cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/mg98ypgc8s.1
title: imbWBI: Classification of Business Entities on Multilingual Web: configuration optimization, auxiliary experimental reports and other resources

description: The DataSet contains auxiliary experimental reports, configuration files and other materiel, produced during research: "Classification of Business Entities on Multilingual Web using Natural Language Processing...". # Content of the folders: ## Configuration optimization process Experiments performed for system optimization ## General resources Graphic representations of Semantic Clouds, created by the system. Collection ACE Scripts, executed during research. Color renders are made with older cloud construction algorithm. ## Particular aspects of the system Results of the experiments performed to evaluate particular mechanisms of the system ## imbWBI_ITM_ProjectFiles Configuration files with sample specification and other resources required for results reproduction ------------------------------------------------------------------------------------------------------------------ In this research, we proposed and developed, an open source business stakeholder classification system, capable of multi-class single-label hard classification of business entities, according to the products they fabricate. The sole external data source is content retrieved from web site of the stakeholder, processed with array of Natural Language Processing, Web Data Mining and statistical techniques. The output is single label result, pointing to the particular industry of the stakeholder. Sample set contains: 5 categories, each having 10 manufacturing companies (web sites). Specific challenges addressed: - multilingual web content - limited availability of domain-specific training data-sets - heterogeneous linguistic resources of variable quality - absence of production ready and publicly available general semantic lexicons, like WordNet Problems solved in this research: - construction of semantic cloud (non-hierarchical lexicon of semantically related terms) from limited amount of web content - adaptation of similarity computation schema, based on Semantic Similarity Retrieval Model - development of efficient and effective Feature Vector Extraction mechanism, used to reduce number of dimensions in Feature Vector to the number of categories (5) - evaluation of wide range of classification algorithms and configuration parameters: kNN, NaiveBayes, Multiclass SVM and Neural Networks. (17 classifier models are evaluated in every experiment) All software tools (application and the libraries), developed during this research, are published under GNU GPL3 licence, thus available for other researchers and professionals. ---- Goran Grubić Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia goran.grubic@koplas.co.rs, +381 62 27 27 55
DOI: 10.17632/msnb8c6xbf.1
title: Hidden Structural Modules in a Cooperative RNA Folding Transition. Gracia et al

description: Quantitative single nucleotide resolution RNA footprinting methods probe structural accessibility of the backbone and base-pairing face at each nucleotide in the RNA structure, i.e. high reactivity indicates unpaired/flexible positions and low reactivity base-paired/rigid positions. By steadily increasing the concentration of free Mg2+ in solution, we induce RNA folding and observe the structural transitions at different levels of Mg2+. We quantify the Mg2+-dependent changes in reactivity by fitting the observed transitions to a sigmoidal binding equation (hill equation). We extract the transition midpoint parameter from the fit allowing for determination of the Mg2+ requirement for folding at a given nucleotide ([Mg2+]1/2, i.e. the concentration required to reach half maximal saturation). The conclusion from the data for WT P5abc was that there are multiple Mg2+-dependent transitions involving structural modules. To test for cooperativity between two of these modules, P5c and the metal core, we performed footprinting experiments using several mutants that either blocked the transition in P5c (U167C or U167C/U177C) or blocked folding of the metal core (A186U or ArichU). A lack of signal change at the mutated region (for example, U168 for mutant U167C) indicates that the module of interest is blocked by the mutation. We observed Mg2+-dependent changes in the unmutated region similar to WT, but the Mg2+ was greater than WT, consistent with cooperativity between the structural modules. We designed mutations that preformed the P5c module (Nat+3) and found that the overall Mg2+ requirement for folding the structure was significantly reduced compared to WT (e.g. nucleotides U135, A139, G163, G164, A186). We also partially restored folding of the blocked mutants by inserting restoration nucleotides that replace the blocked region (U167C_U177C_G_GUins). This restoration mutant decreased the Mg2+ requirement and restored signals associated with folding to WT levels compared with the U167C blocking mutant (e.g. nucleotides U135, A139, G163, GinsG164, GinsG176, A186). We utilized mutations that block the long-range interactions that form between the folded P5c and metal core modules (U168C) and observed that folding of the modules was not perturbed compared to WT (e.g. U135, A139, G163, A186). At concentrations above 10 mM Mg2+, we observed that some positions were perturbed for folding compared to WT (e.g. A184 SHAPE remains moderately reactive in U168C at high Mg2+ concentrations). We used mutations that preform the P5c module (C165A_G175U) to observe the formation of long range interactions between the folded modules. We found that signals associated with the P5c packing interaction (e.g. A173 DMS) were destabilized when the metal core module was blocked (C165A_G175U_A186U) compared to WT, consistent with cooperativity between the structural modules during formation of long-range interactions.
DOI: 10.5281/zenodo.1182964
title: Wp5 Input For D5.2

description: Continuing the work of D5.1, deliverable D5.2 describes how the interactive Technology Radar is used to describe future technology developments or trends that will impact how makers will create, communicate, organize and might even do business. This dataset represents the input data from the online spreadsheet used for D5.2.

See also: http://make-it.io/open-data-api/
DOI: 10.5281/zenodo.1182965
title: Wp5 Input For D5.2

description: Continuing the work of D5.1, deliverable D5.2 describes how the interactive Technology Radar is used to describe future technology developments or trends that will impact how makers will create, communicate, organize and might even do business. This dataset represents the input data from the online spreadsheet used for D5.2.

See also: http://make-it.io/open-data-api/
DOI: 10.5281/zenodo.1182968
title: Wp5 Input For Techradar-D5.3

description: Continuing the work of D5.1 and D5.2, deliverable D5.3 describes how we are using the gained insights into the <strong>Maker movement</strong> and how this project serves information back into the maker community in the shape of practical tools. This dataset represents the data available within the version of the Technology Radar developed with a new layout and features for D5.3, so makers can now access the data produced in previous deliverables – previously used for research only.

See also: http://make-it.io/open-data-api/
DOI: 10.5281/zenodo.1182969
title: Wp5 Input For Techradar-D5.3

description: Continuing the work of D5.1 and D5.2, deliverable D5.3 describes how we are using the gained insights into the <strong>Maker movement</strong> and how this project serves information back into the maker community in the shape of practical tools. This dataset represents the data available within the version of the Technology Radar developed with a new layout and features for D5.3, so makers can now access the data produced in previous deliverables – previously used for research only.

See also: http://make-it.io/open-data-api/
DOI: 10.5281/zenodo.1183357
title: Luminoso Input Data for SemEval-2018 Task 10: "Capturing Discriminative Attributes"

description: This is the data required to run Luminoso's entry to the SemEval-2018 task on Capturing Discriminative Attributes.

This data includes:


	A recently-computed version of the ConceptNet Numberbatch word embeddings
	The output of an implementation of Semantic Matching Energy over ConceptNet
	A SQLite database containing the lead section of all articles on the English Wikipedia on 2017-12-20
	The text file that that database is constructed from
	A SQLite database of words that co-occur in Google Books 2-grams
	The text file containing total counts of 2-grams in the Google Books data, which that database is constructed from


For more information, see the paper "Luminoso at SemEval-2018 Task 10: Distinguishing Attributes Using Text Corpora and Relational Knowledge", by Robyn Speer and Joanna Lowry-Duda, to appear in the proceedings of the SemEval workshop at NAACL 2018.
DOI: 10.5281/zenodo.1183358
title: Luminoso Input Data for SemEval-2018 Task 10: "Capturing Discriminative Attributes"

description: This is the data required to run Luminoso's entry to the SemEval-2018 task on Capturing Discriminative Attributes.

This data includes:


	A recently-computed version of the ConceptNet Numberbatch word embeddings
	The output of an implementation of Semantic Matching Energy over ConceptNet
	A SQLite database containing the lead section of all articles on the English Wikipedia on 2017-12-20
	The text file that that database is constructed from
	A SQLite database of words that co-occur in Google Books 2-grams
	The text file containing total counts of 2-grams in the Google Books data, which that database is constructed from


For more information, see the paper "Luminoso at SemEval-2018 Task 10: Distinguishing Attributes Using Text Corpora and Relational Knowledge", by Robyn Speer and Joanna Lowry-Duda, to appear in the proceedings of the SemEval workshop at NAACL 2018.
DOI: 10.6073/pasta/3c5cf736143efef60ec1e770d5d09b85
title: Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Phenology of Dominant Plant Species II - Berry Production 2013-2017

description: This drying and warming experiment addresses the following questions: 1) Does ecosystem drying, warming and permafrost thaw cause a net release or uptake of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss? 3) How do drying and warmign affect plant communities and ecosystem properties? We are answering these questions using a combined warming and drying experiment (DryPEHR), which is situated with the Carbon in Permafrost Experimental Heating Research (CiPEHR) project and located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. Warming treatment here refers to growing season air temperature warming (~1C) using open top chambers (OTC) combined with soil 'warming' using snow fences during the snow covered months. Drying is achieve using an automated pumping system that lowers the water table in the dry plots. Soil warming began in 2008; OTCs and drying in 2011.  In this dataset we specifically ask, how does warming and drying of soil and air impact the phenology of dominant plant species? Phenological data was collected to determine the timing of first bud break, onset and completion of senescence, and reproductive effort (flower and berry production).
DOI: 10.6073/pasta/86c0746b25229a9be10ed3c909b7b1ca
title: Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Phenology of Dominant Plant Species I - Bud burst and Senescence 2013-2017

description: This drying and warming experiment addresses the following questions: 1) Does ecosystem drying, warming and permafrost thaw cause a net release or uptake of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss? 3) How do drying and warmign affect plant communities and ecosystem properties? We are answering these questions using a combined warming and drying experiment (DryPEHR), which is situated with the Carbon in Permafrost Experimental Heating Research (CiPEHR) project and located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. Warming treatment here refers to growing season air temperature warming (~1C) using open top chambers (OTC) combined with soil 'warming' using snow fences during the snow covered months. Drying is achieve using an automated pumping system that lowers the water table in the dry plots. Soil warming began in 2008; OTCs and drying in 2011.  In this dataset we specifically ask, how does warming and drying of soil and air impact the phenology of dominant plant species? Phenological data was collected to determine the timing of first bud break, onset and completion of senescence, and reproductive effort (flower and berry production).
DOI: 10.5517/ccdc.csd.cc1z8dtg
title: CCDC 1825336: Experimental Crystal Structure Determination

description: Related Article: Brian W. Skelton, Kristina T. Constantopoulos|2018|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.17632/58phnkr3tj.1
title: Data from: Geographical and socioeconomic determinants of species discovery trends in a biodiversity hotspot

description: These data correspond to information on average description year of anuran, lizard, and snake species in multiple localities in the Atlantic Forest biodiversity hotspot, as well as data on geographic and socioeconomic variables that potentially explain species discovery trends. More specifically, we provided data on 376 species assemblages of anurans, 150 of lizards, and 235 of snakes. For each species assemblage, we present information on longitude, latitude, number of sampling years, number of survey methods applied, taxonomic group (anuran, lizard, or snake), species richness, average discovery trends (two metrics), and others 10 predictors related to human occupation, economic development, on-ground accessibility, biodiversity appeal (i.e. interest of first researching preserved areas), and expertise availability. The data format are csv files and R-scripts.
DOI: 10.17632/bswsr3gpy2.11
title: 3.5d-DEMs optimization-Zagros Ranges (enlarged study area)

description: Multi-dimensional DEM [ALOS(average, median), SRTM, ASTER) ] of SE Zagros Ranges (enlarged study area) rows: 2499, columns: 2799
DOI: 10.17632/fdds7m38wb.1
title: Nucleotide sequences of mandariviruses using degenerate primers

description: Nucleotide sequences of mandariviruses
DOI: 10.17632/n3sksvg2t2.1
title: Smart cities in Poland indexes

description: Indicators for measuring the potential of urban smartness based on a set of variables in Poland. Data collected mainly from Central Statistical Office of Poland, www.stat.gov.pl
DOI: 10.17632/n5m2sgrnpc.1
title: Dataset -- Mid-infrared spectroscopy and microscopy of subcellular structures in eukaryotic cells with atomic force microscopy – infrared spectroscopy

description: Dataset for the following publication L. Quaroni et al. Mid-infrared spectroscopy and microscopy of subcellular structures in eukaryotic cells with atomic force microscopy – infrared spectroscopy. RSC Advances (2018) 8, 5, 2786 - 2794.
DOI: 10.15473/1422208
title: Current Energy Harnessing using Synergistic Kinematics of Schools of Fish-Shaped Bodies: Marine Hydrodynamics Laboratory Tank Testing Data

description: The objectives of the proposed work pertain to building a high power-density and high efficiency device to harness MHK energy by mimicking fish-school kinematics. Vortex Hydro Energy is collaborating with a concept formed and undergone preliminary testing at the University of Michigan to complete this task. This submission contains data from the Marine Hydrodynamics Laboratory tank testing for 1, 2, and 3 cylinders. Tests were run in a 10,000 gallon recirculating tank. Cylinders have a diameter of 0.0889 m and 0.895m long. See "Read Me" for file format explanation and additional details.
DOI: 10.15473/1422209
title: Underwater Videos of the Oscylator-4 Installation and Operation from 2016

description: This data set contains video recorded from the under water camera on the Oscylator-4. The device was installed in the St. Clair River in Port Huron MI in 2016.
DOI: 10.15473/1422210
title: Admiralty Inlet Advanced Turbulence Measurements: Final Data and Code Archive

description: Data and code that is not already in a public location that is used in Kilcher, Thomson, Harding, and Nylund (2017) "Turbulence Measurements from Compliant Moorings - Part II: Motion Correction" doi: 10.1175/JTECH-D-16-0213.1. The links point to Python source code used in the publication. All other files are source data used in the publication.
DOI: 10.6073/pasta/267d698dbbc94fc30fca281e26af0c46
title: GIS Shapefile - Inventory of Historic Properties, Baltimore County

description: Inventory of Historic Properties for Baltimore County.  The Maryland Inventory of Historic Properties vector layers are depictions of the approximate locations of historic structures, monuments, districts, and other properties that are listed on the Maryland Inventory of Historic Properties.  No attribute information is available for this dataset.
      
      
          This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase.
      
      
          The geodatabase is available online and is considerably large.  Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive.
      
      
          The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders.
      
      
          Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.
DOI: 10.6073/pasta/8a18d1d75914af9b2d1336ba11dd1073
title: GIS Shapefile - Inventory of Historic Properties, Anne Arundel County

description: Inventory of Historic Properties for Anne Arundel County.  The Maryland Inventory of Historic Properties vector layers are depictions of the approximate locations of historic structures, monuments, districts, and other properties that are listed on the Maryland Inventory of Historic Properties.  No attribute information is available for this dataset.
      
      
          This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase.
      
      
          The geodatabase is available online and is considerably large.  Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive.
      
      
          The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders.
      
      
          Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.
DOI: 10.6073/pasta/bdd784c1d175ff6c280829e2e1481df2
title: GIS Shapefile - Inventory of Historic Properties, Carroll County

description: Inventory of Historic Properties for Carroll County.  The Maryland Inventory of Historic Properties vector layers are depictions of the approximate locations of historic structures, monuments, districts, and other properties that are listed on the Maryland Inventory of Historic Properties.  No attribute information is available for this dataset.
      
      
          This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase.
      
      
          The geodatabase is available online and is considerably large.  Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive.
      
      
          The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders.
      
      
          Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.
DOI: 10.6073/pasta/e9993a492b28f9293362a29160696503
title: GIS Shapefile - Main Study Area Boundary - BES Main Study Area

description: Boundary for the BES Metropolitan Study Area (MSA) derived from year 2000 GDT census data. This is the universal MSA boundary for all BES research.  The MSA consists of the following 5 counties: Baltimore City, Baltimore County, Anne Arrundel, Carroll, Harford, and Howard.
      
      
          This is part of a collection of  Baltimore Ecosystem Study metadata records that point to a geodatabase.
      
      
          The geodatabase itself is available online at beslter.org or lternet.edu.  It is considerably large.  Upon request, it can be shipped to you on media, such as a flash drive.
      
      
          The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders.
      
      
          Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.
DOI: 10.6073/pasta/eea60ae4150ba2c5144237d5c9542fdc
title: GIS Shapefile - Baltimore City Limits (not coincident with main study area boundary)

description: Baltimore City boundary limits. This is the "official" city boundary used by many Baltimore agencies. It DOES NOT agree with the universal MSA boundaries dervied from GDT census data.
      
      
          This is part of a collection of Baltimore Ecosystem Study metadata records that point to a geodatabase.
      
      
          The geodatabase itself is available online at beslter.org or lternet.edu.  It is considerably large.  Upon request, it can be shipped to you on media, such as a flash drive.
      
      
          The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders.
      
      
          Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.