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802 results for “films”
Time perception in suspenseful films
<p>Dataset of an experimental study in which participants (N = 50) watched short video clips from horror films. Participants were asked to estimate perceived suspense levels and to do a time estimation task. </p>
Using thin films of phase-change material for active tuning of terahertz waves scattering on dielectric cylinders
<p>The uploaded files contain the data generated by MATLAB and used to plot a part of the figures, and a sample code.</p> <p>Research supported by Narodowe Centrum Nauki, project no UMO-2020/39/I/ST3/02413.</p>
Investigating the ageing process of polymer modified bitumen using a modified Thin-Film Oven Test in the aspect of recycling purpose within Weave-UNISONO 2021 project, NCN project No 2021/03/Y/ST8/00079
<div><strong>Summary:</strong></div> <div>One polymer-modified bitumen PMB 25/55-60 was tested in two stages: original and after the modified Thin-Film Oven Test (TFOT). The time ranges from 1h-5h, and temperatures from 120°C-200°C were used. The Fourier-Transform Infrared (FTIR) Spectroscopy and Dynamic Shear Rheometer (DSR) with parallel plates were conducted. Test temperatures range from 30–70°C for a 25 mm diameter plate and 0–30°C for an 8 mm plate with 10°C intervals and angular frequency range of 0.1, 1.0, and 10 Hz.</div> <div> </div> <div> </div> <div><strong>The dataset includes:</strong></div> <div>Basic characteristics of bituminous binder (R&B Temperatur, Penetration), CSV raw data:</div> <div> <ul> <li>01 - SP Pen.csv</li> </ul> </div> <div> </div> <div>Dynamic shear rheometer (Temperatures 0-70 °C, Angular Frequency 0.1Hz, 1.0Hz, 10 Hz, Complex Shear Modulus, Phase Angle):</div> <ul> <li>02.1 - DSR Rheology_Unaged.csv</li> <li>02.2 - DSR Rheology_2h_140C.csv</li> <li>02.3 - DSR Rheology_2h_200C.csv</li> <li>02.4 - DSR Rheology_5h_140C.csv</li> <li>02.5 - DSR Rheology_5h_200C.csv</li> </ul> <div> </div> <div>FTIR - Fourier-Transform Infrared Spectroscopy </div> <div> <ul> <li>OPUS Spectroscopy files.zip</li> </ul> </div> <div> </div> <div>--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---</div> <div>to open the OPUS files, please go to the © Bruker webpage and download the free OPUS Viewer.</div> <div>https://www.bruker.com/en/products-and-solutions/infrared-and-raman/opus-spectroscopy-software/downloads.html</div> <div>--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---</div> <div> </div>
Pushing the Study of Point Defects in Thin Film Ferrites to Low Temperatures Using In Situ Ellipsometry
<p>Dataset for article "Pushing the Study of Point Defects in Thin Film Ferrites to Low Temperatures Using In Situ Ellipsometry" published in <em>Adv. Mater. Interfaces</em> 2021, <strong>8</strong>, 2001881. </p> <p>The data includes:</p> <ul> <li>XRD data of La<sub>1-x</sub>Sr<sub>x</sub>FeO<sub>3</sub> thin films</li> <li>Optical conductivity of LSF films as a function of oxygen partial pressure and Sr content</li> <li>Concentration of electronic holes in LSF thin films as a function of oxygen partial pressure and temperature</li> <li>Defect chemistry models employed for describing the concentration of point defects in LSF thin films.</li> <li>Equilibrium constants for oxygen incorporation reactions in LSF thin films at different temperatures</li> <li>Ellipsometry raw data of LSF50 thin film as a function of equivalent oxygen pressure </li> <li>Electrochemical impedance spectra of the LSF50 thin film at 400 ºC</li> </ul>
Ultrathin epitaxial Bi film growth on 2D HfTe2 template (data)
<p>This dataset contains the raw data files connected to the figures included in the paper "<em>Ultrathin epitaxial Bi film growth on 2D HfTe<sub>2</sub> template</em>" by E. Xenogiannopoulou et al., 2022 <em>Nanotechnology</em> <strong>33</strong> 015701; <a href="https://doi.org/10.1063/5.0038799">https://doi.org/10.1088/1361-6528/ac2d08</a></p> <p>An Open Access version of the paper can be found here: <a href="https://zenodo.org/record/4562057#.YaDC4NBBxPY">https://zenodo.org/record/5720132#.YaDKWNBBxPb</a></p>
Historical Film Shot Dataset V1 (HistShotDS V1)
<p><em>Paper title: </em></p> <p><strong>HistShot: A Shot Type Dataset based on Historical Documentation during WWII</strong></p> <p><em>Conference title: </em></p> <p>International Conference on Pattern Recognition Applications and Methods (ICPRAM 2022)</p> <p> </p> <p><em>Description:</em></p> <p>Automated shot type classification plays a significant role in film preservation and indexing of film datasets. In this paper a historical shot type dataset (HistShot) is presented, where the frames have been extracted from original historical documentary films. A center frame of each shot has been chosen for the dataset and is annotated according to the following shot types: Close-Up (CU), Medium-Shot (MS), Long-Shot (LS), Extreme-Long-Shot (ELS), Intertitle (I), and Not Available/None (NA). The validity to choose the center frame is shown by a user study. Additionally, standard CNN-based methods (ResNet50, VGG16) have been applied to provide a baseline for the HistShot dataset.</p> <p> </p> <p><em>References: </em></p> <p>Github Repository: <a href="https://github.com/dahe-cvl/ICPRAM2022_histshotV1">https://github.com/dahe-cvl/ICPRAM2022_histshotV1</a></p> <p>VHH-MMSI: <a href="https://vhh-mmsi.eu/">https://vhh-mmsi.eu/</a></p> <p>VHH-project Page: <a href="https://www.vhh-project.eu/">https://www.vhh-project.eu/</a></p> <p> </p> <p> </p> <p> </p> <p> </p>
Film: Sojatoasten für die Legehennen-Fütterung
<p>Die Sojabohne ist eine hervorragende Eiweissquelle für den menschlichen Verzehr und für die tierische Ernährung. Sie hat aber auch Verdauungshemmstoffe, die mittels Hitzebehandlung deaktiviert werden müssen.</p> <p>Um eine Hitzebehandlung von Sojabohnen durchzuführen, gibt es verschiedene Verfahren. Eines davon ist das Toasten. Üblicherweise ist dieser Vorgang ausgelagert und erfolgt nicht auf dem bäuerlichen Betrieb. Das Video zeigt, wie die Verarbeitung von Soja auf dem Betrieb von Johannes Edhofer in Österreich abläuft. Er baut selbst Soja an und verfüttert dieses nach einer Aufbereitung auf seinem Betrieb an seine Legehennen.</p> <p> </p> <p>The soybean is an excellent source of protein for human consumption and for animal nutrition. However, it also has digestive inhibitors that need to be deactivated by heat treatment.</p> <p>There are several methods to perform heat treatment on soybeans. One of them is toasting. Usually this process is outsourced and is not done on the farm. The video shows how soy is processed on Johannes Edhofer's farm in Austria. He grows soya himself and feeds it to his laying hens after processing it on his farm.</p>
HISTORIAN: a large-scale HISTORIcal film dataset with cinematographic ANnotation
<p>Developing automated tools for sustainable film preservation of extensive historical film collections assumes an understanding of fundamental cinematographic settings. In order to be able to investigate new approaches to detect and classify cinematographic settings, this paper proposes a novel large-scale historical film dataset with cinematographic annotations (HISTORIAN), i.e., shot boundaries, shot types, camera movements. The dataset consists of 98 digitized original analog film reels related to the Second World War and 10593 film shots manually annotated by human film experts. Moreover, annotations for overscan areas such as sprocket holes are included. A baseline film analysis pipeline is introduced and evaluated. To the best of our knowledge, HISTORIAN is the first dataset that covers the challenges and characteristics of historical film documentaries and provides novel possibilities for exploring automatic film analysis tools.</p> <p>This repository presents a tiny set including a few examples for demonstration.</p> <p>A link to the Github repository (including helper scripts and readme) can be found <a href="https://github.com/dahe-cvl/historian_dataset">here</a></p> <p> </p> <p> </p> <p> </p> <p> </p>
Data presented in the article entitled "Thermoelectric Inks and Power Factor Tunability in Hybrid Films through All Solution Process"
<p>Summary of the data plots presented in the article entitled "Thermoelectric Inks and Power Factor Tunability in Hybrid Films through All Solution Process".</p>
Raw data for the plots in the article entitled "Thermoelectric Inks and Power Factor Tunability in Hybrid Films through All Solution Process"
<p>Raw data for the plots in the article entitled "Thermoelectric Inks and Power Factor Tunability in Hybrid Films through All Solution Process"</p> <p>https://doi.org/10.1021/acsami.1c24392 </p> <p>ACS Appl. Mater. Interfaces 2022, 14, 19295−19303</p>
Tantalum thin films EUVR data
<p>S-polarized Angle-Dependent Reflectance (ADR) data measured from two Tantalum thin films deposited on Silicon substrates using monochromatized synchrotron radiation. The data was collected at the soft X-ray radiometry beamline (SX700) in the radiometry laboratory of the Physikalisch-Technische Bundesanstalt (PTB), in the electron storage facility BESSY II of Helmholtz Centre for Materials and Energy (HZB). The nominal thicknesses for the two thin films were 30.0 nm and 50.0 nm, hence, the .txt data files of the thin films samples are referred to as Sample-30 and Sample-50.</p> <p>The ADR data was collected in the Extreme Ultraviolet (EUV) range. For Sample-30 and Sample-50 the ADR was measured in the spectral ranges 5.0 nm – 24.0 nm and 10.0 nm – 20.0 nm, respectively. The EUVR of Sample-30 correspond to the angular range 3.0° – 85.5°. The EUVR of Sample-50 correspond to the angular range 4.5° – 87.0°.</p> <p>The .txt files contain post-processed reflectance values each with its calculated absolute uncertainty. </p>
Badoh-Pathari Saptamatrka Inscription Film Photo
<p>This is a scan from a negative film photograph taken in 2002 by Anne Casile and is published here with her permission. The picture shows the extremely weathered inscription accompanying the Saptamātṛkā panel on Gyānnāth Hill in Badoh-Pathari (Vidisha District, Madhya Pradesh, India). The inscription was reported by M. B. Garde (Annual Report of the Archaeological Department, Gwalior State for Samvat 1980, Year 1923-24, page 12) who mentioned that the name of viṣayeśvara Mahārāja Jayatsena could be read in it along with a partial date, the 13th of a bright half-month. The area has recently been thoroughly surveyed by Anne Casile, the survey reported in 'Changing Religious Landscapes in Gupta Times: Archaeological Evidence from the Area of Baḍoh-Paṭhāri in Central India', South Asian Studies 30 (2014), 245-268.</p> <p>Dániel Balogh is currently working on this inscription and planning to present the findings at the World Sanskrit Conference in 2018. Zenodo users who study this image are requested to share their insights with him, and not to publish a reading of the inscription before Balogh's publication of his reading.</p>
ACTIV-ES: a comparable Spanish corpus comprised of film dialogue from Argentine, Mexican and Spanish productions
<p><strong>DESCRIPTION</strong>: ACTIV-ES is a comparable Spanish corpus comprised of film dialogue from Argentine, Mexican and Spanish productions. Titles for each of these three countries were seeded from the Internet Movie Database, subtitle data for the hearing impaired was provided by Opensubtitles.org and was post-processed to correct/remove subtitle, OCR and diacritic artifacts and annotated for part-of-speech.</p> <p>The data is available in two main formats: 1) running text for each document and 2) 1:5 gram aggregate files. Each format includes a plain text and part-of-speech annotated version. Document names reflect the language code, country, year, title, type, genre (first genre listed in the IMDb), and IMDb ID.</p> <p>For more information about the development and evaluation of these resources and to cite this work refer to:</p> <p>Francom, J., Hulden, M. and Ussishkin, A.. (2014) ACTIV-ES: a comparable, cross-dialect corpus of 'everyday' Spanish from Argentina, Mexico, and Spain. In Proceedings of the Ninth Annual Language Resources and Evaluation Conference, Reykjavik, Iceland. European Language Resources Association (ELRA).</p> <p>In <strong>version .02</strong> of the tagged running format corpus in the /eagles directory has been added which includes the EAGLES tagset. This tagset is much more fleshed out than the simplified tagset in the /tagged directory. For information on the tagset refer here: <a href="http://nlp.lsi.upc.edu/freeling/doc/tagsets/tagset-es.html">http://nlp.lsi.upc.edu/freeling/doc/tagsets/tagset-es.html</a>.</p>
Three-dimensional subnanoscale imaging of unit cell doubling due to octahedral tilting and cation modulation in strained perovskite thin films
<p>Transmission electron microscopy data used in the journal publication <a href="https://doi.org/10.1103/PhysRevMaterials.3.063605">"Three-dimensional subnanoscale imaging of unit cell doubling due to octahedraltilting and cation modulation in strained perovskite thin films"</a></p> <p><strong>Data files</strong></p> <p>There are two data types:</p> <ul> <li>Scanning TEM (STEM) diffraction patterns acquired with a Medipix3 detector (Merlin): m004_LSMO_LFO_STO_medipix.hdf5 <ul> <li>Acquired on a probe corrected Jeol ARM200CF</li> <li>Acceleration voltage: 200 kV</li> <li>Convergence semi-angle: 20.4 mrad (calibrated using the SrTiO<sub>3</sub> substrate HOLZ ring)</li> <li>Detector calibration: 1.357 mrad per pixel (calibrated using the SrTiO<sub>3</sub> substrate HOLZ ring)</li> </ul> </li> <li>Atomic resolution STEM data, both annular dark field (ADF) and annular bright field (ABF), which were acquired simultaneously: s007_ADF.hdf5, s007_ABF.hdf5</li> </ul> <p>The data can be loaded in python using h5py.</p> <p>For the Medipix3 data:</p> <pre><code class="language-python">import h5py f = h5py.File('m004_LSMO_LFO_STO_medipix.hdf5', mode='r') data = f['fpd_expt/fpd_data/data'] data_subset = data[0:16, 0:16, :, :]</code></pre> <p>For the STEM-ADF or STEM-ABF data:</p> <pre><code class="language-python">import h5py f = h5py.File('s007_ADF.hdf5', mode='r') data = f['Experiments/__unnamed__/data']</code></pre> <p>Exploring the Medipix3 dataset lazily, i.e. without loading the whole dataset into memory at the same time. Using pixStem:</p> <pre><code class="language-python">import pixstem.api as ps s = ps.load_ps_signal("003_stripe1.hdf5", lazy=True) s.plot()</code></pre> <p>Loading the STEM-ADF or STEM-ABF data using HyperSpy, which automatically loads the probe scaling:</p> <pre><code class="language-python">import hyperspy.api as hs s = hs.load("s007_ADF.hdf5") s.plot()</code></pre> <p><br> <strong>Processing files</strong></p> <p>All the TEM data has been processed using python scripts, which is named based on the type of processing:</p> <ul> <li>d00N_...: Medipix3 data processing</li> <li>a00N_...: Atomic resolution STEM-ADF and STEM-ABF processing using Atomap</li> </ul> <p>The scripts generate intermediate files, which are saved in folders with the same prefix as the scripts. So the d001_... script makes a folder named d001_... . These intermediate files are included here as zip-files, since Zenodo doesn't support folder structures.</p> <p>The python libraries required to run the scripts are listed in requirements.txt. Newer versions of the libraries will most likely also work.</p> <p>To setup the python environment with the required libraries, and run all the scripts:</p> <pre><code class="language-bash">pip3 install -r requirements.txt python3 run_all_scripts.py</code></pre> <p> </p>
Dataset related to publication: Nanoporous Film Layers to Enhance the Performance of Passive Radiative Cooling Paint Mixtures
<p>Dataset related to the publication:</p> <p><em>Giuseppe Emanuele Lio,</em><em> Sara Levorin</em><em>, Atakan Erdoğan</em><em>, Jérémy Werlé</em><em>, Alain J. Corso</em><em>, Luca </em><em>Schenato</em><em>, Diederik S. Wiersma</em><em>, Marco Santagiustina</em><em>, Lorenzo Pattelli</em><em>, Maria Guglielmina Pelizzo. </em><em>Nanoporous Film Layers to Enhance the Performance of Passive Radiative Cooling Paint Mixtures, accepted and in publishing on International Journal of Thermophysics. </em>DOI: 10.1007/s10765-024-03439-8</p> <p>The repository contains the temperature, relative humidity, irradiance, sky temperature and net cooling power measurements in the file named "DATA_IJT_Nanoporous_Film_ Layer.txt" and the python script (Data_analizer.py) to analyze them as done in the paper.</p> <p> </p> <p>The repository also contain the Matlab workspace (IJT_data.mat)and code (Data_R_ATR_analyzer)to evaluate all the sample reflectance and FTIR measurements. Moreover, the extra measurements performed on the Celgard thin film are available in the workspace "Celgard_UV_VIS_NIR_T.mat" and "R_Celgard.mat". Moreover in Version 2 all the used data for Reflectance and Transmittance are also available in "txt" format for a simple and fast usage. </p> <p> </p> <h2>Notes (English)</h2> <div> <table> <tbody> <tr> <td> <p>This work was partly supported by</p> <ul> <li>European Project 21GRD03 PaRaMetriC, which received funding from the European Partnership on Metrology, co-financed by the European Union's Horizon Europe Research and Innovation Programme, and from the Participating States.</li> <li>European Union - PON Research and Innovation 2014-2020 in accordance with Article 24, paragraph 3a), of Law No. 240 of December 30, 2010, as amended, and Ministerial Decree No. 1062 of August 10, 2021</li> </ul> </td> </tr> </tbody> </table> </div>
Vacuum-Sublimed Cocrystalline Thin Films of Naphthalene Bisimide and Pt(II) Complex for Phosphorescent Light-Emitting Diodes
<p>Additional data to report <a href="https://doi.org/10.1002/adom.202402117">https://doi.org/10.1002/adom.202402117</a>:</p> <p>Cocrystals employing small organic molecules with platinum(II)-complexes are known to exhibit organic room-temperature phosphorescence (RTP). However, this desirable property was so far only demonstrated in the macroscopic 1:1 cocrystalline state, which limitsdevice applications outside of small single-crystal devices. Here, we show that vacuum cosublimed thin films of both components in various mixing ratios form layers with selfassembled small cocrystalline domains which exhibit RTP. This Pt(II) doping improved the photoluminescence (PL) quantum yield (ΦPL) of the now phosphorescence emitting 1,8:4,5-naphthalene bisimide (NBI) from below 0.1% to 9% in respective thin films. These doped layers were employed as active layers in light-emitting diodes to emit red electroluminescence (EL). Via time-resolved measurements the lifetime of the device EL was determined in accordance with the PL to be around 50 µs. Maximum external quantum efficiencies (EQEs) of over 0.2% with RTP emission signatures consistent with the PL of solution-grown cocrystals could be achieved.</p>
Simulated thickness profiles of ALD film in a wide microchannel of 500 nm height published as Fig.4 in PCCP 24 (2022) 8645-8660
<p>A series of simulated thickness profiles of atomic layer deposition (ALD) film grown in a wide lateral high-aspect-ratio (LHAR) microchannel is archived as an Excel file. This dataset has been published as Figure 4 in the publication "Conformality of atomic layer deposition in microchannels: impact of process parameters on the simulated thickness profile" (Yim and Verkama et al., Phys. Chem. Chem. Phys. 24 (2022) 8645-8660. https://doi.org/10.1039/D1CP04758B). A diffusion-reaction model by Ylilammi et al. (Ylilammi et al., J. Appl. Phys. 123 (2018) 205301. https://doi.org/10.1063/1.5028178) was re-implemented for the simulation. For this simulation, a channel height of 500 nm, which is a typical height for microscopic PillarHallTM LHAR test chips (Yim and Ylivaara et al., Phys. Chem. Chem. Phys., 22 (2020) 23107-23120. https://doi.org/10.1039/D0CP03358H), was used.<br> The Excel file consists of 11 tabs in total: metadata, baseline thickness profile, and Fig4a to Fig4i. The baseline thickness profile and data of fig4a Fig4i are also available as a CSV file. The metadata page describes the data with its baseline conditions. The baseline conditions used in the simulation are: sticking coefficient = 0.01, temperature = 250 °C, initial partial pressure of Reactant A = 100 Pa, molar mass of Reactant A = 0.100 kg mol-1, hard-sphere diameter of Reactant A = 6 × 10-10 m, partial pressure of inert gas I = 500 Pa, molar mass of inert gas I = 0.028 kg mol-1, hard-sphere diameter of inert gas I = 3.74 × 10-10 m, mass density of deposited film = 3500 kg m-3, areal number density of metal M atoms in MyZx material = 4 nm-2, number of metal atoms in a Reactant A molecule = 1, number of metal atoms in a formula unit of growing film = 1, number of cycle = 250, desorption probability = 0.01 s-1, channel height = 500 nm, and channel width = 10 mm. The baseline thickness profile tab contains a thickness profile obtained in the baseline conditions as film thickness versus distance within a microchannel. The thickness profile stored from Fig4a to Fig4i tabs was obtained by varying individual parameters with other parameter values in baseline conditions: initial partial pressure of Reactant A (Fig4a), pulse time (Fig4b), molar mass of Reactant A (Fig4c), mass density of deposited film (Fig4d), adsorption density (Fig4e), desorption probability (Fig4f), sticking coefficient (Fig4g), temperature (Fig4h) and partial pressure of inert gas (Fig4i).</p>
Spray impact onto a hot solid substrate: film boiling suppression by lubricant addition. Supplementary Data
<p>This is a Supplementary Data for a paper entitled "Spray impact onto a hot solid substrate: film boiling suppression by lubricant addition" by Gajevic Joksimovic et al. <em>Frontiers in Physics </em>(2023).</p> <p><strong>Data used for plotting of figures</strong></p> <p><em>Datasets for Fig. 5: </em></p> <p>Number at the end of the file name corresponds to the volumetric lubricant concentration used.</p> <ul> <li> Dataset_Figure5 TimeTemperatatureFlux Concentration 0.97.txt</li> <li> Dataset_Figure5 TimeTemperatatureFlux Concentration 1.09.txt</li> <li> Dataset_Figure5 TimeTemperatatureFlux Concentration 1.26.txt </li> <li> Dataset_Figure5 TimeTemperatatureFlux Concentration 1.49.txt</li> <li> Dataset_Figure5 TimeTemperatatureFlux Concentration 1.82.txt</li> <li> Dataset_Figure5 TimeTemperatatureFlux Concentration 2.34.txt</li> <li> Dataset_Figure5 TimeTemperatatureFlux Concentration 3.28.txt</li> <li> Dataset_Figure5 TimeTemperatatureFlux Concentration 5.47.txt</li> <li> Dataset_Figure5 TimeTemperatatureFlux PureWater.txt</li> </ul> <p><em>Datasets for Fig. 11:</em></p> <ul> <li>Dataset_Figure11 PHI Omega Omega nu.txt</li> </ul> <p><strong>Supplementary videos</strong></p> <p><em>The supplementary videos for Fig. 8</em>:</p> <ul> <li>supplementary video Figure8a.mp4</li> <li>supplementary video Figure9b.mp4</li> <li>supplementary video Figure9c.mp4</li> </ul> <p><em>The supplementary video for Fig. 13</em></p> <ul> <li>supplementary video Figure13.mp4</li> </ul>
data for journal article 'Nernst-Ettingshausen effect in thin Pt and W films at low temperatures'
<p>This dataset contains the supporting information for the journal article 'Nernst-Ettingshausen effect in thin Pt and W films at low temperatures'.</p>
Film Circulation dataset
<p>Complete dataset of “Film Circulation on the International Film Festival Network and the Impact on Global Film Culture”</p> <p> </p> <p>A peer-reviewed data paper for this dataset is in review to be published in NECSUS_European Journal of Media Studies - an open access journal aiming at enhancing data transparency and reusability, and will be available from https://necsus-ejms.org/ and https://mediarep.org</p> <p>Please cite this when using the dataset.</p> <p><br> Detailed description of the dataset:</p> <p><strong>1 Film Dataset: Festival Programs</strong></p> <p>The Film Dataset consists a data scheme image file, a codebook and two dataset tables in csv format. </p> <p>The codebook (csv file “1_codebook_film-dataset_festival-program”) offers a detailed description of all variables within the Film Dataset. Along with the definition of variables it lists explanations for the units of measurement, data sources, coding and information on missing data. </p> <p>The csv file “1_film-dataset_festival-program_long” comprises a dataset of all films and the festivals, festival sections, and the year of the festival edition that they were sampled from. The dataset is structured in the long format, i.e. the same film can appear in several rows when it appeared in more than one sample festival. However, films are identifiable via their unique ID. </p> <p>The csv file “1_film-dataset_festival-program_wide” consists of the dataset listing only unique films (n=9,348). The dataset is in the wide format, i.e. each row corresponds to a unique film, identifiable via its unique ID. For easy analysis, and since the overlap is only six percent, in this dataset the variable sample festival (fest) corresponds to the first sample festival where the film appeared. For instance, if a film was first shown at Berlinale (in February) and then at Frameline (in June of the same year), the sample festival will list “Berlinale”. This file includes information on unique and IMDb IDs, the film title, production year, length, categorization in length, production countries, regional attribution, director names, genre attribution, the festival, festival section and festival edition the film was sampled from, and information whether there is festival run information available through the IMDb data. </p> <p><br> <strong>2 Survey Dataset</strong></p> <p>The Survey Dataset consists of a data scheme image file, a codebook and two dataset tables in csv format.</p> <p>The codebook “2_codebook_survey-dataset” includes coding information for both survey datasets. It lists the definition of the variables or survey questions (corresponding to Samoilova/Loist 2019), units of measurement, data source, variable type, range and coding, and information on missing data. </p> <p>The csv file “2_survey-dataset_long-festivals_shared-consent” consists of a subset (n=161) of the original survey dataset (n=454), where respondents provided festival run data for films (n=206) and gave consent to share their data for research purposes. This dataset consists of the festival data in a long format, so that each row corresponds to the festival appearance of a film.</p> <p>The csv file “2_survey-dataset_wide-no-festivals_shared-consent” consists of a subset (n=372) of the original dataset (n=454) of survey responses corresponding to sample films. It includes data only for those films for which respondents provided consent to share their data for research purposes. This dataset is shown in wide format of the survey data, i.e. information for each response corresponding to a film is listed in one row. This includes data on film IDs, film title, survey questions regarding completeness and availability of provided information, information on number of festival screenings, screening fees, budgets, marketing costs, market screenings, and distribution. As the file name suggests, no data on festival screenings is included in the wide format dataset. </p> <p><br> <strong>3 IMDb & Scripts</strong></p> <p>The IMDb dataset consists of a data scheme image file, one codebook and eight datasets, all in csv format. It also includes the R scripts that we used for scraping and matching.</p> <p>The codebook “3_codebook_imdb-dataset” includes information for all IMDb datasets. This includes ID information and their data source, coding and value ranges, and information on missing data.</p> <p>The csv file “3_imdb-dataset_aka-titles_long” contains film title data in different languages scraped from IMDb in a long format, i.e. each row corresponds to a title in a given language.</p> <p>The csv file “3_imdb-dataset_awards_long” contains film award data in a long format, i.e. each row corresponds to an award of a given film.</p> <p>The csv file “3_imdb-dataset_companies_long” contains data on production and distribution companies of films. The dataset is in a long format, so that each row corresponds to a particular company of a particular film.</p> <p>The csv file “3_imdb-dataset_crew_long” contains data on names and roles of crew members in a long format, i.e. each row corresponds to each crew member. The file also contains binary gender assigned to directors based on their first names using the GenderizeR application.</p> <p>The csv file “3_imdb-dataset_festival-runs_long” contains festival run data scraped from IMDb in a long format, i.e. each row corresponds to the festival appearance of a given film. The dataset does not include each film screening, but the first screening of a film at a festival within a given year. The data includes festival runs up to 2019.</p> <p>The csv file “3_imdb-dataset_general-info_wide” contains general information about films such as genre as defined by IMDb, languages in which a film was shown, ratings, and budget. The dataset is in wide format, so that each row corresponds to a unique film.</p> <p>The csv file “3_imdb-dataset_release-info_long” contains data about non-festival release (e.g., theatrical, digital, tv, dvd/blueray). The dataset is in a long format, so that each row corresponds to a particular release of a particular film.</p> <p>The csv file “3_imdb-dataset_websites_long” contains data on available websites (official websites, miscellaneous, photos, video clips). The dataset is in a long format, so that each row corresponds to a website of a particular film.</p> <p>The dataset includes 8 text files containing the script for webscraping. They were written using the R-3.6.3 version for Windows.</p> <p>The R script “r_1_unite_data” demonstrates the structure of the dataset, that we use in the following steps to identify, scrape, and match the film data.</p> <p>The R script “r_2_scrape_matches” reads in the dataset with the film characteristics described in the “r_1_unite_data” and uses various R packages to create a search URL for each film from the core dataset on the IMDb website. The script attempts to match each film from the core dataset to IMDb records by first conducting an advanced search based on the movie title and year, and then potentially using an alternative title and a basic search if no matches are found in the advanced search. The script scrapes the title, release year, directors, running time, genre, and IMDb film URL from the first page of the suggested records from the IMDb website. The script then defines a loop that matches (including matching scores) each film in the core dataset with suggested films on the IMDb search page. Matching was done using data on directors, production year (+/- one year), and title, a fuzzy matching approach with two methods: “cosine” and “osa.” where the cosine similarity is used to match titles with a high degree of similarity, and the OSA algorithm is used to match titles that may have typos or minor variations.</p> <p>The script “r_3_matching” creates a dataset with the matches for a manual check. Each pair of films (original film from the core dataset and the suggested match from the IMDb website was categorized in the following five categories: a) 100% match: perfect match on title, year, and director; b) likely good match; c) maybe match; d) unlikely match; and e) no match). The script also checks for possible doubles in the dataset and identifies them for a manual check.</p> <p>The script “r_4_scraping_functions” creates a function for scraping the data from the identified matches (based on the scripts described above and manually checked). These functions are used for scraping the data in the next script.</p> <p>The script “r_5a_extracting_info_sample” uses the function defined in the “r_4_scraping_functions”, in order to scrape the IMDb data for the identified matches. This script does that for the first 100 films, to check, if everything works. Scraping for the entire dataset took a few hours. Therefore, a test with a subsample of 100 films is advisable.</p> <p>The script “r_5b_extracting_info_all” extracts the data for the entire dataset of the identified matches.</p> <p>The script “r_5c_extracting_info_skipped” checks the films with missing data (where data was not scraped) and tried to extract data one more time to make sure that the errors were not caused by disruptions in the internet connection or other technical issues.</p> <p>The script “r_check_logs” is used for troubleshooting and tracking the progress of all of the R scripts used. It gives information on the amount of missing values and errors.</p> <p><br> <strong>4 Festival Library Dataset</strong></p> <p>The Festival Library Dataset consists of a data scheme image file, one codebook and one dataset, all in csv format.</p> <p>The codebook (csv file “4_codebook_festival-library_dataset”) offers a detailed description of all variables within the Library Dataset. It lists the definition of variables, such as location and festival name, and festival categories, units of measurement, data sources and coding and missing data. </p> <p>The csv file “4_festival-library_dataset_imdb-and-survey” contains data on all unique festivals collected from both IMDb and survey sources. This dataset appears in wide format, all information for each festival is listed in one row. This includes data on festival ID, sources, festival name and alternatives, location data for country, region and city, data on award recognition, founding year, whether a festival has a specialization and if so the ascribed festival categories. </p> <p> </p>
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
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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