Find research datasets worth reusing

opencc-by-4.0Dec 2022View details →

Dataset for the Underestimation of the Number of Didden Objects

Data analysis of 5 experiments in the study of "Underestimation of the Number of Hidden Objects".

Data for GECCO2023 Paper "Many-objective (Combinatorial) Optimization is Easy"

Data for Paper "Many-objective (Combinatorial) Optimization is Easy" <ul> <li>instances.tar.xz contains 𝜌mnk-landscape instances</li> <li>metrics.csv contains the metric-values based on full enumeration</li> <li>performance.csv contains the Pareto resolution and the hypervolume of the different algorithms one each instance</li> <li>performance_neval.csv contains the number of evaluations performed by PLS</li> <li>script.R is the R script for producing the figures</li> </ul> Reference Arnaud Liefooghe and Manuel López-Ibáñez. 2023. Many-objective (Combinatorial) Optimization is Easy. In Genetic and Evolutionary Computation Conference (GECCO '23), July 15–19, 2023, Lisbon, Portugal. <a href="https://doi.org/10.1145/3583131.3590475">https://doi.org/10.1145/3583131.3590475</a> Abstract It is a common held assumption that problems with many objectives are harder to optimize than problems with two or three objectives. In this paper, we challenge this assumption and provide empirical evidence that increasing the number of objectives tends to reduce the difficulty of the landscape being optimized. Of course, increasing the number of objectives brings about other challenges, such as an increase in the computational effort of many operations, or the memory requirements for storing non-dominated solutions. More precisely, we consider a broad range of multi- and many-objective combinatorial benchmark problems, and we measure how the number of objectives impacts the dominance relation among solutions, the connectedness of the Pareto set, and the landscape multimodality in terms of local optimal solutions and sets. Our analysis shows the limit behavior of various landscape features when adding more objectives to a problem. Our conclusions do not contradict previous observations about the inability of Pareto-optimality to drive search, but we explain these observations from a different perspective. Our findings have important implications for the design and analysis of many-objective optimization algorithms.

opencc-by-4.0Feb 2023View details →

Dataset and stimuli: Perception of saturation in natural objects

This dataset contains observer data and stimulus information for the below publication. Refer to this manuscript for more details. Laysa Hedjar, Matteo Toscani, and Karl R. Gegenfurtner, "Perception of saturation in natural objects," Journal of the Optical Society of American A 40(3), A190-A198 (2023), doi:10.1364/JOSAA.476874.   Participant data is available in two files: fruit_pt_data.csv and blob_pt_data.csv <ul> <li>fruit_pt_data.csv:  <ul> <li>fruit name: name of fruit pair</li> <li>object or swatch: whether the stimulus pair were whole objects or 8x8 swatches</li> <li>participant ID: given participant identification number</li> <li>proportion positive: proportion of trials in which participant chose the positive stimulus as more saturated (out of 10 total trials per stimulus pair)</li> </ul> </li> <li>blob_pt_data.csv: <ul> <li>blob hue (radians - LAB): hue in radians of the blob pair, as defined in LAB-LCH color space</li> <li>object or swatch: whether the stimulus pair were whole objects or 8x8 swatches</li> <li>matched or unmatched: whether the stimulus pair were matched in terms of blob ID (refers to spatial configuration)</li> <li>positive stimulus ID: identification number of the positive LC-slope stimulus (refers to spatial configuration)</li> <li>negative stimulus ID: identification number of the negative LC-slope stimulus (refers to spatial configuration) <ul> <li>note that the above two IDs should be identical if the stimulus is a 'matched' pair</li> </ul> </li> <li>participant ID: given participant identification number</li> <li>proportion positive: proportion of trials in which participant chose the positive stimulus as more saturated (out of 5 total trials per stimulus pair)</li> </ul> </li> </ul>   Stimuli pngs are in the zip file stimuli.zip. Pngs are not gamma-corrected. Blob and fruit stimulus sets are separated by folder; object and swatch stimulus sets are also separated by folder. Fruit pngs are labeled:           fruit_[object/swatch]_[fruitName]-[negative/positive].png For blob pngs, six possible spatial configurations for each hue were used. An ID was given for each configuration. Blob pngs are labeled:           blob_[object/swatch]_hue[hueInRadians]_ID[1-6]-[negative/positive].png   Statistics of the stimulus images are presented in the files fruit_stats_objects.csv, fruit_stats_swatches.csv, blob_stats_objects.csv, and blob_stats_swatches.csv. Each column represents a different stimulus image. Each row represents a different statistic taken across the distribution of pixels. Calculations were made in CIELAB-LCH color space ('white point' defined as white of monitor: CIE1931 xyY 0.3328, 0.3343, 142.35).

opencc-by-4.0Feb 2023View details →

Time Series Data of Gaze, Head Pose, Hand Pose, and Object Positions for Object Approaches with a Given Intention

This data set comprises time series data of gaze, head pose, hand pose, and object positions for object approaches with a given intention. The data was captured in the context of the following publication: <ul> <li>Michael Fennel, Serge Garbay, Antonio Zea, Uwe D. Hanebeck, Intention Estimation with Recurrent Neural Networks for Mixed Reality Environments, Proceedings of the 26th International Conference on Information Fusion (Fusion 2023) (under review)</li> </ul> A Microsoft Hololens 2 was used for recording the data at 60 fps under the modalities explained in detail in the above-mentioned paper. The file names are structured as follows: <ul> <li>1st/2nd: <ul> <li>The data with "1st" contains approaches to randomly placed objects on a grid, which are rendered in augmented reality. The user is informed about the object to approach using a visual cue. This corresponds to Section IV-A.</li> <li>The data with "2nd" contains approaches to real objects placed statically in a room. The user is informed about the object to approach using a voice command.</li> </ul> </li> <li>unfiltered: Contains all approaches, including those where the user disrespects the given commands. Filtering is done as described in the paper.</li> <li>train/val/test: The first dataset was split in a 70/20/10 ratio for training, validation, and test.</li> </ul> Each data set contains the following columns. In each approach, 5 objects numbered from i=0 to i=4 are present. <ul> <li>General: <ul> <li>time: in seconds</li> <li>subject: consecutive subject number</li> <li>handedness: left (1), right (0)</li> <li>trial: consecutive trial number per subject</li> <li>target_label: index of the object to approach (0 to 4)</li> </ul> </li> <li>Data in world coordinates: <ul> <li>head_{x,y,z}: head position</li> <li>head_quat_{w,x,y,z}: head orientation quaternion</li> <li>W_gaze_{x,y,z}: gaze direction</li> <li>W_r_hand_{x,y,z}: right hand position</li> <li>W_r_hand_quat_{w,x,y,z}: right hand orientation quaternion</li> <li>W_l_hand_{x,y,z}: left hand position</li> <li>W_l_hand_quat_{w,x,y,z}: left hand orientation quaternion</li> <li>W_object_i_{x,y,z}: position of object i</li> <li>W_object_i_quat {w,x,y,z}: orientation quaternion of object i</li> </ul> </li> <li>Data in egocentric coordinates (head coordinate system). This data is provided for convenience and can be derived from the other data: <ul> <li>gaze_{x,y,z}: gaze direction</li> <li>r_hand_{x,y,z}: right hand position</li> <li>r_hand_quat_{w,x,y,z}: right hand orientation quaternion</li> <li>l_hand_{x,y,z}: left hand position</li> <li>l_hand_quat_{w,x,y,z}: left hand orientation quaternion</li> <li>object_i_{x,y,z}: position of object i</li> <li>object_i_quat {w,x,y,z}: orientation quaternion of object i</li> </ul> </li> </ul> Acknowledgment: This work was supported by the <a href="https://robdekon.de/">ROBDEKON</a> project of the German Federal Ministry of Education and Research.

opencc-zeroMar 2023View details →

Materials Science Optimization Benchmark Dataset for Multi-Objective, Multi-Fidelity Optimization of Hard-Sphere Packing Simulations

Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a “Turing test” of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In the fields of materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 494498 random hard-sphere packing simulations representing 206 CPU days worth of computational overhead were performed across nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters and results were logged to a free-tier shared MongoDB Atlas database. Two core tabular datasets resulted from this study: 1. a failure probability dataset containing unique input parameter sets and the estimated probabilities that the simulation will fail at each of the two steps, and 2. a regression dataset mapping input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets are used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This is in contrast with a more traditional approach that imposes a-priori assumptions such as Gaussian noise e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios. For usage instructions, see https://matsci-opt-benchmarks.readthedocs.io/.

Dataset of "Early triadic interactions in the first year of life: A systematic review on object-mediated shared encounters"

<pre>Two files are available as a result of data extraction from the 51 studies included in the systematic review entitled: "Early triadic interactions in the first year of life: A systematic review on object-mediated shared encounters": (1) DataExtraction.csv -> Dataset resulting from data extraction. (2) DictionaryVariables.csv -> dictionary of the variables included in the dataset: Authors, doi, Year of publication, Study type, Design type, Data analysis strategy, Type of task, Objects, Infants' age (months), Context of interaction, and Country. </pre>

zebrafish GSE223922 scRNA data set objects

scRNA data from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE223922 (Sur et al. 2023), see a detailed description of the study here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055256/ Data were downloaded from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE223922 to create a R Seurat object and converted into AnnData (h5ad) file to be able to analyse with e.g. python scanpy package. If you use this data, please cite Sur et al. 2023.

Dataset for the article "Computations and Measurements of the Magnetic Polarizability Tensor Characterisation of Highly Conducting and Magnetic Objects"

Datasets to accompany the article "Computations and Measurements of the Magnetic Polarizability Tensor Characterisation of Highly Conducting and Magnetic Objects". Written by J. Elgy, P. D. Ledger, J. L. Davidson, T. Özdeğer and A. J. Peyton. The article has been submitted to "Engineering Computations" (2023). The datasets include data files, meshes, and code for recreating the results from the paper. This requires the open source MPT-Calculator software available at <a href="http://github.com/MPT-Calculator/MPT-Calculator">https://github.com/MPT-Calculator/MPT-Calculator</a> (InitialRelease branch). The datasets also include measurement data for real world objects courtesy of The University of Manchester. J. Elgy and P. D. Ledger gratefully acknowledge the financial support received from EPSRC in the form of grant EP/V009028/1. J. L. Davidson and A. J. Peyton are grateful for the financial support received from an Innovate UK Grant (reference number 39814). T. Özdeğer and A. J. Peyton are grateful for the financial support received from EPSRC, U.K. through the research grant EP/R002177/1.

Difference and number of works published over the years grouped by the objective of the generative process

Difference and number of works published over the years grouped by the objective of the generative process. Part of the study "What do we mean by GenAI?"

Number of works grouped by objective, domain and AI technique employed

Number of works grouped by objective, domain and AI technique employed. Part of the study "What do we mean by GenAI?"

opencc-by-4.0Aug 2023View details →

EnergyPROSPECTS Energy Citizenship Factsheet Series, Part 2: Motivations and objectives

This document is Part 2 of the EnergyPROSPECTS Factsheet Series. We have created the Series to publish the results of a mapping of energy citizenship in Europe, along with the first stage of our analysis of the respective data. The EnergyPROSPECTS consortium mapped 596 cases of energy citizenship between November 2020 and May 2021 using desk research, collecting data on many aspects of the cases. Although the analysis is a work in progress, we believe it is important to share our data and, through doing this, contribute to the understanding of energy citizenship in Europe. EnergyPROSPECTS (PROactive Strategies and Policies for Energy Citizenship Transformation), a H2020 project between 2021-2024, works with a critical understanding of energy citizenship that is grounded in state-of-the-art social sciences and humanities (SSH) insights.

opencc-by-4.0Oct 2023View details →

The Object Detection for Olfactory References (ODOR) Dataset.

Real-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes. Existing datasets provide instance-level annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes. The proposed ODOR dataset fills this gap, offering 38,116 object-level annotations across 4,712 images, spanning an extensive set of 139 fine-grained categories. Conducting a statistical analysis, we showcase challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas. Furthermore, we provide an extensive baseline analysis for object detection models and highlight the challenging properties of the dataset through a set of secondary studies. Inspiring further research on artwork object detection and broader visual cultural heritage studies, the dataset challenges researchers to explore the intersection of object recognition and smell perception. How to use The annotations are provided in COCO JSON format. To represent the two-level hierarchy of the object classes, we make use of the supercategory field in the categories array as defined by COCO. In addition to the object-level annotations, we provide an additional CSV file with image-level metadata, which includes content-related fields, such as Iconclass codes [72 , 73]) or image descriptions, as well as formal annotations, such as artist, license, or creation year. For the sake of license compliance, we do not publish the images directly (although most of the images are public domain). Instead, we provide links to their source  collections in the metadata file (meta.csv) and a python script to download the artwork images (download_images.py).

opencc-by-4.0Oct 2023View details →

Procure-To-Payment (P2P) Object-centric Event Log in OCEL 2.0 Standard

Short Description This process describes the Procure-To-Pay (P2P) procedure within an organization, starting from the initiation of a purchase requirement up to the execution of payment. This simulation extensively uses genuine SAP transactions and object types to offer a realistic representation of the P2P process. Overview Within our simulated organization: <ul> <li> Procurement Initiatives: The procurement journey begins when a department or individual recognizes a need and creates a Purchase Requisition using transaction ME51N. </li> <li> Approval Process: Before the purchase can proceed, the requisition must be approved. This is carried out using transaction ME54N. Given the nature of our simulation, there may be instances where the approval process takes an unusually long time, exemplifying the Lengthy Approval Process behavior. </li> <li> Vendor Interactions: <ul> <li>Upon approval, a Request for Quotation is sent out to potential vendors using transaction ME41.</li> <li>Vendors then submit their quotations, which are maintained in the system using transaction ME47.</li> </ul> </li> <li> Purchase Order Creation: Once a vendor's quotation is selected, a Purchase Order is created using transaction ME21N. The purchase order is then subjected to an internal approval process (ME29N). Occasionally, maverick buying—where purchases are made without proper authorization—can be observed. </li> <li> Goods & Invoice Management: <ul> <li>When the goods are received, a Goods Receipt is recorded using transaction MIGO.</li> <li>Invoices from vendors are then received and recorded. A three-way match, which checks the purchase order, goods receipt, and invoice for discrepancies, is performed using transaction MRBR.</li> </ul> </li> <li> Payment: Once everything is verified, payments are executed using transaction F110. However, there may be instances of Duplicate Payments in our simulation, where the system mistakenly pays the same invoice more than once. </li> </ul> Special Behaviors: <ul> <li>Maverick Buying: Unauthorized purchases, bypassing the standard procedure.</li> <li>Duplicate Payments: An error leading to the same invoice being paid multiple times.</li> <li>Lengthy Approval Process: Delays in approving purchase requisitions or purchase orders, which might lead to operational inefficiencies.</li> </ul> General Properties An overview of log properties is given below. <table> <thead> <tr> <th>Property</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Event Types</td> <td>10</td> </tr> <tr> <td>Object Types</td> <td>7</td> </tr> <tr> <td>Events</td> <td>14671</td> </tr> <tr> <td>Objects</td> <td>9543</td> </tr> </tbody> </table> Authors Gyunam Park and Leah Tacke genannt Unterberg Contributing To contribute, drop us an email! We are happy to receive your feedback.

opencc-by-4.0Apr 2022View details →

Carbonate spectra and Melilite overtone spectra from "Sakurai's Object revisited: new laboratory data for carbonates and melilites suggest the carrier of 6.9- μm excess absorption is a carbonate"

Carbonate and Melilite OVERTONE SPECTRA from  Bowey, J. E., Hofmeister, A. 2022 MNRAS, 513, 1774.  https://doi.org/10.1093/mnras/stac993"Sakurai's Object revisited: new laboratory data for carbonates and melilites suggest the carrier of 6.9- μ m excess absorption is a carbonate"Carbonate spectra in Fig 1 (carbonates.pdf) are in 2 column ascii files:  calcite_verythin.tau, calcite.tau, dolomite.tau, magnesite.tauTrue Meliliate overtone spectra in Fig 3 (goodmeliliteovertones.pdf are in 2 column ascii files:  Ak70_135micron_chip.tau,  Ak70_120micron_chip.tau, Ak70.tau, Ak13.tauThe column 1 increment varies between files.column 1 (x) is in increasing wavenumber (cm^-1)column 2 (tau) is absorbance in optical depth units per micron thickness of sample.Please see README.txt for more details.

opencc-by-4.0Jan 2020View details →

The Experimental Data for the Study "Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value"

The Experimental Data for the Study "Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value" 1. Introduction Frequency Fitness Assignment (FFA) replaces the objective value in the selection step of an optimization method with its encounter frequency in any selection step so far. It turns static problems into dynamic ones. Here we experimentally investigated this approach in two important contexts: First, we integrated it into a basic (1+1)-EA, obtaining the (1+1)-FEA. We applied both algorithms to several well-known benchmark problems with bit-string based search spaces, including the OneMax, LeadingOnes, TwoMax, Jump, Plateau, and W-Model functions. We also applied them to the Max-3-Sat instances from SATLib. We then also integrated FFA into a Memetic Algorithm for the Job Shop Problem. 2. Paper This data is used as the basis for the following article: Thomas Weise, Zhize Wu, Xinlu Li, and Yan Chen. Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value, originally submitted to <a href="https://arxiv.org/abs/2001.01416">arxiv</a> on 2020-01-06 (under the title Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function), updated with the new data in June 2020, and submitted to the IEEE Transactions on Evolutionary Computation. 3. Data This data set contains all the results of these experiments, the source codes used in the experiments (i.e., the algorithm implementations), as well as the scripts used for evaluating the results. 4. Version History This is the second version of the data set, including extended experiments and more evaluation results. Most importantly, data for larger scales of OneMax and LeadingOnes has been added. The original version is at <a href="http://dx.doi.org/10.5281/zenodo.3598172">10.5281/zenodo.3598172</a>. 5. Contact If you have any questions or suggestions, please contact <a href="http://iao.hfuu.edu.cn/team/director">Prof. Dr. Thomas Weise</a> of the <a href="http://iao.hfuu.edu.cn/">Institute of Applied Optimization</a> at <a href="http://www.hfuu.edu.cn">Hefei University</a> in Hefei, Anhui, China via email to <a href="mailto:tweise@hfuu.edu.cn">tweise@hfuu.edu.cn</a> with CC to <a href="mailto:tweise@ustc.edu.cn">tweise@ustc.edu.cn</a>.

opencc-by-4.0Oct 2019View details →

CURE-OR: Challenging Unreal and Real Environments for Object Recognition

As one of the research directions at <a href="https://ghassanalregib.info/">OLIVES Lab @ Georgia Tech</a>, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (1.M images) object recognition dataset (<a href="https://github.com/olivesgatech/CURE-OR">CURE-OR</a>) which is among the most comprehensive datasets with controlled synthetic challenging conditions. In <a href="https://github.com/olivesgatech/CURE-OR">CURE-OR</a> dataset, there are 1,000,000 images of 100 objects with varying size, color, and texture, captured with multiple devices in different setups. The majority of images in the dataset were acquired with smartphones and tested with off-the-shelf applications to benchmark the recognition performance of devices and applications that are used in our daily lives.  Please refer to our <a href="https://github.com/olivesgatech/CURE-OR">GitHub page</a> for code, papers, and more information. Some data specifications are provided below: Image Name Format :  "backgroundID_deviceID_objectOrientationID_objectID_challengeType_challengeLevel.jpg" Background ID:  1: White 2: Texture 1 - living room 3: Texture 2 - kitchen 4: 3D 1 - living room 5: 3D 2 – office Object Orientation ID:  1: Front (0 º) 2: Left side (90 º) 3: Back (180 º) 4: Right side (270 º) 5: Top Object ID:  1-100 Challenge Type:  No challenge 02: Resize 03: Underexposure 04: Overexposure 05: Gaussian blur 06: Contrast 07: Dirty lens 1 08: Dirty lens 2 09: Salt & pepper noise 10: Grayscale 11: Grayscale resize 12: Grayscale underexposure 13: Grayscale overexposure 14: Grayscale gaussian blur 15: Grayscale contrast 16: Grayscale dirty lens 1 17: Grayscale dirty lens 2 18: Grayscale salt & pepper noise Challenge Level:  A number between [0, 5], where 0 indicates no challenge, 1 the least severe and 5 the most severe challenge. Challenge type 1 (no challenge) and 10 (grayscale) has a level of 0 only. Challenge types 2 (resize) and 11 (grayscale resize) has 4 levels (1 through 4). All other challenges have levels 1 to 5.

opencc-by-4.0Jul 2020View details →

2m Perek telescope images of symmetrical object LAMOST J053944.81+531825.7

The pictures of the object LAMOST J053944.81+531825.7 showing the unusual symmetric triple star configuration and complex emission line profiles. Images were taken by newly commissioned photometric camera G2 Mark II 3200 of Ondrejov 2m Perek telescope in primary focus. Plate scale 0.2 arcsec /pixel . The whole field is 7x5 arcmin. The distance of both satellites from the central bright star is 6.7 arcsec. The zoomed composed image is 16x16arcsec in SDSS spectral filter g' (left) and r' (right). Exposure time 300 s. Observed on 15th April 2020, 19:13 and 19:19 UTC respectively. The same region was observed by PanSTARRS-1 in the years 2011 to 2014. See for reference <a href="https://ps1images.stsci.edu/cgi-bin/ps1cutouts?pos=05+39+44.81+%2B53+18+25.7">PS1 image web service</a>

opencc-by-4.0Jul 2018View details →

Fig. 3 in Stereophotography of malacological objects

Fig. 3: Stereo-photographs of living gastropods: (a) Radix balthica, (b) Planorbarius corneus, (c) Bithynia tentaculata.

opencc-by-4.0Nov 2020View details →

Reduced spectroscopy objects WDJ181417.84-735459.83 and VHS 472908521370

Files Spec_WD1814-2011-LKR_XSHOOTER.txt, WDex_SCI_SLIT_FLUX_MERGE1D_UVB.fits and WDex_SCI_SLIT_FLUX_MERGE1D_VIS.fits  are the .txt and .fits versions of the reduced X-SHOOTER spectrum of object WDJ181417.84-735459.83, observed under European Southern Observatory ESO programme 087.C$-$0639(B) in 2011, with PI Dr Avril Day-Jones. Files GaiaJ1814-7355_SCI_SLIT_FLUX_MERGE1D_UVB.fits, GaiaJ1814-7355_SCI_SLIT_FLUX_MERGE1D_VIS.fits,  GaiaJ1814-7355_SCI_SLIT_FLUX_MERGE1D_UVB-1.fits and GaiaJ1814-7355_SCI_SLIT_FLUX_MERGE1D_VIS.fits-1 are the reduced X-SHOOTER spectra of the same object (two exposures taken) observed under ESO programme 0103.C-0431(B) in 2019, with PI Dr Siyi Xu. Spec_WD1814-2019-LKR_XSHOOTER.txt is the mean flux from the two exposures. The raw data of both spectra was processed by Laura Rogers. File Spec182-189A_xtc_FIRE.fits is the reduced spectrum of the object VHS 472908521370, observed with the Folded-port InfraRed Echellette (FIRE) at the Magellan Baade telescope, Las Campanas observatory, on the night of June 02 2019. The raw data was processed by Juan Carlos Beamin.