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2,031 results for “Transformation”
Latent Transforming Growth Factor β Binding Protein 3 Controls Adipogenesis
<p>Transforming growth factor-beta (TGFβ) is released from cells as part of a trimeric latent complex consisting of TGFβ, the TGFβ propeptides, and either a latent TGFβ binding protein (LTBP) or glycoprotein-A repetitions predominant (GARP) protein. LTBP1 and 3 modulate latent TGFβ function with respect to secretion, matrix localization, and activation and, therefore, are vital for the proper function of the cytokine in a number of tissues. TGFβ modulates stem cell differentiation into adipocytes (adipogenesis), but the potential role of LTBPs in this process has not been studied. We observed that 72 h post adipogenesis initiation <em>Ltbp1</em>, <em>2</em>, and <em>4 </em>expression levels decrease by 74-84%, whereas <em>Ltbp3 </em>expression levels remain constant during adipogenesis. We found that LTBP3 silencing in C3H/10T1/2 cells reduced adipogenesis, as measured by the percentage of cells with lipid vesicles and the expression of the transcription factor peroxisome proliferator-activated receptor gamma (PPARγ). Lentiviral mediated expression of an <em>Ltbp3 </em>mRNA resistant to siRNA targeting rescued the phenotype, validating siRNA specificity. Knockdown (KD) of Ltbp3 expression in 3T3-L1, M2, and primary bone marrow stromal cells (BMSC) indicated a similar requirement for <em>Ltbp3</em>. Epididymal and inguinal white adipose tissue fat pad weights of <em>Ltbp3</em><sup>-/-</sup> mice were reduced by 62% and 57%, respectively, compared to wild-type mice. Inhibition of adipogenic differentiation upon LTBP3 loss is mediated by TGFβ, as TGFβ neutralizing antibody and TGFβ receptor I kinase blockade rescue the LTBP3 KD phenotype. These results indicate that LTBP3 has a TGFβ-dependent function in adipogenesis both in vitro and possibly in vivo. </p>
Sulfamethoxazole transformation products in anaerobic batch assays
<p>The document contains the ion spectra and sulfamethoxazole’s extracted ion chromatogram (XIC). The antibiotic was spiked in anaerobic batch assays containing graphene oxide at different levels. Two transformation products were detected, and the proposed biotransformation pathway is described in the last figure.</p>
2D honeycomb transformation into dodecagonal quasicrystals driven by electrostatic forces
<p>This repository contains the key input and output files used for the density fucntional theory calculations of the paper "Mechanism of 2D Oxide Quasicrystal formation from honeycomb structures" by Sebastian Schenk, Oliver Krahn, Eric Cockayne, Holger L. Meyerheim, Marc deBoissieu, Stefan F"orster, and Wolf Widdra (2022).</p> <p>The calculations were performed using the DFT code VASP, version 5.4.4 [Commercial software is mentioned in this README file to adquately described the procedure. This does not imply an endorsement or recommendation by the National Institute of Standards and Technology, nor that the software used is necessarily the best for the given<br> purpose.]</p> <p>The subfolder large_approximant contains the files for the large Sr<sub>48</sub>Ti<sub>132</sub>O<sub>204</sub> approximant on a Pt monolayer. Subfolders honeycomb/Pt<sub>N</sub> and sigma/Pt<sub>N</sub> contain the files for honeycomb and sigma Ba<sub>8</sub>Ti<sub>24</sub>O<sub>36</sub> structures on Pt trilayers with N Pt per layer per periodic cell. Subfolders honeycomb/Pt<sub>N</sub>/substrate contain the corresponding files<br> for the Pt substrate alone. The honeycomb and sigma structures are at the equilibrium strain as determined by matching interpolated stress results, as described in the Supplementary Information associated with the main Article.</p> <p>The input files are the standard VASP input files: POSCAR (structure information), POTCAR_TITEL (pseudopotential information. Because the VASP pseudopotential files are proprietary, only the titles of the pseudopotentials used are given), KPOINTS (k-point generation) and INCAR (most calculation details). To accelerate the DFT van der Waals calculation, the file vdw_kernel.bindat from the VASP package (not included here) should also be used. The output files are OSZICAR (summarizes energy at each iteration) and OUTCAR (full ouput).</p>
Data for manuscript: "Longitudinal Analysis of Sentiment and Emotion in News Media Headlines Using Automated Labelling with Transformer Language Models"
<p>This data set contains automated sentiment and emotionality annotations of 23 million headlines from 47 popular news media outlets popular in the United States. </p> <p>The set of 47 news media outlets analysed (listed in Figure 1 of the main manuscript) was derived from the AllSides organization <a href="https://www.allsides.com/blog/updated-allsides-media-bias-chart-version-11">2019 Media Bias Chart v1.1</a>. The human ratings of outlets’ ideological leanings were also taken from this chart and are listed in Figure 2 of the main manuscript. </p> <p>News articles headlines from the set of outlets analyzed in the manuscript are available in the outlets’ online domains and/or public cache repositories such as The Internet Wayback Machine, Google cache and Common Crawl. Articles headlines were located in articles’ HTML raw data using outlet-specific XPath expressions. </p> <p>The temporal coverage of headlines across news outlets is not uniform. For some media organizations, news articles availability in online domains or Internet cache repositories becomes sparse for earlier years. Furthermore, some news outlets popular in 2019, such as <em>The Huffington Post</em> or <em>Breitbart</em>, did not exist in the early 2000’s. Hence, our data set is sparser in headlines sample size and representativeness for earlier years in the 2000-2019 timeline. Nevertheless, 18 outlets in our data set have chronologically continuous partial or full headline data availability fulfilling our inclusive criteria (see manuscript Methods) since the year 2000. Figure S 1 in the SI reports the number of headlines per outlet and per year in our analysis.</p> <p>In a small percentage of articles, outlet specific XPath expressions might fail to properly capture the content of the headline due to the heterogeneity of HTML elements and CSS styling combinations with which articles text content is arranged in outlets online domains. After manual testing, we determined that the percentage of headlines following in this category is very small. Additionally, our method might miss detecting some articles in the online domains of news outlets. To conclude, in a data analysis of over 23 million headlines, we cannot manually check the correctness of every single data instance and hundred percent accuracy at capturing headlines’ content is elusive due to the small number of difficult to detect boundary cases such as incorrect HTML markup syntax in online domains. Overall however, we are confident that our headlines set is representative of headlines in print news media content for the studied time period and outlets analyzed.</p> <p>The list of compressed files in this data set is listed next:</p> <p>-analysisScripts.rar contains the analysis scripts used in the main manuscript as well as aggregated data of sentiment and emotionality automated annotations of the headlines and human annotations of a subset of headlines sentiment and emotionality used as ground truth. </p> <p>-models.rar contains the Transformer sentiment and emotion annotation models used in the analysis. Namely: </p> <p>Siebert/sentiment-roberta-large-english from https://huggingface.co/siebert/sentiment-roberta-large-english. This model is a fine-tuned checkpoint of <a href="https://huggingface.co/roberta-large">RoBERTa-large</a> (<a href="https://arxiv.org/pdf/1907.11692.pdf">Liu et al. 2019</a>). It enables reliable binary sentiment analysis for various types of English-language text. For each instance, it predicts either positive (1) or negative (0) sentiment. The model was fine-tuned and evaluated on 15 data sets from diverse text sources to enhance generalization across different types of texts (reviews, tweets, etc.). See more information from the original authors at https://huggingface.co/siebert/sentiment-roberta-large-english</p> <p>DistilbertSST2.rar is the default sentiment classification model of the HuggingFace Transformer library https://huggingface.co/ This model is only used to replicate the results of the sentiment analysis with sentiment-roberta-large-english </p> <p>DistilRoberta j-hartmann/emotion-english-distilroberta-base from https://huggingface.co/j-hartmann/emotion-english-distilroberta-base. The model is a fine-tuned checkpoint of <a href="https://huggingface.co/distilroberta-base">DistilRoBERTa-base</a>. The model allows annotation of English text with Ekman's 6 basic emotions, plus a neutral class. The model was trained on 6 diverse datasets. Please refer to the original author at https://huggingface.co/j-hartmann/emotion-english-distilroberta-base for an overview of the data sets used for fine tuning. https://huggingface.co/j-hartmann/emotion-english-distilroberta-base</p> <p>-headlinesDataWithSentimentLabelsAnnotationsFromSentimentRobertaLargeModel.rar URLs of headlines analyzed and the sentiment annotations of the siebert/sentiment-roberta-large-english Transformer model. https://huggingface.co/siebert/sentiment-roberta-large-english</p> <p>-headlinesDataWithSentimentLabelsAnnotationsFromDistilbertSST2.rar URLs of headlines analyzed and the sentiment annotations of the default HuggingFace sentiment analysis model fine-tuned on the SST-2 dataset. https://huggingface.co/</p> <p>-headlinesDataWithEmotionLabelsAnnotationsFromDistilRoberta.rar URLs of headlines analyzed and the emotion categories annotations of the j-hartmann/emotion-english-distilroberta-base Transformer model. https://huggingface.co/j-hartmann/emotion-english-distilroberta-base</p>
Transformed Eulerian mean data from the ERA5 reanalysis (daily means)
<p>This dataset provides <strong>daily</strong> and zonal mean variables derived from the ERA5 reanalysis, including terms of the transformed Eulerian mean (TEM) momentum budget.</p> <p>All variables (zonal, meridional and vertical wind speed, temperature, zonal wind tendencies from Eliassen-Palm (EP) flux divergence and advection, EP fluxes and the residual streamfunction) are obtained from 6-hourly and native vertical and 0.5 degrees spatial resolution data. Zonal mean wind tendency from parameterizations is also provided (from the forecasts). Data are obtained from the MARS archive.</p> <p>Data are provided as one .zip file per decade (only partial for the 2020s and 1950s). <strong>Monthly</strong> means of the same quantities are provided in a companion dataset (10.5281/zenodo.7081721).</p> <p>The data and related documentation are provided 'as is' and without any warranty of any kind. Users are invited to report any issue or inconsistency they may find. Please cite the reference publication when using this dataset.</p> <p> </p> <p>Known issues:</p> <p>- All TEM terms divided by<em> <span>\(\cos(\phi)\)</span></em>, where <span>\(\phi\)</span> is latitude, diverge at the north and south poles (where <span>\(\phi = \pm \pi/2\)</span>), so they should not be considered. If variables at the poles are needed, values at neighbouring latitudes should be taken.</p>
Transformed Eulerian mean data from the ERA5 reanalysis (monthly means)
<p>This dataset provides <strong>monthly</strong> and zonal mean variables derived from the ERA5 reanalysis, including terms of the transformed Eulerian mean (TEM) momentum budget.</p> <p>All variables (zonal, meridional and vertical wind speed, temperature, zonal wind tendencies from Eliassen-Palm (EP) flux divergence and advection, EP fluxes and the residual streamfunction) are obtained from 6-hourly and native vertical and 0.5 degrees spatial resolution data. Zonal mean wind tendency from parameterizations is also provided (from the forecasts). Data are obtained from the MARS archive.</p> <p>Data are provided as one .zip file per decade (only partial for the 2020s and 1950s). <strong>Daily</strong> means of the same quantities are provided in a companion dataset (10.5281/zenodo.7081436).</p> <p>The data and related documentation are provided 'as is' and without any warranty of any kind. Users are invited to report any issue or inconsistency they may find.</p> <p> </p> <p>Known issues:</p> <p>- All TEM terms divided by<em> <span>\(\cos(\phi)\)</span></em>, where <span>\(\phi\)</span> is latitude, diverge at the north and south poles (where <span>\(\phi = \pm \pi/2\)</span>), so they should not be considered. If variables at the poles are needed, values at neighbouring latitudes should be taken.</p>
Internet of Things - transforming businesses, people's lives and driving growth in the coming years
<p><b>Abstract</b></p><p class="dhik-abstract-content">IoT is the biggest computer revolution and will transform businesses, people's lives and drive growth in the coming years. We discuss novel IoT systems, technologies, and future trends to increase productivity, efficiency and quality in manufacturing, smart buildings, energy and wireless industries.</p><p></p><p><b>Weitere Beiträge aus dem DHIK-Forum 2022 auf Zenodo:</b></p><p class="dhik-session-list"></p><ul><li>Session #1: Viktor Sigrist: Internationalisierung - Partnerschaften für den Ausbau von Forschung und Entwicklung (DOI:<a href="https://zenodo.org/record/7123701">10.5281/zenodo.7123701</a>)</li><li>Session #2: Dieter Leonhard: DHIK- Strategien der internationalen Zusammenarbeit in Forschung und Lehre (DOI:<a href="https://zenodo.org/record/7123456">10.5281/zenodo.7123456</a>)</li><li>Session #3: Stephen Wittkopf: Wissens- und Innovationstransfer - Interdisziplinäre Zusammenarbeit mit Unternehmen und Institutionen (DOI:<a href="https://zenodo.org/record/7025707">10.5281/zenodo.7025707</a>)</li><li>Session #4: Xiao Feng: CDHAW - Chinesisch-Deutsche Hochschule für Angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123458">10.5281/zenodo.7123458</a>)</li><li>Session #5: Antonio Pita und Isabel Kreiner: Academy-Industry-Collaboration - Outreach Strategy (DOI:<a href="https://zenodo.org/record/7123460">10.5281/zenodo.7123460</a>)</li><li>Session #6: Martin Sternberg: Promotionsrecht – aktueller Stand an deutschen Hochschulen für angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123757">10.5281/zenodo.7123757</a>)</li><li>Session #7: Adrian Derungs: Duo mit Innovationskraft - Zusammenspiel von Forschung und Wirtschaft in der Zentralschweiz (DOI:<a href="https://zenodo.org/record/7123767">10.5281/zenodo.7123767</a>)</li><li>Session #8: Theres Paulsen: Transdisziplinäre Forschung - komplexe gesellschaftliche Herausforderungen erfordern diverse Ansätze (DOI:<a href="https://zenodo.org/record/7123769">10.5281/zenodo.7123769</a>)</li><li>Session #9: Jörg Schneider: International research collaboration - New funding opportunities for universities of applied sciences (DOI:<a href="https://zenodo.org/record/7123771">10.5281/zenodo.7123771</a>)</li><li>Session #10: Cornelia Spycher und Matthew Whellens: Horizon Europe - overview of funding opportunities for your research and innovation (DOI:<a href="https://zenodo.org/record/7123773">10.5281/zenodo.7123773</a>)</li><li>Session #11: Janique Siffert: Eureka Eurostars - erfolgreiche Förderung für internationale Innovationsprojekte (DOI:<a href="https://zenodo.org/record/7123777">10.5281/zenodo.7123777</a>)</li><li>Session #12: Ludger Fischer: Energy Lab - ein Netzwerk für innovative Lösungen im Energiebereich (DOI:<a href="https://zenodo.org/record/7123779">10.5281/zenodo.7123779</a>)</li><li>Session #13: Jörg Worlitschek: Thermal energy storage - heating the north, cooling the south (DOI:<a href="https://zenodo.org/record/7123781">10.5281/zenodo.7123781</a>)</li><li>Session #14: Jonas Mühlethaler: Neues DC Microgrid-Konzept – netzunabhängige Elektrifizierung in Entwicklungsländern (DOI:<a href="https://zenodo.org/record/7123783">10.5281/zenodo.7123783</a>)</li><li>Session #15: Tommy Claussen: Dekarbonisierung des Gebäudesektors - digitale Transformation in der Gebäudetechnik und im Gebäudemanagement (DOI:<a href="https://zenodo.org/record/7123785">10.5281/zenodo.7123785</a>)</li><li>Session #16: Christoph Imboden: Flexibility solutions - making the power grid fit for the future (DOI:<a href="https://zenodo.org/record/7123787">10.5281/zenodo.7123787</a>)</li><li>Session #17: Uwe Schulz: Spielerisches Sarnetz - Simulationen für die fossile Unabhängigkeit einer Ortschaft (DOI:<a href="https://zenodo.org/record/7123790">10.5281/zenodo.7123790</a>)</li><li>Session #18: Jana Koehler: Künstliche Intelligenz – Erfolg durch Erwünschtheit, Machbarkeit und Wirtschaftlichkeit (DOI:<a href="https://zenodo.org/record/7123792">10.5281/zenodo.7123792</a>)</li><li>Session #19: Rolf Kamps: KI in der Prävention - Befragungsmethoden und Schulungen trainieren, Krankheitserreger erkennen (DOI:<a href="https://zenodo.org/record/7123794">10.5281/zenodo.7123794</a>)</li><li>Session #20: Gwendolyne Pascua: Artificial Intelligence in Space - CIMON assisting astronauts on the International Space Station (DOI:<a href="https://zenodo.org/record/7123796">10.5281/zenodo.7123796</a>)</li><li>Session #21: Tobias Matter et.al.: Augmented Reality Soundscapes - mit maschinellem Lernen Klangkulissen von zukünftigen Bauvorhaben generieren (DOI:<a href="https://zenodo.org/record/7123798">10.5281/zenodo.7123798</a>)</li><li><b>Session #22: Angela Nicoara: Internet of Things - transforming businesses, people's lives and driving growth in the coming years (<a href="#collapseTwo">Video</a>)</b></li><li>Session #23: Adrian Koller: Feldrobotik - unermüdliche und zunehmend intelligentere Hilfe in der Landwirtschaft (DOI:<a href="https://zenodo.org/record/7123802">10.5281/zenodo.7123802</a>)</li><li>Session #24: Widar von Arx et.al.: Realisierung der Verkehrswende - Einfluss der Preispolitik in der Mobilität (DOI:<a href="https://zenodo.org/record/7124000">10.5281/zenodo.7124000</a>)</li><li>Session #25: Andreas Liebrich: Tourismusdateninfrastruktur - Was die Schweiz von Europa lernen kann (DOI:<a href="https://zenodo.org/record/7123806">10.5281/zenodo.7123806</a>)</li><li>Session #26: Frank Pöhlau und Stefan May: Find life on Mars - Schülerprojekte zur mobilien Robotik (DOI:<a href="https://zenodo.org/record/7123808">10.5281/zenodo.7123808</a>)</li><li>Session #27: Jiayun Shen: Open Innovation - Innovationsmanagement bei der Schweizerischen Post (DOI:<a href="https://zenodo.org/record/7123810">10.5281/zenodo.7123810</a>)</li><li>Session #28: Tobias Specker: Interkulturelles Management – innovative Konzepte zum Ausbau der China-Kompetenzen an Hochschulen (DOI:<a href="https://zenodo.org/record/7123812">10.5281/zenodo.7123812</a>)</li><li>Session #29: Elena Algorri: Swimming robots - exploring the unterwater from the surface (DOI:<a href="https://zenodo.org/record/7123814">10.5281/zenodo.7123814</a>)</li><li>Session #30: Sergio Camacho: Robotics and Digital Systems Engineering at the Tec de Monterrey (DOI:<a href="https://zenodo.org/record/7123816">10.5281/zenodo.7123816</a>)</li><li>Session #31: Thomas Dorn: Industrie 4.0 - Forschungskooperationen mit der CDHAW und der Tongji Universität Shanghai (DOI:<a href="https://zenodo.org/record/7123818">10.5281/zenodo.7123818</a>)</li><li>Session #32: Walter Reichert et.al.: Kollaboration und Unterstützung - Mobile Robotik und Exoskelette in der flexiblen Produktion (DOI:<a href="https://zenodo.org/record/7123820">10.5281/zenodo.7123820</a>)</li><li>Session #33: Louis Palmer: Solar Butterfly - climate pioneer world tour supported by HSLU (DOI:<a href="https://zenodo.org/record/7123822">10.5281/zenodo.7123822</a>)</li></ul><p></p>
Dekarbonisierung des Gebäudesektors - digitale Transformation in der Gebäudetechnik und im Gebäudemanagement
<p><b>Abstract</b></p><p class="dhik-abstract-content">Im Real Estate Bereich sind sehr viele Daten vorhanden. In der Regel sind diese jedoch unstrukturiert, ungenutzt und werden weder geteilt noch intelligent verknüpft. Dabei sind diese Daten der Schlüssel für die Skalierung von geeigneten CO2-Reduktionsmassnahmen. </p><p></p><p><b>Weitere Beiträge aus dem DHIK-Forum 2022 auf Zenodo:</b></p><p class="dhik-session-list"></p><ul><li>Session #1: Viktor Sigrist: Internationalisierung - Partnerschaften für den Ausbau von Forschung und Entwicklung (DOI:<a href="https://zenodo.org/record/7123701">10.5281/zenodo.7123701</a>)</li><li>Session #2: Dieter Leonhard: DHIK- Strategien der internationalen Zusammenarbeit in Forschung und Lehre (DOI:<a href="https://zenodo.org/record/7123456">10.5281/zenodo.7123456</a>)</li><li>Session #3: Stephen Wittkopf: Wissens- und Innovationstransfer - Interdisziplinäre Zusammenarbeit mit Unternehmen und Institutionen (DOI:<a href="https://zenodo.org/record/7025707">10.5281/zenodo.7025707</a>)</li><li>Session #4: Xiao Feng: CDHAW - Chinesisch-Deutsche Hochschule für Angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123458">10.5281/zenodo.7123458</a>)</li><li>Session #5: Antonio Pita und Isabel Kreiner: Academy-Industry-Collaboration - Outreach Strategy (DOI:<a href="https://zenodo.org/record/7123460">10.5281/zenodo.7123460</a>)</li><li>Session #6: Martin Sternberg: Promotionsrecht – aktueller Stand an deutschen Hochschulen für angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123757">10.5281/zenodo.7123757</a>)</li><li>Session #7: Adrian Derungs: Duo mit Innovationskraft - Zusammenspiel von Forschung und Wirtschaft in der Zentralschweiz (DOI:<a href="https://zenodo.org/record/7123767">10.5281/zenodo.7123767</a>)</li><li>Session #8: Theres Paulsen: Transdisziplinäre Forschung - komplexe gesellschaftliche Herausforderungen erfordern diverse Ansätze (DOI:<a href="https://zenodo.org/record/7123769">10.5281/zenodo.7123769</a>)</li><li>Session #9: Jörg Schneider: International research collaboration - New funding opportunities for universities of applied sciences (DOI:<a href="https://zenodo.org/record/7123771">10.5281/zenodo.7123771</a>)</li><li>Session #10: Cornelia Spycher und Matthew Whellens: Horizon Europe - overview of funding opportunities for your research and innovation (DOI:<a href="https://zenodo.org/record/7123773">10.5281/zenodo.7123773</a>)</li><li>Session #11: Janique Siffert: Eureka Eurostars - erfolgreiche Förderung für internationale Innovationsprojekte (DOI:<a href="https://zenodo.org/record/7123777">10.5281/zenodo.7123777</a>)</li><li>Session #12: Ludger Fischer: Energy Lab - ein Netzwerk für innovative Lösungen im Energiebereich (DOI:<a href="https://zenodo.org/record/7123779">10.5281/zenodo.7123779</a>)</li><li>Session #13: Jörg Worlitschek: Thermal energy storage - heating the north, cooling the south (DOI:<a href="https://zenodo.org/record/7123781">10.5281/zenodo.7123781</a>)</li><li>Session #14: Jonas Mühlethaler: Neues DC Microgrid-Konzept – netzunabhängige Elektrifizierung in Entwicklungsländern (DOI:<a href="https://zenodo.org/record/7123783">10.5281/zenodo.7123783</a>)</li><li><b>Session #15: Tommy Claussen: Dekarbonisierung des Gebäudesektors - digitale Transformation in der Gebäudetechnik und im Gebäudemanagement (<a href="#collapseTwo">Video</a>)</b></li><li>Session #16: Christoph Imboden: Flexibility solutions - making the power grid fit for the future (DOI:<a href="https://zenodo.org/record/7123787">10.5281/zenodo.7123787</a>)</li><li>Session #17: Uwe Schulz: Spielerisches Sarnetz - Simulationen für die fossile Unabhängigkeit einer Ortschaft (DOI:<a href="https://zenodo.org/record/7123790">10.5281/zenodo.7123790</a>)</li><li>Session #18: Jana Koehler: Künstliche Intelligenz – Erfolg durch Erwünschtheit, Machbarkeit und Wirtschaftlichkeit (DOI:<a href="https://zenodo.org/record/7123792">10.5281/zenodo.7123792</a>)</li><li>Session #19: Rolf Kamps: KI in der Prävention - Befragungsmethoden und Schulungen trainieren, Krankheitserreger erkennen (DOI:<a href="https://zenodo.org/record/7123794">10.5281/zenodo.7123794</a>)</li><li>Session #20: Gwendolyne Pascua: Artificial Intelligence in Space - CIMON assisting astronauts on the International Space Station (DOI:<a href="https://zenodo.org/record/7123796">10.5281/zenodo.7123796</a>)</li><li>Session #21: Tobias Matter et.al.: Augmented Reality Soundscapes - mit maschinellem Lernen Klangkulissen von zukünftigen Bauvorhaben generieren (DOI:<a href="https://zenodo.org/record/7123798">10.5281/zenodo.7123798</a>)</li><li>Session #22: Angela Nicoara: Internet of Things - transforming businesses, people's lives and driving growth in the coming years (DOI:<a href="https://zenodo.org/record/7123800">10.5281/zenodo.7123800</a>)</li><li>Session #23: Adrian Koller: Feldrobotik - unermüdliche und zunehmend intelligentere Hilfe in der Landwirtschaft (DOI:<a href="https://zenodo.org/record/7123802">10.5281/zenodo.7123802</a>)</li><li>Session #24: Widar von Arx et.al.: Realisierung der Verkehrswende - Einfluss der Preispolitik in der Mobilität (DOI:<a href="https://zenodo.org/record/7124000">10.5281/zenodo.7124000</a>)</li><li>Session #25: Andreas Liebrich: Tourismusdateninfrastruktur - Was die Schweiz von Europa lernen kann (DOI:<a href="https://zenodo.org/record/7123806">10.5281/zenodo.7123806</a>)</li><li>Session #26: Frank Pöhlau und Stefan May: Find life on Mars - Schülerprojekte zur mobilien Robotik (DOI:<a href="https://zenodo.org/record/7123808">10.5281/zenodo.7123808</a>)</li><li>Session #27: Jiayun Shen: Open Innovation - Innovationsmanagement bei der Schweizerischen Post (DOI:<a href="https://zenodo.org/record/7123810">10.5281/zenodo.7123810</a>)</li><li>Session #28: Tobias Specker: Interkulturelles Management – innovative Konzepte zum Ausbau der China-Kompetenzen an Hochschulen (DOI:<a href="https://zenodo.org/record/7123812">10.5281/zenodo.7123812</a>)</li><li>Session #29: Elena Algorri: Swimming robots - exploring the unterwater from the surface (DOI:<a href="https://zenodo.org/record/7123814">10.5281/zenodo.7123814</a>)</li><li>Session #30: Sergio Camacho: Robotics and Digital Systems Engineering at the Tec de Monterrey (DOI:<a href="https://zenodo.org/record/7123816">10.5281/zenodo.7123816</a>)</li><li>Session #31: Thomas Dorn: Industrie 4.0 - Forschungskooperationen mit der CDHAW und der Tongji Universität Shanghai (DOI:<a href="https://zenodo.org/record/7123818">10.5281/zenodo.7123818</a>)</li><li>Session #32: Walter Reichert et.al.: Kollaboration und Unterstützung - Mobile Robotik und Exoskelette in der flexiblen Produktion (DOI:<a href="https://zenodo.org/record/7123820">10.5281/zenodo.7123820</a>)</li><li>Session #33: Louis Palmer: Solar Butterfly - climate pioneer world tour supported by HSLU (DOI:<a href="https://zenodo.org/record/7123822">10.5281/zenodo.7123822</a>)</li></ul><p></p>
Data for: Transformation of dissolved organic matter by two Indo-Pacific sponges
<p class="MsoNormal">Dissolved organic matter (DOM) is the largest organic carbon reservoir in the ocean and an integral component of biogeochemical cycles. The role of free-living microbes in DOM transformation has been studied thoroughly, whereas little attention has been directed towards the influence of benthic organisms. Sponges are efficient filter feeders and common inhabitants of many benthic communities circumglobally. In our study, we investigated how two tropical coral reef sponges shape marine DOM. We compared bacterial abundance, inorganic and organic nutrients in off reef, sponge inhalant, and sponge exhalant water of <em>Melophlus sarasinorum</em> and <em>Rhabdastrella globostellata</em>. DOM and bacterial cells were taken up, and dissolved inorganic nitrogen was released by the two Indo-Pacific sponges. Both sponge species utilized a common set of 142 of a total of 3040 compounds detected in DOM on a molecular formula level via ultrahigh-resolution mass spectrometry. In addition, species-specific uptake was observed, likely due to differences in their associated microbial communities. Overall, the sponges removed presumably semi-labile and semi-refractory compounds from the water column, thereby competing with pelagic bacteria. Within minutes, sponge holobionts altered the molecular composition of surface water DOM (inhalant) into a composition similar to deep-sea DOM (exhalent). The apparent radiocarbon age of DOM increased consistently from off reef and inhalant to exhalant by about 900 <sup>14</sup>C years for <em>M. sarasinorum</em>. In the pelagic, similar transformations require decades to centuries. Our results stress the dependence of DOM lability definition on the respective environment and illustrate that sponges are hotspots of DOM transformation in the ocean.</p> <p class="MsoNormal">Here, we provide the data from this study. The four tables contain the metadata, bulk measurements of dissolved organic carbon (DOC), total dissolved nitrogen, NOx, and radiocarbon dating of DOC, bacterioplankton abundances measured with a flow cytometer, and relative peak intensities of molecular formulas obtained through Fourier-transform ion cyclotron resonance mass spectrometry and the online tool ICBM-OCEAN.</p>
Variation in arthropod responses to tropical landscape transformation: spiders 2015
<b>Description: </b><p>Postdoctoral project</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/61"><b>Ecosystem quality and herbivore dynamics in tropical rainforests fragmented by deforestation</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>Australian Research Council (ARC Discovery Project, DP140101541)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=7252200">here</a></p><p><b>Files: </b>This consists of 1 file: Maunsell_spiders_220811.xlsx</p><p><b>Maunsell_spiders_220811.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Spider assemblage composition data 2015</b> (described in worksheet SpiderAssem)</p><p>Description: Spider assemblage composition data collected at the SAFE Project in 2015. Worksheet contains a site by morphospecies abundance matrix. Spiders were collected by beating plant foliage for a period of 20 minutes per location. Spiders were identified to family and seperated into morphospecies using a combination of genitalia dissections and DNA barcoding (CO1). </p><p>Number of fields: 219</p><p>Number of data rows: 48</p><p>Fields: </p><ul><li><b>Date</b>: Date of the collection (Field type: date)</li><li><b>Location</b>: SAFE Project location 2nd order (Field type: location)</li><li><b>Type</b>: Disturbance gradient (Field type: ordered categorical)</li><li><b>ANA01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA04_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA05_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA06_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA07_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA08_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA09_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA10_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA11_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA12_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA13_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA14_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA15_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ARA16_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>COR01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>COR02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>COR03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>COR04_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>COR05_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>DIC01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>EUT01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>EUT02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>EUT03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>EUT04_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>EUT05_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>EUT06_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>GNA01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>HAH01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>HAH02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>HER01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>LIN01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>LIN02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>MIM01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>MIM02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>MIM03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>MYS01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>MYS02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>MYS03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OON01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OON02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OON03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OON05_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OON06_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OON08_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OON09_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OON10_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OON11_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OXY01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OXY02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OXY03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OXY04_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OXY05_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>OXY06_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO04_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO05_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO06_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO07_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO08_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO09_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO11_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PHO13_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>PIS01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL04_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL05_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL06_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL07_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL08_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL09_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL10_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL11_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL12_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL13_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL14_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL15_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL16_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL17_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL18_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL19_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL20_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL21_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL22_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL23_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL24_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL25_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL26_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL27_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL28_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL29_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL30_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL31_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL32_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL33_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL34_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL35_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL36_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL37_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL38_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL39_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL40_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL41_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL42_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL43_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL44_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL45_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL46_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SAL47_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>SCY01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>TEB01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>TET01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>TET02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>TET03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>TET04_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>TET05_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>TET06_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD04_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD05_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD06_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD07_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD08_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD09_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD10_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD11_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD12_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD13_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD14_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD15_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD16_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD17_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD18_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD19_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD20_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD21_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD22_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD23_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD24_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD25_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD26_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD27_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD28_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD29_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD30_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD31_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD32_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD33_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD34_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD35_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD36_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD37_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD38_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD39_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD40_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD41_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD42_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD43_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD44_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD45_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD46_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD47_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD48_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD49_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD50_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD51_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD52_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD53_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD54_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD55_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD56_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD57_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD58_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD59_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD60_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD61_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD62_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD63_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD64_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD65_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD66_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THD67_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO04_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO05_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO06_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO07_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO08_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO09_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO10_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO11_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO12_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO13_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO14_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO15_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO16_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO17_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO18_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO19_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THO20_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>THS01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ULO01_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ULO02_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ULO03_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ULO04_count</b>: Number collected in twenty minute period (Field type: abundance)</li><li><b>ULO05_count</b>: Number collected in twenty minute period (Field type: abundance)</li></ul></li></ol><p><b>Date range: </b>2015-03-10 to 2015-04-11</p><p><b>Latitudinal extent: </b>4.6373 to 4.7714</p><p><b>Longitudinal extent: </b>116.9549 to 117.7021</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Arthropoda <br> -  -  -  Arachnida <br> -  -  -  -  Araneae <br> -  -  -  -  -  Linyphiidae <br> -  -  -  -  -  -  [LIN01] <br> -  -  -  -  -  -  [LIN02] <br> -  -  -  -  -  Uloboridae <br> -  -  -  -  -  -  [ULO01] <br> -  -  -  -  -  -  [ULO02] <br> -  -  -  -  -  -  [ULO03] <br> -  -  -  -  -  -  [ULO04] <br> -  -  -  -  -  -  [ULO05] <br> -  -  -  -  -  Pisauridae <br> -  -  -  -  -  -  [PIS01] <br> -  -  -  -  -  Hersiliidae <br> -  -  -  -  -  -  [HER01] <br> -  -  -  -  -  Corinnidae <br> -  -  -  -  -  -  [COR01] <br> -  -  -  -  -  -  [COR02] <br> -  -  -  -  -  -  [COR03] <br> -  -  -  -  -  -  [COR04] <br> -  -  -  -  -  -  [COR05] <br> -  -  -  -  -  Salticidae <br> -  -  -  -  -  -  [SAL01] <br> -  -  -  -  -  -  [SAL02] <br> -  -  -  -  -  -  [SAL03] <br> -  -  -  -  -  -  [SAL04] <br> -  -  -  -  -  -  [SAL05] <br> -  -  -  -  -  -  [SAL06] <br> -  -  -  -  -  -  [SAL07] <br> -  -  -  -  -  -  [SAL08] <br> -  -  -  -  -  -  [SAL09] <br> -  -  -  -  -  -  [SAL10] <br> -  -  -  -  -  -  [SAL11] <br> -  -  -  -  -  -  [SAL12] <br> -  -  -  -  -  -  [SAL13] <br> -  -  -  -  -  -  [SAL14] <br> -  -  -  -  -  -  [SAL15] <br> -  -  -  -  -  -  [SAL16] <br> -  -  -  -  -  -  [SAL17] <br> -  -  -  -  -  -  [SAL18] <br> -  -  -  -  -  -  [SAL19] <br> -  -  -  -  -  -  [SAL20] <br> -  -  -  -  -  -  [SAL21] <br> -  -  -  -  -  -  [SAL22] <br> -  -  -  -  -  -  [SAL23] <br> -  -  -  -  -  -  [SAL24] <br> -  -  -  -  -  -  [SAL25] <br> -  -  -  -  -  -  [SAL26] <br> -  -  -  -  -  -  [SAL27] <br> -  -  -  -  -  -  [SAL28] <br> -  -  -  -  -  -  [SAL29] <br> -  -  -  -  -  -  [SAL30] <br> -  -  -  -  -  -  [SAL31] <br> -  -  -  -  -  -  [SAL32] <br> -  -  -  -  -  -  [SAL33] <br> -  -  -  -  -  -  [SAL34] <br> -  -  -  -  -  -  [SAL35] <br> -  -  -  -  -  -  [SAL36] <br> -  -  -  -  -  -  [SAL37] <br> -  -  -  -  -  -  [SAL38] <br> -  -  -  -  -  -  [SAL39] <br> -  -  -  -  -  -  [SAL40] <br> -  -  -  -  -  -  [SAL41] <br> -  -  -  -  -  -  [SAL42] <br> -  -  -  -  -  -  [SAL43] <br> -  -  -  -  -  -  [SAL44] <br> -  -  -  -  -  -  [SAL45] <br> -  -  -  -  -  -  [SAL46] <br> -  -  -  -  -  -  [SAL47] <br> -  -  -  -  -  Dictynidae <br> -  -  -  -  -  -  [DIC01] <br> -  -  -  -  -  Tetragnathidae <br> -  -  -  -  -  -  [TET01] <br> -  -  -  -  -  -  [TET02] <br> -  -  -  -  -  -  [TET03] <br> -  -  -  -  -  -  [TET04] <br> -  -  -  -  -  -  [TET05] <br> -  -  -  -  -  -  [TET06] <br> -  -  -  -  -  Mimetidae <br> -  -  -  -  -  -  [MIM01] <br> -  -  -  -  -  -  [MIM02] <br> -  -  -  -  -  -  [MIM03] <br> -  -  -  -  -  Scytodidae <br> -  -  -  -  -  -  [SCY01] <br> -  -  -  -  -  Pholcidae <br> -  -  -  -  -  -  [PHO01] <br> -  -  -  -  -  -  [PHO02] <br> -  -  -  -  -  -  [PHO03] <br> -  -  -  -  -  -  [PHO04] <br> -  -  -  -  -  -  [PHO05] <br> -  -  -  -  -  -  [PHO06] <br> -  -  -  -  -  -  [PHO07] <br> -  -  -  -  -  -  [PHO08] <br> -  -  -  -  -  -  [PHO09] <br> -  -  -  -  -  -  [PHO11] <br> -  -  -  -  -  -  [PHO13] <br> -  -  -  -  -  Mysmenidae <br> -  -  -  -  -  -  [MYS01] <br> -  -  -  -  -  -  [MYS02] <br> -  -  -  -  -  -  [MYS03] <br> -  -  -  -  -  Araneidae <br> -  -  -  -  -  -  [ARA01] <br> -  -  -  -  -  -  [ARA02] <br> -  -  -  -  -  -  [ARA03] <br> -  -  -  -  -  -  [ARA04] <br> -  -  -  -  -  -  [ARA05] <br> -  -  -  -  -  -  [ARA06] <br> -  -  -  -  -  -  [ARA07] <br> -  -  -  -  -  -  [ARA08] <br> -  -  -  -  -  -  [ARA09] <br> -  -  -  -  -  -  [ARA10] <br> -  -  -  -  -  -  [ARA11] <br> -  -  -  -  -  -  [ARA12] <br> -  -  -  -  -  -  [ARA13] <br> -  -  -  -  -  -  [ARA14] <br> -  -  -  -  -  -  [ARA15] <br> -  -  -  -  -  -  [ARA16] <br> -  -  -  -  -  Cheiracanthiidae <br> -  -  -  -  -  -  [EUT01] <br> -  -  -  -  -  -  [EUT02] <br> -  -  -  -  -  -  [EUT03] <br> -  -  -  -  -  -  [EUT04] <br> -  -  -  -  -  -  [EUT05] <br> -  -  -  -  -  -  [EUT06] <br> -  -  -  -  -  Gnaphosidae <br> -  -  -  -  -  -  [GNA01] <br> -  -  -  -  -  Oxyopidae <br> -  -  -  -  -  -  [OXY01] <br> -  -  -  -  -  -  [OXY02] <br> -  -  -  -  -  -  [OXY03] <br> -  -  -  -  -  -  [OXY04] <br> -  -  -  -  -  -  [OXY05] <br> -  -  -  -  -  -  [OXY06] <br> -  -  -  -  -  Tetrablemmidae <br> -  -  -  -  -  -  [TEB01] <br> -  -  -  -  -  Theridiosomatidae <br> -  -  -  -  -  -  [THS01] <br> -  -  -  -  -  Oonopidae <br> -  -  -  -  -  -  [OON01] <br> -  -  -  -  -  -  [OON02] <br> -  -  -  -  -  -  [OON03] <br> -  -  -  -  -  -  [OON05] <br> -  -  -  -  -  -  [OON06] <br> -  -  -  -  -  -  [OON08] <br> -  -  -  -  -  -  [OON09] <br> -  -  -  -  -  -  [OON10] <br> -  -  -  -  -  -  [OON11] <br> -  -  -  -  -  Anapidae <br> -  -  -  -  -  -  [ANA01] <br> -  -  -  -  -  Thomisidae <br> -  -  -  -  -  -  [THO01] <br> -  -  -  -  -  -  [THO02] <br> -  -  -  -  -  -  [THO03] <br> -  -  -  -  -  -  [THO04] <br> -  -  -  -  -  -  [THO05] <br> -  -  -  -  -  -  [THO06] <br> -  -  -  -  -  -  [THO07] <br> -  -  -  -  -  -  [THO08] <br> -  -  -  -  -  -  [THO09] <br> -  -  -  -  -  -  [THO10] <br> -  -  -  -  -  -  [THO11] <br> -  -  -  -  -  -  [THO12] <br> -  -  -  -  -  -  [THO13] <br> -  -  -  -  -  -  [THO14] <br> -  -  -  -  -  -  [THO15] <br> -  -  -  -  -  -  [THO16] <br> -  -  -  -  -  -  [THO17] <br> -  -  -  -  -  -  [THO18] <br> -  -  -  -  -  -  [THO19] <br> -  -  -  -  -  -  [THO20] <br> -  -  -  -  -  Hahniidae <br> -  -  -  -  -  -  [HAH01] <br> -  -  -  -  -  -  [HAH02] <br> -  -  -  -  -  Theridiidae <br> -  -  -  -  -  -  [THD01] <br> -  -  -  -  -  -  [THD02] <br> -  -  -  -  -  -  [THD03] <br> -  -  -  -  -  -  [THD04] <br> -  -  -  -  -  -  [THD05] <br> -  -  -  -  -  -  [THD06] <br> -  -  -  -  -  -  [THD07] <br> -  -  -  -  -  -  [THD08] <br> -  -  -  -  -  -  [THD09] <br> -  -  -  -  -  -  [THD10] <br> -  -  -  -  -  -  [THD11] <br> -  -  -  -  -  -  [THD12] <br> -  -  -  -  -  -  [THD13] <br> -  -  -  -  -  -  [THD14] <br> -  -  -  -  -  -  [THD15] <br> -  -  -  -  -  -  [THD16] <br> -  -  -  -  -  -  [THD17] <br> -  -  -  -  -  -  [THD18] <br> -  -  -  -  -  -  [THD19] <br> -  -  -  -  -  -  [THD20] <br> -  -  -  -  -  -  [THD21] <br> -  -  -  -  -  -  [THD22] <br> -  -  -  -  -  -  [THD23] <br> -  -  -  -  -  -  [THD24] <br> -  -  -  -  -  -  [THD25] <br> -  -  -  -  -  -  [THD26] <br> -  -  -  -  -  -  [THD27] <br> -  -  -  -  -  -  [THD28] <br> -  -  -  -  -  -  [THD29] <br> -  -  -  -  -  -  [THD30] <br> -  -  -  -  -  -  [THD31] <br> -  -  -  -  -  -  [THD32] <br> -  -  -  -  -  -  [THD33] <br> -  -  -  -  -  -  [THD34] <br> -  -  -  -  -  -  [THD35] <br> -  -  -  -  -  -  [THD36] <br> -  -  -  -  -  -  [THD37] <br> -  -  -  -  -  -  [THD38] <br> -  -  -  -  -  -  [THD39] <br> -  -  -  -  -  -  [THD40] <br> -  -  -  -  -  -  [THD41] <br> -  -  -  -  -  -  [THD42] <br> -  -  -  -  -  -  [THD43] <br> -  -  -  -  -  -  [THD44] <br> -  -  -  -  -  -  [THD45] <br> -  -  -  -  -  -  [THD46] <br> -  -  -  -  -  -  [THD47] <br> -  -  -  -  -  -  [THD48] <br> -  -  -  -  -  -  [THD49] <br> -  -  -  -  -  -  [THD50] <br> -  -  -  -  -  -  [THD51] <br> -  -  -  -  -  -  [THD52] <br> -  -  -  -  -  -  [THD53] <br> -  -  -  -  -  -  [THD54] <br> -  -  -  -  -  -  [THD55] <br> -  -  -  -  -  -  [THD56] <br> -  -  -  -  -  -  [THD57] <br> -  -  -  -  -  -  [THD58] <br> -  -  -  -  -  -  [THD59] <br> -  -  -  -  -  -  [THD60] <br> -  -  -  -  -  -  [THD61] <br> -  -  -  -  -  -  [THD62] <br> -  -  -  -  -  -  [THD63] <br> -  -  -  -  -  -  [THD64] <br> -  -  -  -  -  -  [THD65] <br> -  -  -  -  -  -  [THD66] <br> -  -  -  -  -  -  [THD67] <br></div><p></p>
Published and new Geochronology, Thermochronology, Geodetic, and Earthquake Data from the Fairweather Transform Region
<p>Published and new Geochronology, Thermochronology, Geodetic, and Earthquake Data from the Fairweather Transform Region used in <strong>Benowitz, J., </strong>Lease, R., Hauessler, P., Pavlis, P., Mann, M., Fairweather Transform Orogenesis: 25 million years of Crustal-block vertical extrusion and double indenter tectonics since ca. 3 Ma., for <i>Tectonophysics</i>.</p>
Case study input data set for article "Stochastic planning of energy system transformation pathways under uncertain industry demands"
<p>The data set contains input data for the model EMPRISE of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. </p>
Figures. Exploring the Archived Web in a Highly Transformative Age. Proceedings. dir. S.Gebeil & J.-C. Peyssard
<p>Given recent global crises, the imperative to preserve and analyze online content has never been more vital to enhancing our comprehension of contemporary changes. This book, the outcome of an 5th international RESAW conference that convened experts from 50 disciplines across 17 countries in Marseille in June 2023, tackles the multifaceted challenges of web archiving. It underscores the dual roles of web archiving, as a cultural heritage and as essential source material for researchers delving into contemporary events and the evolution of digital culture. Through 20 chapters, it explores the development of web archiving and examines how technical, cultural, geopolitical, societal and environmental shifts impact its conception, study and dissemination. </p> <p> </p>
Digital Transformation and Its Relationship to the Job Performance of Employees at a Private University in Peru
<p><span>Private universities in Peru still need to implement digital transformation models to enhance the job performance of faculty and staff, achieving consistent improvement in the performance levels of university teachers by deploying technological and didactic tools for students. Therefore, the study aims to determine the relationship between digital transformation and the job performance of employees at a Private University. The research approach was quantitative, employing a non-experimental, longitudinal correlational design. The technique used was a survey, applied to a sample of 104 employees (school heads, faculty, and a director) from the university on a national level from a total population of 144. The findings highlight that organizational motivation to achieve high performance significantly affects job performance. This motivation creates pressure to achieve the desired outcomes. According to over 90% of the surveyed faculty, these demands foster a sense of urgency, though sometimes exceeding the capabilities of the employee. Therefore, creating an innovative culture across all hierarchical levels and identifying key technologies that add value to the learning flow can meet the needs of an increasingly demanding society.</span></p> <p><strong><span>Keywords: </span></strong><span>digital transformation, job performance, virtual education, digital tools, educational quality.</span></p>
Quantifying Progress: Metrics and Indicators for Measuring Digital Transformation Maturity in Organizations
<p><span>As organizations increasingly embark on digital transformation journeys, the need for effective metrics and indicators to measure progress and maturity becomes paramount. This paper investigates the development and application of metrics for quantifying digital transformation maturity in organizations. Through an extensive review of literature and examination of case studies, the paper identifies key dimensions and stages of digital maturity. It proposes a framework encompassing both quantitative and qualitative metrics that can be used to assess an organization's digital transformation journey. The paper explores challenges associated with defining meaningful metrics and offers insights into adapting measurement frameworks to diverse organizational contexts. By addressing this critical gap in the literature, the paper aims to provide practitioners, researchers, and decision-makers with a valuable resource for evaluating and benchmarking digital transformation progress, fostering a more nuanced understanding of the multifaceted nature of organizational digital maturity.</span></p>
Earthquake catalog at the Blanco Transform Fault Zone between 2012 and 2013
<p>The csv file provides an earthquake catalog derived from data of ocean-bottom seismometers operated between 2012 and 2013 at the Blanco Transform Fault Zone. The data file is in ASCII text format. The first row is a column header that describes the content of the catalog: Earthquake origin date, time, latitude, longitude, depth and local magnitude. Not all earthquakes have a local magnitude estimate due to data selection.</p>
Waning snowfields have transformed into hotspots of greening within the alpine zone
<p>Dataset of the paper</p> <p><strong><span>Waning snowfields have transformed into hotspots of greening within the alpine zone</span></strong></p> <p><span>Nature Climate Change - <span>NCLIM-24020412</span></span></p>
Raw data to "Quantum melting of long-range ordered quantum antiferromagnets investigated by momentum-space continuous similarity transformations"
<p>This collection of data is complementary to the publication "Quantum melting of long-range ordered quantum antiferromagnets investigated by momentum-space continuous similarity transformations", Dag-Björn Hering, Matthias R. Walther, Kai P. Schmidt, Götz S. Uhrig, arXiv:2405.13768 (https://arxiv.org/abs/2405.13768).</p> <p>It contains three data sets:</p> <ul> <li>raw_data: Contains the results and metadata of individual CST runs in a ".json" format</li> <li>processed_data: Contains processed raw data in a “.pkl” format, where derived quantities are directly accessible (sublattice magnetization, second derivative of the ground-state energy) </li> <li>plot_data: Contains the data points used in Figs 4,6,7,8,910,11,12 and 13 in a “.csv” format</li> </ul> <p>For details on the CST, the used error estimates and physical quantities we refer the the publication.</p> <p>For details on the format, we recommend the README.md files.</p>
PubChem and ChEMBL-series processed dataset used in Exhaustive local chemical space exploration using a transformer model
<p>PubChem and ChEMBL-series processed dataset used in <span>Exhaustive local chemical space exploration using </span><span>a transformer model</span></p>
EC-MS dataset of electrocatalytic transformations of butane on Pt
<p>This distribution provides the code and reference data for the manuscript of "Understanding the interplay between electrocatalytic C(sp3)‒C(sp3) fragmentation and oxygenation reactions".</p> <p>The code can be excecuted using Python version 3.8. </p> <p><strong>Data</strong></p> <p>The distribution includes an <code>.xlsx</code> file with reference mass spectra data and experimental data in <code>.tsv</code> format.</p> <p> </p> <p><strong>Usage</strong></p> <ol> <li>Ensure Python 3.8 and Jupyter Notebook are installed.</li> <li>Execute each cell in the notebook <code>example.ipynb</code>. A pop-up window will prompt you to upload your experimental mass spectra data; select your file accordingly. The cells are organized as: <ol> <li><code>Load Data</code>: This step loads the reference spectra data.</li> <li><code>Preprocess Data</code>: This step removes background signals and smooths the signal.</li> <li><code>Optimization</code>: This step uses constrained least squares optimization to reconstruct spectra and predict flux.</li> <li><code>Plot</code>: This step displays the spectra reconstruction and flux prediction.</li> <li><code>File Output</code>: This step saves the predicted flux and reconstructed spectra to files.</li> </ol> </li> </ol>
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.