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7,195 results for “SET”
Mutating Zta(N182) to S, Q, T, I, and V changes sequence specific DNA binding to four types of DNA (65k data set)
GEO Series GSE126588. synthetic construct; Mus musculus. 36 samples. Type: Other.
Human cells infected with Mucormycosis-causing strains from clinical settings
GEO Series GSE58634. Homo sapiens. 0 samples. Type: Expression profiling by high throughput sequencing; Third-party reanalysis.
Differential messenger RNA expression in Granulosa Cells from polycystic ovary syndrome with Normoandrogen and Hyperandrogen: Identification of gene sets through bioinformatic Filtering analysis
GEO Series GSE137684. Homo sapiens. 12 samples. Type: Expression profiling by array.
A single cell-based computational platform to identify chemical compounds targeting desired sets of transcription factors for cellular conversion [Single-cell RNA-seq]
GEO Series GSE162909. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Global expression analysis of differential expression of genes working under Soil Water Stress condition in different genotypes of rice in Vegetative and Grain-filling stage [set 2]
GEO Series GSE63718. Oryza sativa. 12 samples. Type: Expression profiling by array.
Impulse Responses (IRs) of selected reverb processors with different settings
<p>The issues of reverberation in acoustic architecture and in music production share the same theoretical core, nevertheless the first one has been scientifically researched in depth while the second one remains at technical and experimental crossroads. It could be stated that the ISO 3382 parameters were proposed in the “analog” era for room acoustics (actual halls), whereas the “digital” parameters introduced in software, artificial reverbs, are not standardized in any way but help to create desired reverberation for music or audio effects. The interest herein is to bind these two disciplines together and analyze some of the significant descriptors of room acoustics (RT, C50, C80, BR, ER, CT, STI) applied in plug-in reverberant processors to observe how the virtual space is affected by changing the values of different parameters. Psychoacoustic ranges of JND were applied to conclude their relevance (or rather, influence), and whether it is possible to perceive alteration. Five of the selected, popular and commercial VST reverbs are juxtaposed with five similar settings and the results of analysis might be useful for sound mixers and automated mixing algorithms.The issues of reverberation in acoustic architecture and in music production share the same theoretical core, nevertheless the first one has been scientifically researched in depth while the second one remains at technical and experimental crossroads. It could be stated that the ISO 3382 parameters were proposed in the “analog” era for room acoustics (actual halls), whereas the “digital” parameters introduced in software, artificial reverbs, are not standardized in any way but help to create desired reverberation for music or audio effects. The interest herein is to bind these two disciplines together and analyze some of the significant descriptors of room acoustics (RT, C50, C80, BR, ER, CT) applied in plug-in reverberant processors to observe how the virtual space is affected by changing the values of different parameters. Psychoacoustic ranges of JND were applied to conclude their relevance (or rather, influence), and whether it is possible to perceive alteration. Five of the selected, popular and commercial VST reverbs are juxtaposed with five similar settings and the results of analysis might be useful for sound mixers and automated mixing algorithms.</p>
MLCQProjects: Towards evolving data set of industry-relevant software projects
<p>Short: This data set contains three snapshots from an evolving software project data set and a script that generates those snapshots. Projects in snapshots are annotated with features used to assess industry-relevance - documentation links, installation means, support channels and whether the project delivers functionalities or samples.</p> <p>Context: Researchers involved in mining software repositories face a challenge that many of existing data sets reflect how software was developed in the past (typically many years ago), instead of in the present. Another challenge is that data sets based on open-source software projects include projects that are not necessarily industry-relevant.</p> <p>Aim: The aim of this paper is to address the aforementioned challenges and provide both: 1) a snapshot-based evolving data set of software projects (thus reflecting their present, as well as previous states), manually enriched with data considered important for industrial relevance assessment, and 2) a method of assessing industrial relevance of software projects.</p> <p>Method: We present a systematic method of selecting data sets of software projects for the purposes of mining software repositories of potentially industry-relevant projects and a semi-systematic method of assessing the industrial relevance of those projects.</p> <p>Data set: The data set contains three snapshots (spanning over 10 months) of popular Java projects from GitHub, manually enriched with industrial relevance and maintenance-related information. The presented acquisition method is sufficient to generate further snapshots and one should be able to assess the industrial relevance of the projects using the presented assessment method. Provided data set open directions of further research, e.g, 1) evaluation of code smells or defect prediction models on industry-relevant software projects prepared by independent authors and not used to build models, 2) analysis of some social aspects of open source projects (such as tooling used for providing support) and how they evolve in time.</p>
Introduction to curating and managing research data for re-use: natural gas infrastructure case study data set
<p>This data set underpins the report and data provided between 2004-2006 for registration in the restrictions application of Estonian Land Board.</p>
Bodhgayā, Bihār. String of four conch shells and four flowers in gold; each of the flowers set with a sapphire.
<p>Bodhgayā, Bihār. String of four conch shells and four flowers in gold; each of the flowers set with a sapphire. British Museum 1892,1103.13 presented by Alexander Cunningham.</p>
Data sets for Perspective: SARS-CoV-2's potential mechanism of regulating cellular responses through depletion of specific host miRNAs
<p>Using a bioinformatic analysis of human miRNA potential interactions with the SARS-CoV-2’s genome, we examined the potential miRNA target sites in 7 coronavirus genomes that include SARS-CoV-2, MERS-CoV, SARS-CoV, and 4 non-pathogenic coronaviruses. (<strong>Data Set 1</strong>).</p> <p><em>Our approach was to examine and compare 3 pathogenic and 4 non-pathogenic strains of HCoVs. The HCoVs' RNA genomes of pathogenic strains were SARS-CoV-2 (NC_045512.2), SARS-CoV (NC_004718.3), MERS-CoV (NC_019843.3). The non-pathogenic strains were HCoV-OC43 (KU131570.1), HCoV-229E (NC_002645.1), HCoV-HKU1 (KF686346.1), and HCoV-NL63 (NC_005831.2). These coronaviruses were tested against the set of 896 confident mature human miRNA sequences that were obtained from the miRBbase v2.21 using the RNA22 v2 microRNA target discovery tool web-server. In order to reduce the false discovery rate of the MTS predictions, the most strict parameters were applied to the default computation workflow using a specificity of 92% versus a sensitivity of 22%.</em></p> <p><em>In <strong>Data set 2</strong>, using the miRDIP database with only top 1% of the most probable targets considered, we analyzed the potential targets of miRNA that could be bound to either the pathogenic, the non-pathogenic or both groups of HCoVs. </em></p> <p>In <strong>Data set 3</strong>, using miRNAFold webserver we identified 10 pre-miRNA sequences in the <em>SARS-CoV-2</em> RNA sequence that could potentially enter the human RNAi pathway.</p> <p>The graphical summary of our working hypothesis is provided in the<strong> graphical abstract</strong>.</p> <p> </p> <p> </p>
Data set and results of regression analysis of gene variants in Prostate Cancer patients from Mexico-mestizo population.
<p>Genotyping assays were performed using TaqMan SNP Genotyping Assays probes C_27532228_20 and C_2362601_10 (Life Technologies, Carlsbad, CA, USA).</p> <p>Thermalcycler: StepOnePlus™ Real-Time PCR (Thermo Fisher Scientific, Waltham, MA, USA).</p> <p>Sample: genomic DNA from blood.</p> <p>Subjects: Prostate cancer patients and controls (healthy and benign prostatic hyperplasia). </p> <p>Results of Regression Anlysis for clinical features of gene SRD5A2 variants (rs9282858 and<strong> </strong>rs523349) in prostate cancer patients from Mexican-mestizo population.</p> <p>Starting with the full model, successive models are created, each one using one less regressor (or covariate) than the previous model. Each of the regressors currently in the model is removed to create a "trial" model excluding that regressor. The p-value of the current model (or full model) versus the trial model (or reduced model) is calculated, and the model with the smallest p-value is used as the next model. This method removes the least significant variable from the current model. If every p-value is smaller than the p-value cut off specified, the backward elimination method stops. The method also stops if all variables have been removed from the model, or if all variables left are included in the original reduced model. From the standpoint of further analysis, the final model becomes the "full model" for this set of potential regressors</p> <p>Ethics approval and consent to participate in the study: The protocol and informed consent were approved by the Ethics and Research Committee of the School of Medicine (Universidad Autonoma de Nuevo Leon), with registration number UR16-00007. In the data set, no data was included that compromises the confidentiality of the participating subjetcs.</p> <pre> </pre>
Data set from "Combining fiber Brillouin amplification with a repeater laser station for fiber-based optical frequency dissemination over 1400 km"
<p>The data set contains the data underlying the fiber link performance evaluation published in Koke et al 2019 New J. Phys. 21 123017 (https://doi.org/10.1088/1367-2630/ab5d95). The experimental setup and the methodology used is explained in this publication.</p> <p>The data is stored in the Matlab(R)-native file format. Although proprietary, import of data from this file format is supported by other numerical computing environments, too. An example script for importing into Python is included.</p> <p>The files 'data_export_campaign_*.mat' contain the timeseries data shown in Figures 2, 3 and 4. Each of these files include variables with the following meaning:<br> 'date_year', 'date_month', 'date_day', 'time_hour', 'time_minute', 'time_second': Timestamp of the data sample in UTC time<br> 'inloop1' and 'inloop2': $\Lambda_{1s}$ frequency offset from nominal value in Hz of the beat signal used for stabilizing the uplink<br> 'aom': $\Lambda_{1s}$ frequency offset from nominal value in Hz of the uplink servo acoustic-optical modulator's drive frequency<br> 'remote1' and 'remote2': $\Lambda_{1s}$ frequency offset from nominal value in Hz of the roundtrip beat signal used for out-of-loop characterization of the frequency transfer error<br> 'no_of_run_in_NJP_21_123017': A flag indicating the measurement runs discussed in the paper. Samples with the same non-zero integer values belong to the same measurement run. Samples marked with 0 did not enter the publication.<br> 'DataCollected_FBAPTBLocked_CavityLocked': Result of our current monitoring of the fulfillment of the prerequisites for fiber link performance evaluation; values of 1/True indicate that data logging was active, the transfer laser was locked to the signal of the ultra-stable cavity, and the lock of the FBA(PTB) pump laser was active.</p> <p>The file 'data_export_uplink_downlink_aom.mat' contains the data underlying the fiber phase noise correlation analysis in Fig. 5.<br> 'aom_uplink': $\Lambda_{1s}$ frequency offset from nominal value in Hz of the uplink servo acoustic-optical modulator's drive frequency<br> 'aom_downlink': $\Lambda_{1s}$ frequency offset from nominal value in Hz of the downlink servo acoustic-optical modulator's drive frequency</p> <p>Since publication of the paper, we discovered a slight inconsistency of the nominal frequencies used in our analysis. Hence, remote fractional frequency offsets published in Koke et al 2019 New J. Phys. 21 123017 have to be corrected by subtracting fractional frequency values of 4.2E-22 (campaigns 2015-06, 2016-03, 2018-03) and 3.3E-22 (campaign 2018-12). This inconsistency does not change the conclusions drawn in the paper as these corrections are well below the associated statistical uncertainties. The correct nominal frequency values have been employed for the uploaded data set.</p>
Eddy Covariance and Agronomical Meta Data Set from a Three Year Agroecosystem Site (RNG2)
<p>Dataset of soil, plant, forage, agronomical and eddy flux data from three years of commercial bioenergy feedstock production in Arkansas funded under the DOE ARPA-E SMARTFARM program in Phase I. Data set is provided to restricted users only. Data sets include three years of all 30 minute interval data. Full data sets will be published through Ameriflux. Some data, such as methane and nitrous oxide are embargoed.</p> <p>This work was partially funded through the U.S. Department of Energy, ARPA-E, under Cooperative Agreement DE-AR0001228 led by ARVA Intelligence Corp. (https://www.arvaintelligence.com) in collaboration with Lawrence Berkeley National Laboratory (LBNL).</p>
Data set for Dermal Biomimicry: Human dermal decellularized ECM Hydrogels as Fidelity-Rich Scaffolds for In Vitro Skin Models.
<p>Data set for the article <strong>Dermal Biomimicry: Human dermal decellularized ECM Hydrogels as Fidelity-Rich Scaffolds for In Vitro Skin Models.</strong></p>
Minimal training set for SAM
<p>Example dataset for training <a href="https://github.com/giacomo-janson/idpsam">SAM</a>, a deep learning model for protein conformation generation. The dataset consists in 10 Markov Chain Monte Carlo simulations for 4 peptide sequences. Simulations were carried out with the <a href="https://campari.sourceforge.net/V4/index.html">CAMPARI 4.0</a> package using the <a href="https://pubmed.ncbi.nlm.nih.gov/18506808/">ABSINTH</a> implicit solvent model. The trajectory files store only C alpha coordinates.</p>
Selective electroreduction of acetylene to 1,3-butadiene on iodide-induced Cuδ+–Cu0 sites (Raw Data Set)
<p>This depository contains the raw data set for the experiments performed for the publication 'Selective electroreduction of acetylene to 1,3-butadiene on iodide-induced Cuδ+–Cu0 sites', published in Nature Catalysis (<a title="https://ddec1-0-en-ctp.trendmicro.com:443/wis/clicktime/v1/query?url=https%3a%2f%2furldefense.com%2fv3%2f%5f%5fhttps%3a%2f%2fddec1%2d0%2den%2dctp.trendmicro.com%3a443%2fwis%2fclicktime%2fv1%2fquery%3furl%3dhttps%2a3a%2a2f%2a2fwww.nature.com%2a2farticles%2a2fs41929%2a2d024%2a2d01250%2a2d0%26umid%3d02b9092a%2d1af6%2d4cb5%2d849f%2d1e4e69c3362b%26auth%3d8d3ccd473d52f326e51c0f75cb32c9541898e5d5%2d49d0802b0b156dfd40e197957bc2396004387b42%5f%5f%3bJSUlJSUlJSU%21%21NLFGqXoFfo8MMQ%21riTc23aLZjIqs2CX4e3E48cfguXGobRD34gzGdqNClmD54WJ3g%5fR2kOcbDJ2ohtXGotgiSceoB28KNBBw%5ftR7GrMu3Q%24&umid=a0571516-9094-4fcf-aed3-d6b3ce8837a5&auth=8d3ccd473d52f326e51c0f75cb32c9541898e5d5-2320f313d5a4855ab4fc2c0c8713c98a5673ffa4" href="https://ddec1-0-en-ctp.trendmicro.com/wis/clicktime/v1/query?url=https%3a%2f%2furldefense.com%2fv3%2f%5f%5fhttps%3a%2f%2fddec1%2d0%2den%2dctp.trendmicro.com%3a443%2fwis%2fclicktime%2fv1%2fquery%3furl%3dhttps%2a3a%2a2f%2a2fwww.nature.com%2a2farticles%2a2fs41929%2a2d024%2a2d01250%2a2d0%26umid%3d02b9092a%2d1af6%2d4cb5%2d849f%2d1e4e69c3362b%26auth%3d8d3ccd473d52f326e51c0f75cb32c9541898e5d5%2d49d0802b0b156dfd40e197957bc2396004387b42%5f%5f%3bJSUlJSUlJSU%21%21NLFGqXoFfo8MMQ%21riTc23aLZjIqs2CX4e3E48cfguXGobRD34gzGdqNClmD54WJ3g%5fR2kOcbDJ2ohtXGotgiSceoB28KNBBw%5ftR7GrMu3Q%24&umid=a0571516-9094-4fcf-aed3-d6b3ce8837a5&auth=8d3ccd473d52f326e51c0f75cb32c9541898e5d5-2320f313d5a4855ab4fc2c0c8713c98a5673ffa4">https://www.nature.com/articles/s41929-024-01250-0</a>).</p>
Data set related to the article "An in vitro model for cardiac organoid production: the combined role of geometrical confinement and substrate stiffness"
<p>This record contains raw data related to the article "An in vitro model for cardiac organoid production: the combined role of geometrical confinement and substrate stiffness"</p>
Annotated image training set for the copepod Temora longicornis
<p>The zip files contain annotated training data on images captured in an experiment of Temora longicornis. the images are a subset of a much larger imaging dataset, that is part of a master thesis. Annotation was done using the in Computer Vision Annotation Tool. The copepod, faecal pellets and the well were manually labeled using rectangle. The images were captured using an automated imaging platform similar to the one by Heuschele et al 2019. The datasets were exported in the Coco1.0 dataset format. </p>
AgriLink - Data Set on Suppliers of farm advice in 7 European countries
<p>This Data Set is derived from the WP4 of the AgriLink project.</p> <p>It is part of task T4.4 of Work Package (WP) 4 of the H2020 AgriLink project. AgriLink [Agricultural Knowledge: linking farmers, advisors and researchers to boost innovation] aims at better understanding the role of advisory services in farmers’ decision making and at boosting their contribution to innovation for sustainable development of agriculture. WP4 addresses more specifically the governance of farm advisory services. The objective of the research presented in this report is to understand the institutions that influence how farm advisory services function on the ground, and to discuss implications for the support for sustainable development innovation.</p> <p><strong>Data were collected in seven European countries: the Czech Republic, France, Greece, Poland, Portugal, Spain and the UK.</strong></p> <p><strong>Data were collected for a diversity of types of innovation: Market, Technological, Process, and Social Innovation.</strong></p> <p>The Data set was built based on interviews with farm advisory suppliers.</p> <p>In total 170 farm advisory suppliers were interviewed.</p> <p>The table below provides the distribution of interviews according to countries.</p> <table> <tbody> <tr> <td> <p><strong>Country</strong></p> </td> <td> <p><strong>Market innovation (NCRO & RETRO)</strong></p> </td> <td> <p><strong>Technological innovation (TECH)</strong></p> </td> <td> <p><strong>Process innovation (BIOP & SOIL)</strong></p> </td> <td> <p><strong>Social innovation </strong><strong>(LABO & COMM)</strong></p> </td> <td> <p><strong>TOTAL</strong></p> </td> </tr> <tr> <td> <p><strong>Czech Republic</strong></p> </td> <td> <p> </p> </td> <td> <p>4</p> </td> <td> <p>16</p> </td> <td> <p> </p> </td> <td> <p><strong>20</strong></p> </td> </tr> <tr> <td> <p><strong>France</strong></p> </td> <td> <p>14</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>11</p> </td> <td> <p><strong>25</strong></p> </td> </tr> <tr> <td> <p><strong>Greece</strong></p> </td> <td> <p>11</p> </td> <td> <p> </p> </td> <td> <p>10</p> </td> <td> <p> </p> </td> <td> <p><strong>21</strong></p> </td> </tr> <tr> <td> <p><strong>Poland</strong></p> </td> <td> <p> </p> </td> <td> <p>6</p> </td> <td> <p> </p> </td> <td> <p>18</p> </td> <td> <p><strong>24</strong></p> </td> </tr> <tr> <td> <p><strong>Portugal</strong></p> </td> <td> <p> </p> </td> <td> <p>11</p> </td> <td> <p>20</p> </td> <td> <p> </p> </td> <td> <p><strong>31</strong></p> </td> </tr> <tr> <td> <p><strong>Spain</strong></p> </td> <td> <p>9</p> </td> <td> <p> </p> </td> <td> <p>29</p> </td> <td> <p> </p> </td> <td> <p><strong>38</strong></p> </td> </tr> <tr> <td> <p><strong>UK</strong></p> </td> <td> <p> </p> </td> <td> <p>7</p> </td> <td> <p> </p> </td> <td> <p>4</p> </td> <td> <p><strong>11</strong></p> </td> </tr> <tr> <td> <p><strong>TOTAL</strong></p> </td> <td> <p><strong>34</strong></p> </td> <td> <p><strong>28</strong></p> </td> <td> <p><strong>75</strong></p> </td> <td> <p><strong>33</strong></p> </td> <td> <p><strong>170</strong></p> </td> </tr> </tbody> </table> <p>The data has two aims.</p> <p><strong>First, to characterise farm advisory suppliers, in terms of (table below):</strong></p> <ul> <li><strong>what do they provide?</strong></li> <li><strong>Who is in control of the supplier?</strong></li> </ul> <table> <tbody> <tr> <td><strong>What do they provide</strong></td> <td><strong>Farmers</strong></td> <td><strong>NGO</strong></td> <td><strong>Private</strong></td> <td><strong>Public</strong></td> <td><strong>semi-public</strong></td> <td><strong>Total</strong></td> </tr> <tr> <td><strong>Advice and Bookkeeping</strong></td> <td>8</td> <td> </td> <td>4</td> <td>1</td> <td> </td> <td>13</td> </tr> <tr> <td><strong>Advice and Digital tech</strong></td> <td>1</td> <td> </td> <td>3</td> <td> </td> <td> </td> <td>4</td> </tr> <tr> <td><strong>Advice and Education</strong></td> <td>2</td> <td>4</td> <td>3</td> <td>5</td> <td> </td> <td>14</td> </tr> <tr> <td><strong>Advice and Health services</strong></td> <td> </td> <td> </td> <td>1</td> <td>2</td> <td> </td> <td>3</td> </tr> <tr> <td><strong>Advice and Inputs</strong></td> <td>1</td> <td> </td> <td>14</td> <td> </td> <td> </td> <td>15</td> </tr> <tr> <td><strong>Advice and Inputs and Outputs</strong></td> <td>15</td> <td> </td> <td>5</td> <td> </td> <td> </td> <td>20</td> </tr> <tr> <td><strong>Advice and Machinery</strong></td> <td> </td> <td> </td> <td>7</td> <td> </td> <td> </td> <td>7</td> </tr> <tr> <td><strong>Advice and Outputs</strong></td> <td>8</td> <td>1</td> <td>8</td> <td>2</td> <td> </td> <td>19</td> </tr> <tr> <td><strong>Advice and Research</strong></td> <td>2</td> <td>2</td> <td>5</td> <td>12</td> <td>1</td> <td>22</td> </tr> <tr> <td><strong>Only advice and training</strong></td> <td>16</td> <td> </td> <td>26</td> <td>10</td> <td>1</td> <td>53</td> </tr> <tr> <td><strong>Total</strong></td> <td>53</td> <td>7</td> <td>76</td> <td>32</td> <td>2</td> <td>170</td> </tr> </tbody> </table> <p><strong>Second, we have set a series of variables to characterise the services they provide. The main variables are:</strong></p> <ul> <li><strong>Number of advisors of the organisation</strong></li> </ul> <table> <tbody> <tr> <td><strong>Number of advisors</strong></td> <td><strong>Number of organisations in that group</strong></td> </tr> <tr> <td><strong>[0:5]</strong></td> <td>96</td> </tr> <tr> <td><strong>]10:50]</strong></td> <td>35</td> </tr> <tr> <td><strong>]5:10]</strong></td> <td>16</td> </tr> <tr> <td><strong>>50</strong></td> <td>19</td> </tr> <tr> <td><strong>n.a.</strong></td> <td>4</td> </tr> <tr> <td><strong>Total</strong></td> <td>170</td> </tr> </tbody> </table> <ul> <li><strong>Percentage of advisors in the staff of the organisation</strong></li> </ul> <table> <tbody> <tr> <td><strong>% of advisors</strong></td> <td><strong>Number of organisations</strong></td> </tr> <tr> <td><strong>[0:25[</strong></td> <td>43</td> </tr> <tr> <td><strong>[25:50[</strong></td> <td>17</td> </tr> <tr> <td><strong>[50:75[</strong></td> <td>30</td> </tr> <tr> <td><strong>[75:100]</strong></td> <td>70</td> </tr> <tr> <td><strong>n.a.</strong></td> <td>10</td> </tr> <tr> <td><strong>Total</strong></td> <td>170</td> </tr> </tbody> </table> <ul> <li><strong>Share of back-office activities in the staff of the organisation</strong></li> </ul> <table> <tbody> <tr> <td><strong>Share of back-office (%)</strong></td> <td><strong>Number of organisations</strong></td> </tr> <tr> <td><strong>[0:25[</strong></td> <td>41</td> </tr> <tr> <td><strong>[25:50[</strong></td> <td>26</td> </tr> <tr> <td><strong>[50:75[</strong></td> <td>66</td> </tr> <tr> <td><strong>[75:100]</strong></td> <td>24</td> </tr> <tr> <td><strong>n.a.</strong></td> <td>13</td> </tr> <tr> <td><strong>Total </strong></td> <td>170</td> </tr> </tbody> </table> <ul> <li><strong>Number of farmers client of the supplier per advisor</strong></li> </ul> <table> <tbody> <tr> <td><strong>Number of clients per organisation</strong></td> <td><strong>Number of organisation</strong></td> </tr> <tr> <td><strong>[0:25[</strong></td> <td>31</td> </tr> <tr> <td><strong>[25:75[</strong></td> <td>43</td> </tr> <tr> <td><strong>[50:75[</strong></td> <td>3</td> </tr> <tr> <td><strong>[75:175[</strong></td> <td>28</td> </tr> <tr> <td><strong>>175</strong></td> <td>36</td> </tr> <tr> <td><strong>n.a.</strong></td> <td>29</td> </tr> <tr> <td><strong>Total </strong></td> <td>170</td> </tr> </tbody> </table> <ul> <li><strong>Main advisory method</strong></li> </ul> <table> <tbody> <tr> <td><strong>Main Advisory method</strong></td> <td><strong>Number of organisations</strong></td> </tr> <tr> <td><strong>Group Advice</strong></td> <td>19</td> </tr> <tr> <td><strong>IT tool (app, software…)</strong></td> <td>2</td> </tr> <tr> <td><strong>n.a.</strong></td> <td>1</td> </tr> <tr> <td><strong>One to One Advice</strong></td> <td>129</td> </tr> <tr> <td><strong>Phone or web helpdesk</strong></td> <td>15</td> </tr> <tr> <td><strong>Publications</strong></td> <td>4</td> </tr> <tr> <td><strong>Total</strong></td> <td>170</td> </tr> </tbody> </table> <ul> <li>Main funding source</li> </ul> <table> <tbody> <tr> <td><strong>Main funding source</strong></td> <td><strong>Number of organisations</strong></td> </tr> <tr> <td><strong>EU funds</strong></td> <td>15</td> </tr> <tr> <td><strong>Fee-for-advice</strong></td> <td>46</td> </tr> <tr> <td><strong>Joint trade</strong></td> <td>42</td> </tr> <tr> <td><strong>Membership</strong></td> <td>11</td> </tr> <tr> <td><strong>Membership fee</strong></td> <td>6</td> </tr> <tr> <td><strong>n.a.</strong></td> <td>16</td> </tr> <tr> <td><strong>Public funding</strong></td> <td>3</td> </tr> <tr> <td><strong>Public funds</strong></td> <td>4</td> </tr> <tr> <td><strong>State budget</strong></td> <td>27</td> </tr> <tr> <td><strong>Total</strong></td> <td>170</td> </tr> </tbody> </table> <p>More detailed information about the variables collected can be found in the questionnaire that is available in the appendix of the deliverable D4.2 of AgriLink</p> <p> </p>
Data set of "Context-sensitive Requirements Search in Natural Language Specifications"
<p>We use this data set for the evaluation of our Context-sensitive Requirements Search approach. It contains three files and 15 searches.</p> <p> </p> <p><strong>Attribution</strong></p> <p>The foundation of this data set is PURE:<br> A. Ferrari, G. O. Spagnolo, and S. Gnesi. <em>PURE: a Dataset of Public Requirements Documents</em>. Version 1.0. Sept. 2018.<br> Available at: <a href="https://doi.org/10.5281/zenodo.1414117">https://doi.org/10.5281/zenodo.1414117</a></p> <p>The original data is licensed under <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a></p> <p> </p> <p><strong>Annotations</strong></p> <ul> <li>Each of the provided files contains annotations that we created with Label Studio for the corresponding file of the PURE data set.</li> <li>These annotations depict the search results of Plain Keyword Search (PKS) and the corresponding gold standard.</li> <li>The following is an example of an annotation in the .json file:</li> </ul> <pre><code class="language-json">{ "id": "UnaxUjB6oY", "meta": { "text": ["0"] }, "type": "labels", "value": { "end": "/text()[2]", "text": "speed", "start": "/text()[2]", "labels": ["speed: train speed profile"], "endOffset": 364, "startOffset": 359, "globalOffsets": { "end": 1403, "start": 1398 } }, "origin": "manual", "to_name": "text", "from_name": "ner" },</code></pre> <p> </p> <p><strong>Important Json attributes to consider</strong></p> <p><strong>Meta</strong></p> <p>We encode the relevance of each result in its metadata.</p> <p><em>Example:</em></p> <pre><code class="language-json">"meta": { "text": ["0"] },</code></pre> <ul> <li>1 = relevant for this search and found by PKS</li> <li>0 = irrelevant for this search but found by PKS</li> <li>-1 = relevant for this search and not found by PKS</li> </ul> <p> </p> <p><strong>Labels</strong></p> <p>This element is the search identifier we use in Label Studio. It corresponds to the table in the thesis that describes each search in detail.</p> <p><em>Example:</em></p> <pre><code class="language-json">"labels": ["speed: train speed profile"],</code></pre> <p> </p> <p><strong>GlobalOffsets</strong></p> <p>"globalOffsets" describes the position of a label in the text.</p> <p><em>Example:</em></p> <pre><code class="language-json">"globalOffsets": { "end": 1403, "start": 1398 }</code></pre> <p> </p>
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