.semanticweb.org/program/iswc-metadata/<br><br>The metadata is in a single compressed .gz file that can be opened with standard compression utilities, metadata is provided in Terse RDF Triple Language; .ttl that can be openly accessed via text edit software.<br><br>ISWC 2017, the 16th International Semantic Web Conference, taking place on 21–25 October 2017 in Vienna, Austria, is the premier international forum for the Semantic Web / Linked Data Community. As such, it is committed to publishing data in a reusable way.<br><br>Data for the ISWC 2017 conference proceedings are available at https://springernature.figshare.com/semweb. The conference proceedings papers themselves are available via https://dx.doi.org/10.1007/978-3-319-68288-4
DOI: 10.25378/janelia.5514319
title: Fly Light Split-GAL4 Driver Collection

description: The data presented on this site are the work of the Janelia FlyLight Project Team and the laboratories of Gerald M. Rubin. The split-GAL4 lines can be requested from the Janelia fly facility by performing a search and adding the desired lines to your cart. You will then be able to use the FlyBank website to tell us where to send them. For additional help ordering lines, please contact us at flybank.janelia.orgIn publications, please attribute the data presented on this site to one of the following papers, as follows: <br><br>For the overall strategy and methods used to produce the split-GAL4 lines for the mushroom body neurons: <br>Aso, Y., Hattori, D., Yu, Y., Johnston, R. M., Iyer, N., Ngo, T. B., Dionne, H., Abbott, L. F., Axel, R., Tanimoto, H. & Rubin, G. M. . The neuronal architecture of the mushroom body provides a logic for associative learning. eLife (2014) 3:e04577<br><br>For split-GAL4 lines for the Lobula Columnar (LC) visual projection neurons:<br>Wu, M., Nern, A., Williamson, W. R., Morimoto, M. M., Reiser, M. B., Card, G. M. & Rubin, G. M. Visual projection neurons in the Drosophila lobula link feature detection to distinct behavioral programs. under review<br><br>For refinement of the split-GAL4 vectors and methodology: <br>Pfeiffer, B. D., Ngo, T. T., Hibbard, K. L., Murphy, C., Jenett, A., Truman, J. W. & Rubin, G. M. Refinement of tools for targeted gene expression in Drosophila. Genetics (2010) 186: 735-55. <br><br>For Multicolor Flp-out (MCFO) technique and single cell labeling:<br>Nern, A., Pfeiffer, B.D., and Rubin, G.M. Optimized tools for multicolor stochastic labeling reveal diverse stereotyped cell arrangements in the fly visual system. Proc Natl Acad Sci USA (2015) 112: E2967-2976. <br><br>Split-GAL4 lines were designed based on the expression patterns of GAL4 driver lines in the adult nervous system: <br>The Janelia collection of lines is described in Jenett, A., Rubin, G.M., Ngo, T.-T. B., Shepherd, D., Murphy, C., Dionne, H., Pfeiffer, B.D., Cavallaro, A., Hall, D., Jeter, J., Iyer, N., Fetter, D., Hausenfluck, J.H., Peng, H., Trautman, E., Svirskas, R., Myers, G.W., Iwinski, Z.R., Aso, Y., DePasquale, G.M., Enos, A., Hulamm, P., Lam, S.C.B., Li, H-H., Laverty, T., Long, F., Qu, L., Murphy, S.D., Rokicki, K., Safford, T., Shaw, K., Simpson, J.H., Sowell, A., Tae, S., Yu, Y., Zugates, C.T. A GAL4-Driver Line Resource for Drosophila Neurobiology. Cell Reports (2012) 2: 991-1001 <br><br>The VT collection of lines is described in Kvon, E.Z., Kazmar, T., Stampfel, G., Yanez-Cuna, J.O., Pagani, M., Schernhuber, K., Dickson, B.J., and Stark, A. Genome-scale functional characterization of Drosophila developmental enhancers in vivo. Nature (2014) 512: 91-95 and Barry J. Dickson, unpublished data. <br><br>For opening and viewing h5j and LSM stacks:<br>Use Fiji (http://fiji.sc). Fiji has a built-in plugin (H5J_Loader_Plugin-1.0.4) for opening stack in h5j format, a "visually lossless" compression format.
DOI: 10.25378/janelia.5514319.v1
title: Fly Light Split-GAL4 Driver Collection

description: The data presented on this site are the work of the Janelia FlyLight Project Team and the laboratories of Gerald M. Rubin. The split-GAL4 lines can be requested from the Janelia fly facility by performing a search and adding the desired lines to your cart. You will then be able to use the FlyBank website to tell us where to send them. For additional help ordering lines, please contact us at flybank.janelia.orgIn publications, please attribute the data presented on this site to one of the following papers, as follows: <br><br>For the overall strategy and methods used to produce the split-GAL4 lines for the mushroom body neurons: <br>Aso, Y., Hattori, D., Yu, Y., Johnston, R. M., Iyer, N., Ngo, T. B., Dionne, H., Abbott, L. F., Axel, R., Tanimoto, H. & Rubin, G. M. . The neuronal architecture of the mushroom body provides a logic for associative learning. eLife (2014) 3:e04577<br><br>For split-GAL4 lines for the Lobula Columnar (LC) visual projection neurons:<br>Wu, M., Nern, A., Williamson, W. R., Morimoto, M. M., Reiser, M. B., Card, G. M. & Rubin, G. M. Visual projection neurons in the Drosophila lobula link feature detection to distinct behavioral programs. under review<br><br>For refinement of the split-GAL4 vectors and methodology: <br>Pfeiffer, B. D., Ngo, T. T., Hibbard, K. L., Murphy, C., Jenett, A., Truman, J. W. & Rubin, G. M. Refinement of tools for targeted gene expression in Drosophila. Genetics (2010) 186: 735-55. <br><br>For Multicolor Flp-out (MCFO) technique and single cell labeling:<br>Nern, A., Pfeiffer, B.D., and Rubin, G.M. Optimized tools for multicolor stochastic labeling reveal diverse stereotyped cell arrangements in the fly visual system. Proc Natl Acad Sci USA (2015) 112: E2967-2976. <br><br>Split-GAL4 lines were designed based on the expression patterns of GAL4 driver lines in the adult nervous system: <br>The Janelia collection of lines is described in Jenett, A., Rubin, G.M., Ngo, T.-T. B., Shepherd, D., Murphy, C., Dionne, H., Pfeiffer, B.D., Cavallaro, A., Hall, D., Jeter, J., Iyer, N., Fetter, D., Hausenfluck, J.H., Peng, H., Trautman, E., Svirskas, R., Myers, G.W., Iwinski, Z.R., Aso, Y., DePasquale, G.M., Enos, A., Hulamm, P., Lam, S.C.B., Li, H-H., Laverty, T., Long, F., Qu, L., Murphy, S.D., Rokicki, K., Safford, T., Shaw, K., Simpson, J.H., Sowell, A., Tae, S., Yu, Y., Zugates, C.T. A GAL4-Driver Line Resource for Drosophila Neurobiology. Cell Reports (2012) 2: 991-1001 <br><br>The VT collection of lines is described in Kvon, E.Z., Kazmar, T., Stampfel, G., Yanez-Cuna, J.O., Pagani, M., Schernhuber, K., Dickson, B.J., and Stark, A. Genome-scale functional characterization of Drosophila developmental enhancers in vivo. Nature (2014) 512: 91-95 and Barry J. Dickson, unpublished data. <br><br>For opening and viewing h5j and LSM stacks:<br>Use Fiji (http://fiji.sc). Fiji has a built-in plugin (H5J_Loader_Plugin-1.0.4) for opening stack in h5j format, a "visually lossless" compression format.
DOI: 10.6084/m9.figshare.5519149
title: Brain size in Hylarana guentheri seems unaffected by variation in temperature and growth season. Supplementary Table S1

description: Brain size
varies dramatically between vertebrate species. Two prominent adaptive
hypotheses – the Cognitive Buffer Hypothesis (CBH) and the Expensive Brain
Hypothesis (EBH) – have been proposed to explain brain size evolution. The CBH
assumes that brain size should increase with seasonality, as the cognitive
benefits of a larger brain should help overcoming periods of food scarcity via,
for example, increased behavioral flexibility. Alternatively, the EBH states
that brain size should decrease with seasonality because a smaller brain
confers energetic benefits in periods of food scarcity. Here, to test the two
adaptive hypotheses by studying the effects of variation in temperature and
growth season on variations in overall brain size and the size of specific
brain regions (<i>viz</i>. olfactory nerves, olfactory bulbs, telencephalon,
optic tectum and cerebellum) among <i>Hylarana guentheri </i>populations.
Inconsistent with the predictions of both the EBH and the CBH, variation in
temperature and growth season did not exhibit correlations with overall brain
size and the size of brain regions across populations. Hence, our data do not
provide support for either the EBH or the CBH to explain brain size variation in
<i>H. guentheri</i>. Furthermore, brain size variation did not differ between
males and females in this species. Our findings suggest that both the variation
in temperature and growth season did not shape the variation in brain size in <i>H.
guentheri</i>.
DOI: 10.6084/m9.figshare.5519149.v1
title: Brain size in Hylarana guentheri seems unaffected by variation in temperature and growth season. Supplementary Table S1

description: Brain size
varies dramatically between vertebrate species. Two prominent adaptive
hypotheses – the Cognitive Buffer Hypothesis (CBH) and the Expensive Brain
Hypothesis (EBH) – have been proposed to explain brain size evolution. The CBH
assumes that brain size should increase with seasonality, as the cognitive
benefits of a larger brain should help overcoming periods of food scarcity via,
for example, increased behavioral flexibility. Alternatively, the EBH states
that brain size should decrease with seasonality because a smaller brain
confers energetic benefits in periods of food scarcity. Here, to test the two
adaptive hypotheses by studying the effects of variation in temperature and
growth season on variations in overall brain size and the size of specific
brain regions (<i>viz</i>. olfactory nerves, olfactory bulbs, telencephalon,
optic tectum and cerebellum) among <i>Hylarana guentheri </i>populations.
Inconsistent with the predictions of both the EBH and the CBH, variation in
temperature and growth season did not exhibit correlations with overall brain
size and the size of brain regions across populations. Hence, our data do not
provide support for either the EBH or the CBH to explain brain size variation in
<i>H. guentheri</i>. Furthermore, brain size variation did not differ between
males and females in this species. Our findings suggest that both the variation
in temperature and growth season did not shape the variation in brain size in <i>H.
guentheri</i>.
DOI: 10.25378/janelia.5519755
title: FlyEM Connectome Project EM Neural Circuits Source Data

description: Repositories of EM Source DataAt Janelia, the FlyEM Project is developing technology that will enable us to create a map of every neuron and synapse in the Drosophila nervous system, using novel approaches to electron microscopy (EM) as the foundation. The data offered here can be used to map neural circuitry. Fly medulla small ground truth volumesThis repository contains several small subvolumes of EM data from the Drosophila optic lobe medulla. These are regions from the same dataset as medulla7column in the DICED gs://flyem-public-connectome but the ground truth is more accurate. These examples are documented in more detail in neuroproof_examples.The ground truth is not pixel-perfect but should be suitable for supervised training.Each subvolume is listed below. For a given volume X, there is a 3D grayscale volume X-grayscale and a groundtruth volume X-groundtruth.training2: 250x250x250 subvolume (corresponds to training_sample2 in neuroproof_examples)val: 500x500x500 subvolume (corresponds to validation_sample in neuroproof_examples)Seven medulla column connectomeThis repository contains the EM image source data, segmentation, and proofread result for seven medulla columns in the Drosophila optic lobe. Information about this connectome and some of the interesting biological insights can be read here.The following describes the different 3D image arrays in this package and the various support files. This dataset should be conducive for studying 3D neuronal morphology, connectomes, and as ground truth for validating EM segmentation and synapse prediction algorithms. This package specifies a smaller ROI within this dataset where the reconstruction accuracy is high. The completeness is not 100% in this region so it might be better for validation instead of supervised classifier training.The results correspond to internal DVID version of #3b548. For convenience, that repository was flattened and copied into this one.Central complex small ground truth volumesThis repository contains several small subvolumes of EM data from the Drosophila central complex. Each subvolume of raw grayscale data is accompanied by a segmentation labels data.The ground truth is not pixel-perfect but should be suitable for supervised training.Each subvolume is described below. For a given volume X, there is a 3D grayscale volume X-grayscale and a groundtruth volume X-groundtruth. Some of the segmentation/body IDs have been identified as glia.pb1: ~500x500x500 subvolume in the fly protocerebral bridge. Body id 16232 represent glia.pb2: ~500x500x500 subvolume in the fly protocerebral bridge. Body id 16280 represent glia.
DOI: 10.6084/m9.figshare.5527906
title: Malaria infections, vectors and geophysical predictors

description: Three datasets are provide, 1 (malaria infections data), 2 (densities of Anopheles gambiae) and 3 (geophysical data). The geophysical variables were linked with either malaria infections or densities of An. gambiae outcomes using a TCU_cluster variable, which appears in each of the three datasets. This variable provides the spatial unit of analysis for the study. All data were aggregated at a TCU level, which is a cluster of at least ten houses to a maximum of one hundred.<br>
DOI: 10.6084/m9.figshare.5527906.v1
title: Malaria infections, vectors and geophysical predictors

description: Three datasets are provide, 1 (malaria infections data), 2 (densities of Anopheles gambiae) and 3 (geophysical data). The geophysical variables were linked with either malaria infections or densities of An. gambiae outcomes using a TCU_cluster variable, which appears in each of the three datasets. This variable provides the spatial unit of analysis for the study. All data were aggregated at a TCU level, which is a cluster of at least ten houses to a maximum of one hundred.<br>
DOI: 10.6084/m9.figshare.5531737
title: Nucleopolyhedrovirus infection enhances plant defences by increasing plant volatile diversity

description: Plant–herbivore–entomopathogen tri-trophic interactions and biodiversity are relatively understudied topics in ecology. Particularly, the effects of entomopathogens on herbivore-induced plant volatiles and plant volatile diversity on the defensive function of plants have not been studied in detail. We used soybean (<i>Glycine max</i>), beet armyworm larvae (<i>Spodoptera exigua</i>), and nucleopolyhedrovirus (NPV) as a tri-trophic system to determine whether NPV infection can promote the emission and diversity of volatiles from plants. We also investigated whether NPV infection affects the attraction of <i>Microplitis pallidipes</i>, an important endoparasitoid of larval <i>S</i>. <i>exigua</i>. Uninfested soybean plants released 7 detectable volatile compounds while plants fed upon by healthy and NPV-infected <i>S. exigua</i> larvae released 12 and 15 volatiles, respectively. Female parasitoids were more attracted to the volatiles from plants that were fed upon by NPV-infected larvae than healthy larvae, and more attracted to the volatiles from plants that were fed upon by healthy larvae than no larvae. The selective responses of parasitoids to plant odours increased as plant volatile diversity increased. Our study suggests that the NPV infection facilitates the release of plant volatiles and enhances the defensive function of plants by increasing plant volatile diversity which in turn attracts more parasitoids. Also, this work reveals that plants might accrue two indirect benefits from NPV infection, cessation of herbivore feeding and more parasitisation.